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Closing Data’s Last-Mile Gap: Visualizing For Impact!



I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well.

A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights.

The last-mile gap is the distance between your trends and getting an influential company leader to take action.

Your biggest asset in closing that last-mile gap is the way you present the data.

On a slide. On a dashboard in Google Data Studio. Or simply something you plan to sketch on a whiteboard. This presentation of the data will decide if your trends and insights are understood, accepted and inferences drawn as to what action should be taken.

If your data presentation is good, you reduce the last-mile gap. If your data presentation is confusing/complex/wild, all the hard work that went into collecting the data, analyzing it, digging for context will all be for naught.

With the benefits so obvious, you might imagine that the last-mile gap is not a widely prevalent issue. I’m afraid that is not true. I see reports, dashboards, presentations with wide gaps. It breaks my heart, because I can truly appreciate all that hard work that went into creating work that resulted in no data-influence.

Hence today, one more look at this pernicious problem and a collection of principles you can apply to close the last-mile gap that exists at your work.

For our lessons today, I’m using an example that comes from analysis delivered by the collective efforts of a top American university, a top 5 global consulting company, and a major industry association. The analysis is publicly available.

I’ve chosen to block out the name of entities involved. Last-mile gaps exist at all our companies. It is not important where this 2018 analysis came from. In the tiny chance that you recognize the source, I request you to keep it out of your comments as well.

For each of the 17 examples we review, I’ll share an alternative version I created. I invite you to play along and share your version of any of the examples. I’ll add them to the post, and credit you.


Let’s go!

I persistently advocate for simplicity in slides. Don’t create handouts!

In this case the goal was to create handouts, perhaps to make it easier for audiences to consume the data by themselves. I would humbly still advocate for simplicity when it comes to data presentation.


Some of the fixes to solve for simplicity could be to use fewer sprinkles, a simpler header – graphics and text –, and we can be very selective about what’s on he slide. As you look at the slide, I’m sure you’ll come up with other ways in which we can liberate the white space for the tyranny of text/colors.

Solving for simplicity contributes to communication effectiveness. It of course reflects on your brand, and, most of all, helps you have better control over the story you are trying to tell.

For the rest of this post I’ll ignore the simplicity and storytelling elements and focus exclusively on the data itself. How, what, why and instead of.

Look at the graph above, and the little table… Ponder for a moment what you would do to close the last-mile gap and help the essential message shine through.

Here are some things that stood out for me:

1. Graphing choices can exaggerate or undersell reality.

One way to exaggerate is to start your y-axis at 40, as it the case above. The resulting line exaggerates the trend and ends up implying something that might not quite be there.

Start at zero. Please.

2. False precision can cause clutter, and undercut the Analyst’s brilliance.

This is very subtle.

You’ll notice that the numbers on the graph are expressed with one decimal point. As in 47.7, 56.5, etc. If you pause and consider how this data is collected, via a small triple digit sample self-reported survey results, you’ll quickly realize that the error range in this data is likely a few points. If that’s true, showing the .6, .5 is implying a precision that simply does not exist.

Besides, this false precision also clutters the graph.

3. Remove the distractions, ruthlessly.

In an 11-year span, each data point is a lot less important than the trend. Do you need the dots on the graph? Do you even need the numbers for the individual months?

When it comes to closing the last-mile gap it is helpful to have a ruthless streak. It is helpful because in service of our ultimate objective, you’ll have to kill some of your favorite things, you’ll have push back against your boss/peers who might love clutter, and you might have to help change an entire culture. Hard, painful, work. But, immensely worth it.

Here’s an alternative way to present the data, using nothing more than the standard settings in good old Excel:


It shows the trend, simply. You can see it is up broadly over eleven years. That it was under 50 and is now close to 70.

Did you notice the trend is not as exaggerated as the original? And, still effective!

You might use a different font, perhaps have the graph be smaller, or maybe twist the month-year in the other direction. No problem. I’m confident if you apply the first three filters, whatever you create will close the last-mile gap better.

Here’s an example of doing exactly the opposite of principle #1. The y-axis is artificially set at 100%, as a result the trend is understated.


You don’t need to go this far.

Just let your favorite graphing tool auto-set the major and minor-axis, which will result in the graph looking like this…


Simple. No funny business. 

The trend stands by itself waiting your words as to why it is meaningful.

This next one is pretty interesting. My request to you is to not scroll beyond the slide. Pause. Absorb the graph. Try to understand what the author is really trying to say.

For bonus points, consider the perspective of the person reading this graph rather than the person who created it.

Read. Don’t scroll. Absorb.


How well did you understand the trend and the insight being communicated? What would you have done differently if you’d created the graph?

Here are some things that stood out for me:

4. Show as much data as is required, and no more.

The goal in the original seems to be to show top priorities for 12 months. If so, is the data for August 2017 really adding value?

Often we want to show all the data we have (after all we spent time collecting it!). In this case, it get’s in the way of understanding the 12 month shift.

5. Experiment with visualization options, even in Excel!

We have five dimensions of data, and two data points each (if you apply principle #4). We want the audience to be able to compare two data points for each dimension, and look across all five dimensions.

The bar chart is a sub-optimal way to let the audience see this. Consider experimenting with different visuals in Excel (or D3js).

I applied the radar chart to this data, and got this lovely end result…


It is ten million times easier to see the two data points for five dimensions, and realize that only two have changed.

Likewise, the overall trend also pops out at you so much easier in this case.

It would have taken ten minutes for us to explain the data and trend in the original. We can do that in five seconds now. You can use the time remaining discussing why this trend is material and what to do about it (if anything). Actually allowing data to play its natural role: Influence decisions.

This is a really nice example of a lesson that we tend to forget all the time (myself included).

You know the exercise by now. Pause, reflect on this slide, then scroll.


Here’s what stood out for me:

6. Don’t send a graphic to do a table’s job.

In this case, we are comparing two simple data points, on two dimensions (past, present). Why do we need a graph taking up all the space?

Why not just have a table that shows previous 12 months as 7.1% and a row under it with next 12 months as 8.9%?

Even better, why not just have one line of text:

Percent change in marketing budgets = +1.8 PP

Why have two fat bars?

Once you arrive at that conclusion, you’ll apply principle #4 and realize that the most interesting data on this slide is not the visual… Rather, it is the table on the top right corner of the slide.

Bada, bing, bada, boom, ten seconds later here’s your slide:


A simple table with a touch of colors that draws out the core message simply, directly and quickly.

The lighter shade for the core numbers will result in them being pushed a bit into the background. This simple choice guides the reader’s eyes gently to the delta (the most important bit).

I like playing with the borders a bit, as you see above. You might have other things you are picky about. And, that is ok. 🙂

To illustrate principle #6, here’s another slide where the graphic is completely unnecessary:


A tiny table with two data points will do just fine.

Here’s a bonus lesson for the analysis ninjas out there. Please don’t imply a linear trend between “current levels” and “next three years.” There is no indication that data from 2017 to 2020 is available, and it is highly unlikely that it will follow a linear trend. This is another example of breaking principle #1.

(Let’s not lose sight of the big picture: I am delighted that spending on analytics is going to increase that much! As our leaders spend this largesse, I hope that they’ll remember the 10/90 rule to ensure optimal returns. The money needs to go to you!)

This one flummoxed me.

Let’s see if you can internalize what is going on. Stare at the graph intently, seriously, and see if you get the points…


Bold items naturally catch the eye, in this case the blue bars. Most people in the western world look from left to right, that is how you’ll likely try and understand what’s going on.

Your first impression will likely be that the blue bars are showing a random trend in marketing spending.

If you are the curious type you’ll realize that is the wrong conclusion, and you’ll want to understand what’s really going on. Soon enough you’ll get to the x-axis and a carefully review will illuminate that the reason for the weirdness is the choice to show the industry names alphabetically!

7. Please, please, please keep the end-user in mind.

In this case the end-users (our senior leaders) would be primarily be interested in understanding where marketing spending is highest and lowest. This is very difficult to accomplish above.

Secondarily, they’ll want to know where they fall in context of all other industries, this is almost impossible to accomplish above.

The reason the x-axis is organized alphabetically is to allow you to look up your specific industry easily. This thought is good. My hypothesis is that it likely forms a small percent of the use cases, primarily because just knowing your spend is not that valuable. What’s valuable are the above two use cases.

Here’s what I recommend keeping front of mind: If a non-analyst is looking at the data, what uses cases form the basis of the value they’ll extract. Then, ensure the info viz is solving for that.

In this case the bars with the data seem to be randomly sorted. The visualization is getting in the way, creating a wider last-mile gap.

Luckily this is a quick fix in good old Excel. Two minutes later, you’ll have a little waterfall…


It is easy to see the outliers and the pack of eight that are close to each other (something you can’t even see in the original).

It will certainly take an extra couple of seconds to find your industry, but in service of the two bigger use cases,  it is a small price to pay.

You can play with the layout to your heart’s content. If you dislike waterfalls for some reason and prefer towers…


I like the waterfall, but this is not bad. 🙂

Play with the colors, drop shadows, fonts, and more. Make the graph your own. Just don’t forget to look at it through the eyes of the end user and solve for their use cases.

(Speaking of colors… I’m partial to chart styles 17 through 24 in Excel. In my work you’ll see a particular affection for style 18.)

I hate pie charts. I really do.

You can read a 506 word love-letter to my profound dislike (including a lovely exercise you can do).

Here’s the scientific reason:

Comparison by angle is significantly more difficult than by length.

That is well on display below…


The colors in the pie will catch your eye. Yet, from the sizes of the slices it is difficult to internalizes the differences between each dimension.

8. Eat Pies, Don’t Share Them!

Since humans find comparing lengths much easier, it should only take a few minutes to take the data and convert the slide above into something that closes the last-mile gap efficiently.


The above slide is a good example how to apply all the principles you’ve learned thus far. The question and the data are the hero, almost all by themselves. Allowing you to focus sharply on your story.

Scroll up and down and compare the two slides. You’ll see many more differences.

I’ve extoled the virtue of using a table, instead of trying to be extra sexy and throwing in a graphic.

The challenge with tables is that they can become overwhelming very quickly.

Here’s an example that illuminates that clearly.


It feels like there is a lot. It also breaks principle #2, false precision,  which makes things worse.

Considering the core message the analysis is trying to send, I believe that it is also breaking rule #4, extra perhaps unnecessary data.

9. Make your tables pop, guide the reader’s eye.

There are numerous tools available to you inside Excel to make your tables pop. I usually start by playing with the options at my disposal under Conditional Formatting.

One straight-forward option is to use Color Scales, green to yellow, to produce a simpler table that pops…


The elimination of the overall average makes the table tighter.

It is easier to look at the trend in each column. What’s even more delightful is the second use case of comparing the highs and lows across the four dimensions. So much easier.

While all the data is still there, most senior leaders want to understand trends and the contrasts. They want relative positioning, the above table does not require expending too many brain cells to get that. And, if your boss does not trust you… She still has the numbers there.

Notice the combination of fonts, colors, style treatments, in the table above. Bunch of subtle points there.

If your personal tastes are different, no problem. There are other styles you can use.

Here’s the data rendered using solid fill Data Bars…


In this case I feel data bars add clutter, but they make internalizing the trend across individual dimensions easier.

If, like me, you are biased towards radical simplicity via white space, you can keep the table. Consider applying some subtle font color treatment to create something that’s still a step change over the original…


I’ve shown the highs and lows in a way that you’ll see them quickly.

Red was chosen on purpose to emphasize that it was the most important thing from the customer’s perspective. Blue fades into the background a bit because it is the least important.

One final touch.

I felt it might be of value to see the product and services dimensions together, comparing them across B2B and B2C.

Here’s that version…


There’s a little air gap in the table to emphasize the two comparisons are different. You can usually use visual cues like these to help the consumers of your analysis.

We disagree on a whole lot of subjects in our country these days, but the one thing we can all agree on is that the human attention span is probably ten micro-seconds.

Add to that short attention span the fact that each executive has 18 other urgent things taking up their brain cells. As if all that was not hard enough, while you are presenting they are also likely on their phone or laptop.

Persuading anyone in these circumstances is a herculean task.

With that context in mind, how many leaders do you think will understand what’s going on here…


4 dimensions x 5 time periods x crazy swings = Ouch!

For bonus points, notice the randomness in the x-axis. It jumps from 2014 to 2017 without any visible explanation. To make things worse, look at the trend lines – they connect the two data points to imply a trend between 2015, 2016 that may or may not exist.

For even more bonus points, notice that there are four Februaries and as if it is no big deal an August is thrown in randomly.

Ouch. Ouch.

These might seem like small issues, but I assure you that you’ll instantly lose credibility with any intelligent leader in the room. They won’t raise their hand and start to berate you. They’ll quietly make a mental note about you, and then not pay any attention to anything you are saying.

There’s an even more important principle to learn from this visual…

10. Let the higher order bit be your north star.

It can be difficult to figure out how to go from the complex to the simple.  My recommendation is to start with the most important thing you are trying to say.

In this instance the goal is to illuminate the percent change in marketing knowledge in the next 12 months. So, are the rest of the data points necessary and of value?

In service of the higher order bit, I would argue that we can also get rid of the two Februaries and the lonely August. (Though I sincerely respect the effort it took to get those data points.)

With those decisions we are left with just two data points. We can move to a simple table and close the last-mile gap by creating this slide…

Simpler, right?

We can do one better.

If the objective is to just show the change, we can just show the percentage change.


The colors help focus the attention even more.

To see the dramatic change, scroll back up and look at the original and then come back here. Incredible, right?

It might seem that this is hard work that takes time. It does take more time. But, it is not in the ink rather it is in the think. Discussing, debating, really thinking through what we are trying to communicate. The visualizing part takes a lot less time.

The biggest problem with this type of analysis, compiled into 95 slides, is that it never answers the question why?

Take this slide as an example. It shares a very positive view of analytics…


The slide breaks all ten principles we’ve discussed in this post, but beyond that there is a bigger problem here.

11. Why. Your job is to answer why!

Your first instinct is the marvel at the shift (all blues are up!), and reflect on how this graph is long-term job security for everyone who reads this blog.  But, you’re an Analyst and that good feeling won’t last.

Your mind quickly goes to… Why? What is causing this shift?

Look at Mining/Construction, 60 percent points of change. OMG! Why?

The entity creating this report sadly never answers any why question anywhere. Perhaps by design.

But, consider this: Data creates curiosity. If the Analyst does not satiate that curiosity via deeper analysis that explains why, the same data turns into a disappointment. It certainly drives no change.

I’ve written about this topic before, using an example from Econsultancy and Lynchpin: Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Without the why your last-mile gap is a million miles wide. If you are going to be in the data regurgitation business, please consider it your job to answer the why question. Without it all this is… fake news.

A challenge for you to tackle.

Now that you are aware of the 11 principles that aid in closing the last-mile gap, I want you to tackle something on my behalf.

I had not idea what to do with this slide… Can you create an after version?


Partly the issue is that I could not truly internalize what was being said. Partly it is that the numbers don’t really seem to change much. Partly it is because I was torn between the graphic and the table on the top right.

Regardless, I gave up. Perhaps you can teach me, and our readers, what a version with a reduced last-mile gap will look like.

Just email me your version (blog at kaushik dot net) or comment below.

Here’s a summary of the 11 principles you can use to close the last-mile gap:

01. Graphing choices can exaggerate or undersell reality.
02. False precision can cause clutter, and undercut the Analyst’s brilliance.
03. Remove the distractions, ruthlessly.
04. Show as much data as is required, and no more.
05. Experiment with visualization options, even in Excel!
06. Don’t send a graphic to do a table’s job.
07. Please, please, please keep the end-user in mind.
08. Eat Pies, Don’t Share Them!
09. Make your tables pop, guide the reader’s eye.
10. Let the higher order bit be your north star.
11. Why. Your job is to answer why!

I wish you smaller gaps and more decisions that are data-influenced.

As always, it is your turn now.

In your practice, how wide is the last-mile gap? What do you think contributes to the gap the most? Which of the above principles have you used, to good effect? Do you have a favorite principle, or five, to close the gap? If you had to kill one practice when it comes to data presentation, who would be the chosen candidate?

Please share versions of the above examples that you’ve taken a crack at fixing. And, your lessons, best practices, and as always your critique via comments below.

Thank you.


Orignal Article Can Be Found Here

Create High-Impact Data Visualizations: Nine Effective Strategies

Green_Visual I believe deeply in the value of making data accessible.

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). There is something magical about taking the tons and tons of complexity that lurks in our data, being able to find the core essence, and then illustrate that simply. The result then is both a mind and heart connection that drives action with a sense of urgency. #winning

While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. As we have all learned, tools matter a lot less than what we do with the tool. 🙂

In this post I want to inspire you to think differently. I’ve curated sixteen extremely diverse visualization examples to do that. By design none of them from the world of digital analytics, though I’ll stay connected to that world from a how could you use this idea perspective. My primary goal is to expand your horizon so that we can peek over and see new possibilities.

To spark your curiosity, the visuals I’ve worked hard to find for you cover the US debt, European politics, lynching and slavery, pandemics, movies, gun control, drugs and health, the Chinese economy, and where we spend our lives (definitely review this one!).

The sixteen examples neatly fall into nine strategies I hope you’ll cultivate in your analytics practice as you create data visualizations:

This post has quite a bit of depth, and loads for you to explore, reflect and internalize. It will take a few visits to absorb all the lessons. In as much, my recommendation is to read one section per day. Take time to really understand what’s going on, go to the site, play, look at the higher resolution versions (click on the images), make notes of what you’ll do for the first time or change about what you already do. Most importantly, practice taking action. Then, come back, read the next one and take action. I promise, the rewards will be rich.

Let’s go make you an even more effective influencer when it comes to data!

Strategy 1: The Simplicity Obsession

One of the reasons so many visuals are so very complex is that the Analyst/Creator is trying to demonstrate how clever they are. Sadly in the process of demonstrating aforementioned cleverness, the visuals ends up being incredibly complex crammed with every little bit of amazesomeness they  are trying to demonstrate…

us maximum personal income tax rate vs national debt burden per capita
(Click on the above image for a higher resolution version)

There is absolutely no doubt in my mind that the Creator worked very hard, and, I sincerely mean this, they are very clever.

The problem is that the essence of what they want to communicate is probably only known to them, or to any person willing to take the time to first learn the job of the analyst, dig into the data themselves, create this picture and then understand what is being said.

It breaks my heart.

Go on. Scroll back up. See if you can understand what is being said.

In my humble opinion there is an additional subtle problem. The Creator was asked to plot the data, or perhaps share the insights, but it is unclear whose job it was to answer this simple question at the end: So What?

When you start with that as your destination, so what, as the creator of any visualization you are going to ask for a lot more context, you are going to make sure the visual is in service of the answer, you’ll make sure your cleverness is focused on the outcome the data has to serve.

Please, please, please keep that in mind.

The complicated thing above is trying to highlight an important trend, is missing the context, and is simply not as dramatic as the reality of it actually is!

Here’s a better visual showing the National Debt Burden, with four additional elements of context…


Did you get what the point was in zero seconds?

Are you a whale-load more scared as you contemplate the red and the green?

Are you freaked out that if there is one thing both political parties in the US seem to be good at it is the red (!)?

That is what a good data visual does.

For the few of you that are a part of the team I lead, in addition to creating a visual for your analysis that is simple and effective, you know that my expectation is that you’ll come with recommendations on what to do.

To demonstrate that there are many paths to JesusKrishnaAllah… Here is another simple view of the debt, with a different x-axis, a stretched out y-axis, along with a different set of context…

(Source: CBO)

Different questions, different arguments, different outcomes. But, you’ll get to them much, much, much faster than the first visual.

I can’t stress this enough: Don’t try to earn your performance review from the client/audience. Earn it from your boss. Tell your boss how hard you worked, show her how clever you are, earn her praise. Spare your client/audience – show them the simplest manifestation of your brilliant insight, with the NACR criteria applied.

(For more on using NACR to identify out-of-sights, see TMAI #66.)

Strategy 2: If Complex, Focus!

You are going to see my deep bias for simplicity for the rest of this post (or in the 745,540 words written on this blog thus far). I do not want to come across as a simplicity snob.

Deployed well, there are instances where I love complexity.

I thought this was exceptionally well done…

(Source: Michael Paukner  |  His Flickr collection)
(Click on the above image for a higher resolution version)

While it is a little difficult to follow all the arrows back to the original country, the shape of the graphic is an homage to the visual’s topic. The background color could not have been more prefect. And, notice there is just the perfect amount of information about every tree.

There are other more subtle things to admire. I love, love, love that Michael put the US on the right. When we “trip up” our audiences like this,  it gives them a pause and forces them to look at all the other information more carefully.

There is of course data itself that gives you many pauses. Notice the youngest tree in the graphic is older than Jesus Christ. Or, that we should all be so glad that the American West was settled last (by then we were more appreciative of nature as humans).

I am fine with complexity, if the essential makes it through. I am fine with complexity, if someone who’ll spend 1/100th of the time on the visual compared to you get’s it.

Strategy 3: Venn Diagrams FTW!

I love Venn diagrams. Ok, strictly speaking Euler. But, let’s not get pedantic.

I’ve used them to simplify the presentation of complex topics. Ex: Six Visual Solutions To Complex Digital Marketing/Analytics Challenges

I am only slightly kidding but one of humanity’s most complex undertaking is to understand what the heck Europe is. One end’s up ruing even asking, because you hear back EU, EEA, Euro Zone, Schengen, EFTA, and more.

I felt Bloomberg did a wonderful job with, what looks like an amoeba-inspired, Euler diagram…


(Click on the above image for a higher resolution version)

The color schemes are contrasted enough to allow you to follow along nicely.

The context from the sizes of the economy is a nice touch. (This is embarrassing but I was surprised how big Italy is, and how small Sweden is.)

The clusters of countries next to each other, for the sake of cleaner lines, all by itself has a built-in message. Cyprus and Ireland. UK, Romania, Bulgaria and Croatia. So on and so forth.

Overall, this is a topic that has been tackled numerous times, with painful to see results. Bloomberg managed to make it as simple as possible, with valuable built-in context.

Staying in the same geographic area, and my Euler-love, here’s another fantastic visualization of often a very complicated answer: What is each political party in the UK promising?

I adore this as the answer…

(Source: Economist)
(Click on the above image for a higher resolution version)

Would you have believed that the totally out there UKIP would have something on common with Labour? Or that Labour is completely alone in the minimum wage issue?

The visual makes it easier to understand what we might be most interested in from the thousands of pages that form each party’s manifesto. You, the audience, is now empowered to agree more passionately with your party or feel the uncomfortable squirming that comes with realizing what your party is solving for. Both. Fantastic. Outcomes.

Clearly this is a political picture, and someone has to decide what to include and what to exclude because the parties promise the Earth, Moon and the Andromeda galaxy. But that is the life of an Analyst… They have to make tough choices.

Two hopes.

1. I hope every single news organization in every single country in the world will copy this visualization and create it for their main political parties. (Also see related NYT example on Guns below.)

2. What will you do with this? Can you pull out all the content types from your digital existence and create a visual like this one for which goal (overlapping goals) each type is solving for? How about displaying countries and products purchased? Oh, or your main traffic sources and the visitor acquisition metrics?

So much to do, so simply, and so little time!

Strategy 4: Interactivity With Insightful End-Points.

There is a common belief that your company’s decision makers would use data more if they could explore it – more efficiently, deeper, etc. This is almost never true, primarily due to the problem outlined in the orange and blue triangles that outline skill/competency and insights/action.

Hence, in a business context I rarely advocate for initiatives whose only purpose is to allow the broad collection of company employees to go on random fishing expeditions.

Exploratory environments can be useful, especially when they are 1. sharply focused 2. have an ability to eliminate dead end-points and 3. allow for smart elements like modeling. Let’s look at the first two below and the third one in the following example.

Here’s a valuable dataset from the Equal Justice Initiative on Lynchings in America.

(Click on the above image for a higher resolution version)

Even at a glance the data is useful, along multiple dimensions.

In this case exploration of the data makes it even more valuable. You hover your mouse over your area of interest, and click…


You get your data drill-down, but what’s of most impactful is that you also get an end-point with a valuable insight providing meaning to the data.

In this case the number 29 for Jefferson County would be an insufficiently valuable end-point. The inclusion of Elizabeth Lawrence’s story on the other hand provides meaning. That is what gives the exploration a purposeful end-point.

You can now zoom out, move on to exploring other areas, continuing to get enriched value from the data.

In a business context when you are working with interactive data visualizations, ask this very valuable question: In a sea of data, whose job is it to include a logical end-point with an insight of value?

Surely, your terabytes of Google Analytics data dumped into a Tableau exploratory thingamagigy won’t magically throw them out there.

Surely, lay business decision makers, even senior ones, won’t have all the context they need to have to convert thingamagigy fishing expeditions, sorry, explorations, into the brilliance you feel the data contains.

Interactive visualization are great, only when packaged with insights for actions at logical end-points in exploration. Tweet that.

This is a difficult example to share because of the deeply emotional content it contains. But, those who do not learn from history are doomed to repeat it. Beyond the value of the lessons from the visualizations, I encourage you to explore rest of the EJI website. At the very minimum please consider spending five minutes listening to the story of John Hartfield told by Tarabu Kirkland, and six minutes on the story of Thomas Miles Sr told by Shirah Dedman. Thank you.

Bonus: Another insightful visualization on this topic is at, The Shape of Slavery


A bit more complex of a visualization, a function of the depth of data populated.

Follow the story of Louisiana as you reflect on the data.

Lots of data visualization, storytelling and life lessons in this data set as well.

Strategy 5: What-if Analysis Models.

Building on the thought above, if you create exploratory environments it can be exceedingly accretive to decision-making if we build in what-if type models. Rather than stopping at an end-point, provide an option of doing some type of sensitivity analysis with the goal of prodding the audience to take action.

For example… Let’s say they end up looking at Visitors, Conversion Rates, and Revenue. You can easily imagine how you want someone to explore that data by traffic sources or campaigns or geo or myriad valuable dimensions. You can create an environment where they press buttons to get that data.

Necessary, but not sufficient.

Why not build in a model where the decision maker can change Conversion Rates, to see the impact on Revenue? Move it from 1% to 1.5% to 8%. See what happens by traffic sources. Then, make a smarter decision.

Or, empower them to play with discounting strategies. What happens if they offer a 5%, 10% or 18% discount? Show impact on Revenue and Profit.

Even without bundling insights into your prepackaged environment, the what-if models allow your decision makers to play with scenarios, understand impact and make smarter decisions about what to do.

That’s the key. Don’t make visualizations with dead ends.

Here’s a great example of that from Mosaic. The visualization is about outpacing pandemics.

Quoting them: Vaccines are an essential weapon in fighting disease outbreaks. But how does the time taken to develop vaccines compare to the speed and frequency of outbreaks? And how can we do it better?

This is the simple view that greets you, outbreaks from 1890 to 2016 with vaccine development during that same time…


Each element is clickable.

As an illustration, the longest bar is Typhoid fever and the smallest, mercifully, is Measles. For each bar, click on Measles, you’ll see the first big outbreak (1917, 3,000 deaths) and the last (1989, 123 deaths). It is really easy to explore the data.

What I love is the sensitivity analysis.

Click on the yellow dot, and you’ll see that in action. First, you see what actually happened…


Simple exploration. Good reporting. Easy to understand.

The buttons with the number of weeks represent what I wanted to highlight here. Click on them, and it demonstrates what the outcome would have been if action was taken earlier.

I choose 22 weeks…


Even if the vaccine had been introduced after 22 weeks, a long time, we could have saved 1,628 lives!

The team also built in some hypothetical scenarios to help inform decision-making.

You can play with the implications of a fast-moving flu-like pandemic. It would have grievous overall impact, 30 mil deaths in 12 months.

But, what if we restrict 50% of the travel since we don’t have a vaccine yet. That would have an impact…


Not quite as material as one might imagine, but it slows things down.

What if a vaccine was introduced 22 weeks in?


Insanely helpful. 17 mil lives saved.

This type of modeling is rarer than seeing a rhino in the Ngorongoro crater. (We were there last week, you should go, it is pretty awesome.)

As an analyst, as a Big Data person, as a Data Scientist, pouring the right data on humanity is only marginally effective. In this example, in others above, I hope you’ll see the type of additional creativity we can bring to our work to power smarter decision-making. Starting with no dead end-points.

Strategy 6: Turbocharging Data Visuals with Storytelling.

You know this. Even if data is shared in a simple environment, most people are unable to internalize it. As has been hinted in most examples today, the problem is that the Analyst’s brain has not been packaged with the data.

The Global Gender Gap Report is a fabulous example how to solve this problem. The team nor only shares in a simple and beautiful environment, they also include the story they want to tell in that same environment. The output is not the reporting, the output are the conclusions from the Analyst’s brain.

It is very difficult for me to show the beauty of what they have done in static screenshots. You just have to go there and scroll.

Explore how the initial trend in the gender gap morphs into multiple visualizations, note the subtle but important emphasis on trends, and, most importantly, feel joy from how the story is presented with the data (text on the right).

The website and visualization will work on your mobile device (yea!), but it is best admired on the largest screen you can find.

To tempt you, let me just contrast the gender gap performance of the United States (precipitous decline in the last two years!) with… with… inspired by FLOTUS, the 10 year performance of Slovenia…


Play with the histogram and scatterplot options.

Go back and forth a few times (yes, gender parity is an issue I care deeply about), make sure you absorb the many nuances both in the story (why the above stinky performance by the US?) and the way the text (story) and the visualization (data) play together.

When you send data out, is it bundled with a piece of your brain?

Remember, you’ll be the last person with the intelligence and skills to understand the deep layers and nuances in what the data is actually saying (assuming you are an Analysis Ninja!). It is imperative that your brain go with the data.

Bonus 1: Another fantastic example of this type of sequential storytelling is Film Money….


Lars Verspohl takes you along on a wonderful journey through cost and profit structures of movies. Like me, you’ll love the simple and delightful visualizations, how gracefully flow it all flows, and that all the charts and data are primarily there to support the story that emerges from his analysis.

Please also note the thought put into the order in which the story is told, if and when the visualizations switch (from the one above) and the techniques deployed to keep you interested. All excellent, loads to learn.

Bonus 2: This is one subject, storytelling, that I just love, love, love. Indulge me as I pile on and share one more, dramatically different, example of storytelling where data and text go hand in hand.

The team at Reuters Graphics does a fab job of explaining China’s debt problem.


Almost all the visuals are extremely simple. As you scroll through, observe though how they peel back layers of the onion one by one, segment the data, and zero in on the core point they want to make.

Really lovely. Worth emulating.

Strategy 7: The Magic of 2 x 2 Matrices

If you’ve read anything on this blog, you’ve read the importance of seeking why answers to provide critical context to the what answers that you get out of Adobe or Google Analytics. Hence, the amazing value of Surveys, Usability Studies (on or offline), Heuristic Evaluations, shadowing Customer Service calls, and more.

Customers are an amazing source of problems they are having, sometimes they are also a good source of ideas. The challenge is that if you ask people for their opinions you get tons of ideas.

How do you value them? How do you present them? How fast can you get from data to action?

One solution I love is a visualization strategy used by the team at the New York Times. The example illustrates, simply, the ideas related to an emotionally charged topic: Gun Control.

Everyone knows this is a polarizing topic. Friend against friend. Blue vs. Red. Police and minorities and every other combination thrown in. It is a mess.

But. Is it really as fraught with angst as we believe?

No. It turns out if you ask Americans about individual ideas that will reduce gun deaths… A vast majority of us agree!!


The lowest supported idea is “Demonstrate need for a gun.” Support for it is just shy of 50%. A number that simply sounds unbelievable. 

Did you think vast majorities in our countries agree with these common-sense ideas? I have to admit I did not. It is hopeful data.

But, this is not the reason for the inclusion of this visual on our list.

Rather than just share the ideas, the NYT team added incremental value (remember packing the Analyst’s brain?) by asking Experts to opine on the effectiveness of each idea. That’s what you are seeing in the distribution above.

From the 2×2 matrix, here is the slice of ideas American’s support and the ones Experts say are effective…


There are only two ideas rated as ineffective by Expert, but are supported by over 70% of the Americans (national stand your ground law and honor out-of-state conceal and carry permits).

We all basically agree on ideas, and a lot of them will have an impact.

I love the presentation of the ideas and the fact that Experts were brought in to give valuable context. This is what I meant in my above example by not simply taking all the customer ideas and running with them. A wonderful way for you to visualize multiple ideas, and you can combine it with an Expert dimension or a Customer Satisfaction dimension or even a Revenue dimension to give context to the ideas.

One last element of value from NYT.

I’ve said that all data in aggregate is crap. I’m so happy that the NYT team also segmented the data.

What does Mr. Trump support…


What do American law enforcement support…


And, lots more slices that make the data even more meaningful.

Segment. Always, always, always segment!

It is beyond the scope of this humble analytics blog to explore why in the face of such unanimity that nothing actually happens when it comes to reducing gun violence in the US. But, for lovers of data, for believers in the power of data to drive smart decision-making, this is one more reminder on the limitation of data if you can’t tell the story properly.

Strategy 8: Close Contextual Clusters.

Let’s close with examples of work that you’ll normally include in your enterprise analytics efforts.

Usually data we have is lonely. Just the Visits or Assisted Conversions or Order Size. Without other contextual elements, it turns out this data is less useful.

Consider this, conversion rate could go up by a statistically significant percentage… While revenue actually goes down. Or, the overall Visits to the site stay steady… But drop dramatically from your usually second highest source.

The European Monitoring Center for Drugs and Drug Addiction, also known by the gorgeous acronym EMCDDA (!), publishes a ton of data. Their Statistical Bulletin 2017 has a lovely collection of graphs and charts that we all use in some shape or form. The only difference is that we rarely report on Heroin Price and Purity. 🙂


Along with the use of (mostly) simple visuals to illustrate the data, I appreciated the context that they provide. Sometimes using the time dimensions, sometimes using geographic breakdowns, sometimes using two likely interplaying elements (like above), so on and so forth.

This simple strategy is quite effective at delivering insights – or at least causing the audience to ask relevant interesting questions.

I encourage you to take some time and explore the numerous examples on the site


I’m confident the visualization strategies will spark upgrades to the work you are doing at your company to communicate data more effectively.

Our friends at the EMCDDA mostly avoid two things that I find as poor practices in data visualization. They triggered this in my mind, let me take the opportunity of sharing them with you.

1. Never ever, never, never, never create the loooooooooonnnnnnnggggggg infographics that seem to be in vogue these days. Essentially they are taking 69 “slides”/graphs/tables and shoving them into a 9-meter-long thing that no browser can render decently. By the time you absorb the third screen full of stuff in tiny font/image, you’ve already forgotten what’s on the second.  You have many examples in this post as to how you can avoid making yourself look like sub-optimal Reporting Squirrel.

2. Pie-charts are a very poor data visualization choice. Humans find comparison by angles significantly harder than, for example, by length. I explain this a lot more in the May 14th edition of my newsletter The Marketing Analytics Intersect: Eat pies, don’t share them.

[You should subscriber to TMAI for a weekly dose of intelligence that’ll keep you at the bleeding edge of our industry.]

Bonus: In the spirit of government data, I’ll be remiss if I did not share with you three examples of interactive scatter plots from Our World in Data (produced by the University of Oxford).

The second one is timely, it shows how when we look at health spending and life expectancy the United States is a massive outlier (and not the good kind)…


I love fusion charts, the first one on the site, Child Mortality vs. Mean Years of Schooling, is a good example of that as well. And, it shows great news.

Please review all three. Then, consider plotting one for your digital data. Conversion Rates by Discounts for Top Ten Traffic Sources. Time on Site by Visits to site for Content Types. And, more.

Strategy 9: Multi-dimensional Related Line Graphs.

One final example, to cause introspection about the final years of your life.

Wait. Things really got serious.

They did. But, I really do want you to lean into this one.

A small reason is that you are likely creating graphs like these every single day for your dashboards. I hope you’ll find lessons in how to make yours simpler. Notice the use of fonts and colors. Notice the labeling, or not, of the axis. And other little things.

A big reason is that I care for you deeply and I want this data to be a cautionary signal to all of us to possibly start making new choices.

The plots are from the American Time Use Survey, a multi-year study from 2003 to 2015 conducted by the US Bureau of Labor Statistics.

Age on the x-axis and hours we spend per day with on the y-axis…

(Source: halhen on Reddit  |  Github)

In our 20s we’ll spend most time with our friends and our parents. Our partner and co-workers will take over our lives from then on through our 50s.

I’ll let you internalize the rest, and please share via comments what you see as the lessons in this data.

Three things stood out for me, as I consider the larger latter chunk of life. 1. We might be giving an extraordinary amount of importance to our co-workers, perhaps worth a rethink. 2. I love my spouse, regardless of who goes first, I felt very sad after staring at the Partner and Alone graphs. 3. The data demonstrated the value of loving oneself – of being proud of who you are, of being comfortable in one’s own skin. After all each individual will spend huge chunks of a decade plus… alone. You have from now until you are 50 or so to get there. Hurry!

: )

The power of great data visualized simply.

Closing Thoughts.

The sixteen diverse sources and visualization strategies help you think differently about how you are bridging the critical last-mile when it comes to impact from data – from you to the person who’ll take and action of business value. We don’t give enough time and attention to this last-mile.

While some of these clearly take special skills (especially the ones that tell integrated stories), I hope you’ll note that most of them are simple and ones that you can create with just a little more effort.

What’s most important today is that I’ve sparked your commitment to upgrading your personal data visualization skills.

Good luck!

As always, it is your turn now.

Which one or two examples did you like the most? Why? Is there a visualization technique you deploy in your analytics practice that’s not covered in this post? What barriers prevent you from improving your data viz skills? What are your pet peeves when it comes to data visualizations? Do you have go-to sources when it comes to inspiring you?

Please share your tips, best practices, critique, and praise for the people who created the above examples, via comments.

Thank you.

PS: I was not kidding in the opening of this post… I’ve written a lot about data visualization and shared guidance for this type of storytelling in numerous different contexts. To continue your immersion, here’s another collection of knowledge…

I hope you love it, and paint more beautiful pictures with your data.

Create High-Impact Data Visualizations: Nine Effective Strategies is a post from: Occam’s Razor by Avinash Kaushik

Source: Avinash

The Very Best Digital Metrics For 15 Different Companies!

Colors The very best analysts distill, rather than dilute. The very best analysts focus, when most tend to scatter. The very best analysts display critical thinking, rather than giving into just what’s asked of them. The very best analysts are comfortable operating with ambiguity and incompleteness, while all others chase perfection in implementation / processing / reports. The very best analysts know what matter’s the most are not the insights from big data but clear actions and compelling business impact communicated simply.

The very best analysts practice the above principles every day in every dimension of their jobs. When I interview candidates, tt is that practice that I try to discern carefully. When I see evidence of these qualities in any candidate, my heart is filled with joy (and the candidate’s inbox is filled with a delightful job offer).

This post shares one application of the above skills.

People ask me this seemingly simple question all the time: What Key Performance Indicators should we use for our business? I usually ask in return: What are you trying to get done with your digital strategies?

From experience, I know that there is no one golden metric for everyone. We are all unique snowflakes! 🙂 Hence the optimal answer to the question comes from following a five-step process to build out the Digital Marketing and Measurement Model.

But, what if we did not have that opportunity? What if I was pushed to answer that question with just a cursory glance at their digital existence?

While it is a million times less than ideal, I can still come up with something good based on my distillation skills, application of critical thinking, comfort in operating in ambiguity and prioritizing what will likely help drive big actions. It won’t be perfect, but that is the entire point of this post. It is important, even critical, that we know how to operate in such a environment.

To impress you with the breadth and depth of possibilities, I’m going to take 15 completely different digital companies and share what are the very best key performance indicators (metrics) for each. I don’t know these companies intimately, just like you all I have is access to their digital existence. That’s what makes it such a great exercise of the aforementioned skills.


In the past I’ve shared a cluster of metrics that small, medium and large businesses can use as a springboard…

These are great starting points, but there is an assumption that based on your expertise and business knowledge that you’ll be able to personalize these.

The challenge I want to take on is to be specific in my recommendations, and to share how we can be very nimble and agile.

You’ll see three consistent patterns in the thinking expressed below (I encourage you to consider adopting them as well).

1. You’ll notice that I ask the five questions that help me identify the higher order bits related to the company. This is critical. They are from my post The Biggest Mistake Web Analysts Make… And How To Avoid It!

2. I am a passionate believer in focusing on the Macro AND Micro-Outcomes. It is the only way to ensure your leadership is not trapped in the let’s solve for only 2% of our business success thinking.

3. It pains me how quickly silos emerge in every company. There are Search people and Content people and Landing Page Optimizers and Cart fixers and Attribution Specialists and more. Everyone solves for their own silo, and IF everyone delivers you get to a local maxima. #tearsofpain One way of removing silos and focusing on the entire business is to leverage Acquisition, Behavior and Outcome metrics. This will allow, nay force, our senior business leaders to see the complete picture, see more of cause and effect, and create incentives for the disparate teams to work together.

A small change I’ll make in this post is that when I recommend the metrics, I’ll follow the Outcomes | Behavior | Acquisition structure. I’m reversing the order because when you talk to Senior Executives, they first, sadly sometimes only, care about all the moolah. We bend to this reality.

Hold me accountable to the above three patterns, if you see a mistake… please let me know via the comment form below.

Also, it is unlikely that I’ll have perfect answers for all 15 businesses below. Please chime and let me know what you would use instead or simply how would you improve the collection of metrics for each type of company.


Let’s look at 15 completely different business, and pick just six metrics (two each for A, B and O) that would be the very best ones to measure their digital success. The goal is for each company’s Google Data Studio to not look like a CDP (customized data puke), but to be a focused strategic dashboard with an emphasis on IABI.

If you want to play along. Don’t read what I’ve chosen. Click on the site link, go browse around, go to their social pages, checkout the mobile app, then write down the six metrics you would choose. Then, read on to see what I picked. You’ll discover immense learnings in the gaps between each set of choices (and share yours with me in comments below!).

Ecommerce: Betabrand

I love Betabrand. Their clothes and accessories are eclectic. The brand has a joy that is infectious. And, I’ve been impressed at how they’ve innovated when it comes to what business they really are in.

Here are six O, B, A metrics I would recommend for Betabrand’s strategic dashboard:

Outcomes: Revenue | Ideas Funded
Behavior: Path Length | Cart Abandonment Rate
Acquisition: Assisted Conversions | Share of Search

Every ecommerce site has to obsess about Revenue, hence I use that as the Macro-Outcome. After a consideration of their business evolution, I picked Ideas Funded as the important micro-outcome.

I love driving strategic emphasis on Path Length for larger ecommerce sites as it encourages an obsession away from one-night stands which is the standard operating model for most sites. The implications of Path Length will force a broader analysis of the business, which is harder and hence you’ll hire smarter analysts (#awesome). I feel Cart Abandonment is such an overlooked metric, it has tentacles into everything you are doing!

No decent ecommerce entity can live without a hard core focus on acquisition strategies that are powered by out-of-sights from Assisted Conversions data. Finally, Search (organic and paid) continues to be one of the largest contributor of traffic on mobile and desktop. Analyzing your Share of Search, from context you can glean from competitive intelligence tools), is extremely valuable.

Six simple insanely powerful metrics, simple business booming strategic dashboard.

What’s most important above is the thinking on display, the approach to identifying what’s absolutely essential, and an obsession with the higher-order bits. You swap out Ideas Funded for something relevant, and the above six can be used by any large ecommerce business.

A quick best practice.

You’ll also segment these metrics by your most important priorities.

For example, your company is shifting aggressively into leveraging Machine-Learning in your marketing strategies and hence have made a shift to Smart Display Campaigns a huge priority. Wonderful. You would segment the Assisted Conversions report by your Smart Display Campaigns to validate the power of Machine Learning. Remember: All data in aggregate is crap, segment or suck.

For the rest of this post, I’m going to try really hard to stay with the non-segmented metrics as it is much harder to pull that off. But on occasion I’ll mention the segment that would need more analytical focus as I believe it would yield a higher percentage of out-of-sights. You’ll see that on display in parenthesis (for example below).

Small Business Ecommerce: Lefty’s Sports Cards & Collectibles 

What if you are a tiny local business in a narrow niche, should you use the same approach as Betabrand? No. Always adapt to what’s most important and sensible for you (every measurement decision you make has a cost!).

Within a few minutes of visiting Lefty’s site – put on your sunglasses first – it will be clear that Lefty’s does not really care about their website. You can still put together a quick dashboard that will allow Jim and Bob to make smarter decisions by understanding the importance of their digital presence. Here’s how they could invest their limited budget smartly:

Outcomes: Autograph Pre-orders | Email Signups
Behavior: Unique Page Views (Gallery) | Bounce Rate (Mobile)
Acquisition: Visits | Click-to-delivery Rate

When my kids and I go meet their baseball heroes for autographs, we always book online. Hence the macro-outcome. Additionally, it is pretty clear from their site that email is a very big deal for them – and an ideal cheap marketing / acquisition strategy for them – hence the micro-outcome is Email signups.

Lefty’s stinks when it comes to user experience, even more so on mobile. Hence, I elevated Bounce Rate to a KPI (something I advice against). With the assumption that Galleries drive a lot of people to sign up, the value of UPVs rise in stature.

For a small business Visits are an important metric, even 500 more Visits a week can be huge. Since email is so important as an acquisition channel (and since likely nothing else works for them), I choose one of my three favorite email marketing metrics, CTDR.

Though we have looked at only a couple businesses, I hope you are starting to see common patterns in the approach to identify KPIs. Focus on what’s actually important from a strategy perspective. Macro and Micro-Outcomes. A focus on getting a sense for what the business is actually doing to make the hard choices needed to get to the perfect A, B, O metrics.

A quick best practice.

The metrics you elevate to Key Performance Indicators rarely stay there forever – that would be suicide. You’ll go through the normal metrics lifecycle

If you truly create strategic dashboards, follow the complete process above every six months. On the other hand, if your dashboards are CDPs (customized data pukes), be honest with yourself, I recommend doing this every three months.

B2B / Enterprise Sales: Salesforce

Very little B2B selling is data driven, this gives me profound grief. Mostly because in a B2B context we can deliver such an amazing impact! We as in digital marketers, salespeople, support people, analysts. Let me come back to that thought in a moment, here’s what I would recommend we measure for Salesforce:

Outcomes: Lead Conversion Rate (Visitor) | Trailheads Certified
Behavior: Page Value | Session Quality 
Acquisition: Visitors (Mobile) | Click-thru Rate (Paid)

Since every SINGLE thing of customer value at ends with the same gosh darn lead gen form, we measure Leads. 🙂 We focus on the better conversion rate definition, divide it by Visitors (or Users in GA). It creates an incentive to focus on people, and give each individual visitor the breathing room they need to convert (the burden then shifts to the company to be able to think smarter when it comes to the experience and incentives). I choose Trailhead Certified as the micro-outcome as there are multiple points of value from the Trailheads program (lower support costs, higher retention, faster time to value for clients etc.).

The site has tons and tons of content, almost haphazardly so. Hence for behavior the magical Page Value metric. It will help Salesforce hold every piece of content accountable for delivering business impact (macro or/and micro). Session Quality leverages machine learning to provide Salesforce with behavioral analysis to help personalize the user experience and customize off-site marketing experience. It is a cool KPI you should explore for businesses of any kind.

Mobile is massively undervalued by most B2B companies (including SF), hence the acquisition emphasis there. CTR puts the emphasis on right message to the right person at the right time.

B2B analytics are insanely sexy and exciting. Yes. Really. Please be creative in your analytics efforts, and don’t take no for an answer when it comes to the value of analytics. Don’t accept the excuse oh but all the sales come via phone or I convert at industry events or our buyers are old school! 

A quick best practice.

Push. But, be picky, focus on big important pieces. For example, Salesforce spends tons of people/money on social media posting/activity and you can see this on display on their Facebook, Twitter, YouTube, and other social platforms. A cursory review will demonstrate that a low double digit number of humans engage with this massive amount of content Salesforce publishes. Almost all that investment is wasted (and don’t even get me started on the opportunity cost!).

Yet, you won’t notice it in my KPIs. Yes, their current social strategy not great use of time or money, but we have bigger fish to fry. Make tough choices.

Newspapers: Tampa Bay Times

I am a huge political junkie and it truly breaks my heart that newspapers are dying. I pay monthly subscriptions for the Guardian, New York Times, Washington Post, The New Yorker and National Geographic. We are a better humanity thanks to the work of journalists, I hope the industry finds a sustainable business model.

You’ll see my pet peeves about what media entities don’t measure currently in my recommendations:

Outcomes: New Subscriptions | My Edition Signups 
Behavior: Recency | Unique Page Views (Content Groups)
Acquisition: Visits (Referrals) | % New Visits

With advertising revenue in a tailspin, New Subscriptions are more important than ever and hence that’s our macro-outcome KPI. I have a massive bias against the current click-bait, let’s go viral, “hot story of the moment” traffic. I humbly believe the answer is to solve for loyalty, which if we don’t suck at it, will drive New Subs. Hence, the micro-outcome choice is My Edition Signups. It forces TBT to assess if people find the site valuable enough to open an account, and is TBT then personalizing the experience enough to drive loyalty.

Continuing the obsession with deeper relationships… TBT is a newspaper that’s updated 80k times a day, how does the Recency distribution look like? I visit the New Yorker 8 times each day on average (closer relationship, higher perception of value, and as a result I’m a paying subscriber). Our second behavior metric, Unique Page Views, helps quantify content consumption.

Here’s a lovely graph, from one of my older posts, that would be immensely valuable in trying to find the balance between content production and content consumption.

I would tweak it a bit. For each section of the site, Unique Page Views vs. Amount of Content published in that section. It provides critical food for thought in trying to balance what content and people does it need more of and less of.

In picking acquisition metrics I’m trying to counterbalance my bias to have deeper individual relationships over time. Visits – with an emphasis on referrals, with a deeper segmentation of social and mobile because of how humans get content these days – and % New Visits to grow.

A quick best practice.

You are always going to have biases. It is ok. Invest in becoming aware of them. And, when you catch yourself taking actions due to those biases, correct for them in the best way possible. In the above case, I counterbalanced for my bias in Behavior and Outcomes by choosing against my bias in the Acquisition section.

Charity/Non-Profits: The Smile Train

As some of you know, 100% of the proceeds from both of my books are donated to charity. Thus far, well over $100,000 each to The Smile Train, Doctors Without Borders and Ekal Vidyalaya. Thanks for buying my books.

Digital is a valuable component of The Smile Train’s strategy, here’s how we can measure effectiveness…

Outcomes: Donations (Online, Tracked phone calls) | Cause Related Clicks
Behavior: Amplification Rate | Completed Views (Videos, Stories)
Acquisition: Visitors (Geo) | Clicks (Social)

Donations, straightforward. Of all the micro-outcomes the one that was really innovative (and trackable!) was the Cause Related Marketing effort. So clever of them to become a part of people’s lives to raise money rather than the usual annual donation.

Charities can only market themselves so much, they have to figure out how to get the rest of us to do it for them. They have great content, if we believe in them then ST has to get us to amplify it for them. I love the stories they have, there is the obligatory collection of social links on the top, but they don’t overtly ask you to amplify. How about if I scroll through most of the story then a subtle pop-up from the bottom-right asking me to amplify via my social channels? I can get them to more people like me, more donations. Hence, Amplification Rate is my first behavior metric (to incentivize both ST and site visitors). Smile Train has precious resources, leverage event tracking to measure completed views of all the content is a fabulous way to drive a persistent focus on content optimization.

Charities have opinions about where their donors come from, I recommend a Geo segmentation strategy to understand Visitors to the site to broaden the leadership’s horizons (literally!). You can of course segment this by other elements. Social is a big part for every charity. To avoid Smile Train peanut-buttering their social strategy, measuring Social Clicks is a really sound way to understand where to put more/less effort.

A quick best practice.

Digital strategy for nonprofits should be more innovative than what you currently see. For example, for me the coolest lesson of Bernie 2016 is the mobile fundraising innovation. So, so, so many clever things done that charities should learn from and implement when it comes to their mobile strategy (to complement their 1961 strategy of text Red Cross to 12347 to donate $10).

Pharmaceutical: Humira 

There are some restrictions on selling prescription drugs in the US. This places some limits in terms of what we can track in web analytics tools. Not just PII, which we can’t track anyway, but the ability to use anonymous cookies for remarketing so on and so forth. Still, we can provide transformative KPIs in our Pharma practice:

Outcomes: Humira Complete Signups | Doctor Lookups 
Behavior: Unique Page Views (Condition) | Visitor Status (Login)
Acquisition: Visits (TV) | Click-Share (Search)

You can get tons of enticing stuff if you sign up for Humira Complete, including a Savings Card, and clearly the brand gets a lot out of it. Hence that’ll be our macro-outcome. There are lots of micro-outcomes, in this case given most Pharma companies are still in the early stages of savvy, I choose something close to making money, Doctor Lookups. I know Pharma companies also value prospective patients downloading the Discussion Guides which could also be a micro-outcome (in this case you get that after you do the Lookup).

The Humira site solves for 10 different conditions. That makes UPVs a great KPI to get deep visibility into what content is being consumed. The site hopes to drive a beyond the prescription connection with patients, with loads of resources behind the login. Hence, we use custom variables to track logged in status and we can analyze a whole host of valuable behavior and optimize our investments.

Humira does not believe in digital (ok, I’m just teasing them) but they love, love, love TV. Analysis that leverages their complete media plan in conjunction with site traffic will help provide one important measure of TV effectiveness. Ditto for any other major offline blitzes that Abbvie is running. Our last piece of the puzzle is AdWords Click-Share. There were 1,592,527 searches for Ankylosing Spondylitis, how many those clicks did you get? 1.2%. Great. Now shoot for 20% if you actually believe your drug is effective!

A quick best practice.

There is only one channel where our ability to discern intent is super-strong: Search. On Yandex. On Baidu. On Google. On Seznam. It is a little silly to think of Search in archaic terms like “Brand” and “Category.”

Think in terms of clusters of intent that you can solve for. See. Think. Do. Care. Search will solve for Think and Do. Sometimes your “Brand” terms will have weak commercial intent – in that case you should have Think Targeting and Think Content marketing strategies. Likewise your “Category” terms might reflect strong commercial intent, in that case Do marketing strategies will allow you to win bigger.

Let your competition be lame and play by a 1997 worldview. You take advantage of them by living in 2017!

As the post is getting long, understatement of the decade, let me just make recommendations for metrics for rest of teh businesses, and let you explore the site to figure out why they make the most sense in each case.

Government: California Department of Motor Vehicles

I love governments!

Outcomes: Online Applications/Renewals | Downloads
Behavior: Visits with Search | Customer Satisfaction (by Primary Purpose)
Acquisition: Visitors (Channels) | Visitors (City)

Task Completion by Primary Purpose is my absolute favorite metric for any website (all the ones above). It made most sense here. It is a part of my simple three questions that make the greatest survey questions ever.

A quick best practice.

A much more detailed collection of recommendations I’d written for the Government of Belgium a little while back: Web Analytics Success Measurement For Government Websites.

Stock Photography: Shutterstock

I spend hours looking for inspiration for the stock photos that end up on my LinkedIn Influencer channel posts.

Outcomes: Lifetime Value (Revenue Per User) |  Contributor Signups
Behavior: Cohort Analysis | Top Event (by Category) 
Acquisition: Visitors | Assisted Conversions

For someone as savvy as Shutterstock, Cohort Analysis at the intersection of incredible behavior analysis and optimizing acquisition across media channels.

Movie Studio: The Fate of the Furious

I hear it is Oscar-worthy. : )

Outcomes: Ticket Purchases | Completed Trailers 
Behavior: Unique Page Views | Outbound Clicks (All Access+) 
Acquisition: Visits | CTR (Paid)

One shift in movie sites is that the metrics and strategy have distinct phases, per-release, post-release, off-theaters (DVD, digital sales). You’ll have to have three sets of metrics as outcomes and marketing strategies change.

Mobile Gaming: Jam City 

Raise your hands if you love mobile games!

Outcomes: Downloads (by Store) |  Support Requests
Behavior: Videos Watched | Goal Flow (Source)
Acquisition: Click-Share (Mobile Search) | Visitors (Similar Audiences)

We are only measuring the value the website (mobile and desktop). If we had to measure the Apps itself, there would be an entire new cluster of metrics including 30-day MAUs, Lifetime Value, Sessions/User, so and and so forth.

Automotive Dealer: Nissan Sunnyvale 

Electric cars FTW!

Outcomes: No Brainer Price Requests | Service Appointments 
Behavior: Unique Page Views (Purpose Type) | Sessions With Search 
Acquisition: Visitors | Paid Clicks (by Media)

I have to admit I’m usually pretty torn between tracking online leads (no brainer request in this case) vs. leads via Chat (very prominent on most dealer sites) or Phone (very common). Often Chat and Phone can be more valuable (and numerous) than the online leads.

Food / Beverages: McCormick 

If there is an industry stuck in 1920s, it is the food companies (of all types). Their core value proposition from digital is still recipes – a marketing strategy as old as packed food. And not even interactive digital-first recipes – the same boring presentation and text as you’ll find on the back of the box!

There is so, so, so, so much more that food, beverages and restaurant companies can do. Digital is all pervasive in our lives, food is something we love and adore (and a top five category in content consumption on YouTube!), mobile allows these brands to be ever closer to us… all that’s needed is a pinch of imagination. PopChips and Chobani are two that show imagination with their content strategies, hopefully they inspire others.

Let’s see what we can measure if we had to do it for a great old brand McCormick.

Outcomes: Shopping Lists Created | Reviews Submitted
Behavior: Frequency | Events (Content Type)
Acquisition: Visitors (Referrals) | Clicks (Remarketing)

I came close to using Login Status for behavior, it would provide fascinating insight into the ability of McCormick to create loyalty, even brand evangelists. But, a quick peek at the competitive intelligence data shows that it is seems it is not all that important (barely any people login). If I were at McCormick I would look at the GA data and double-validate that. If it seems to be a big enough number, we can use Login Status as a segmentation strategy.

Tech Support: Dell US

Digital analytics for a tech support site tends to be a lot of fun, primarily because you can directly drive costs down and increase repurchase rate (loyalty) – thus hitting both sides of the balance sheet causing your CFO to give you a thousand kisses. 

Outcomes: Task Completion Rate (split by Primary Purpose, and Direct vs. Community support) | System Updates (Drivers, Diagnostics etc.)
Behavior: Page Views per Visit | Visits to Resolution 
Acquisition: Visits | Search Click-Share

A long, long time ago, when I was but a youth, I had a view on this topic… Measuring Success for a Support Website.

Social | YouTube: Prudential

In case your primary digital existence consists of a YouTube channel (I hope that is not the case, you want to have a solid owned AND rent platform strategies).

Outcomes: Subscribers | Brand Consideration
Behavior: Views (by Content Type) | Conversation Rate 
Acquisition: Views | Sources

I have a detailed primer on comprehensive YouTube success. It has more metrics you can use, if indeed you are a YouTube only existence.

Social | Facebook: Priceline

Priceline is a typical brand, and their page illustrates why an organic strategy is worth almost nothing on Facebook. You can easily validate that statement. Go ahead and click on the link above. As you scroll, you’ll notice that the numbers you see for each post are less than tiny. This applies for all companies, not just Priceline.

Facebook is an important strategy for your company, just let your focus be on a Paid Media strategy and measure success as you would any paid strategy.

But, if like Priceline you continue to have your organic content strategy on Facebook (or Twitter)…

Outcomes: Page Likes | Brand Consideration 
Behavior: Amplification Rate | Conversation Rate 
Acquisition: Visits | Paid Likes

You can do a lot more of course, if Facebook is your only digital outpost (though, again, I hope that is not the case as you need to have an owned and rented platform strategy)…

More here: Facebook Advertising / Marketing: Best Metrics, ROI, Business Value.

There you are. Fifteen completely different types of digital businesses that we can measure immensely effectively, usually uniquely, with the rich collection of data we have in any free/paid digital analytics solution.

I hope that you discovered new valuable metrics that will become KPIs to measure your Acquisition, Behavior and Outcome efforts. But, what I hope you’ll take away more is the application of critical thinking, to be more comfortable operating in ambiguity and bring ruthless focus and prioritization what is most likely to drive big action. You don’t have to get it all right the first time. Implement. Evaluate. Kill/Keep. Improve. Rinse. Repeat.

Carpe diem!

As always, it is your turn now.

If you picked six metrics (two each for A, B, O) for any site above, will you please share them via comments below? Is there a metric above that you particularly love/hate, why? Is there a metric you would use instead of something I used? Is there a type of site you have had a hard time picking metrics that matter the most? You’ve surely noticed some patterns in what I tend to like and don’t (notice, no time metrics above!), will you share your thoughts if you feel there is a sub-optimal bias there?

I look forward to your guidance to improve what I know, fill in gaps in my knowledge and the wisdom you have that I completely overlooked. Please share via comments.

Thank you.

The Very Best Digital Metrics For 15 Different Companies! is a post from: Occam’s Razor by Avinash Kaushik

Source: Avinash

Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

FoldsToday’s post comes from a source of deep pain. Analysis Ninjas are valued less than I would prefer for them to be.

The post is also sourced from a recent edition of my newsletter, The Marketing – Analytics Intersect. I send it once a week, and it contains my insights and recommendations on those two topics. Like this blog, the newsletter is geared towards being instantly actionable (rather than just theory-smart, which is pretty cool too). Do sign up if you want to deliver a small electric shock of simulation to your brain each week.

TMAI #41 covered a graph that resulted from a survey done by Econsultancy and Lynchpin. I received a ton of responses for it, and great discussion ensued. It prompted me to write this post, essentially an expanded version of TMAI #41. I’ve added new insights, recommendations, and two bonus lessons on how to do surveys better and a direct challenge to your company’s current analytics strategy.

If your heart is weak, you can stop reading now. I promise, I won’t mind one bit. I heart you. If you are open to being challenged… then here are the short-stories inside this post…

Let’s go and challenge our collective thinking!

The World Needs Reporting Squirrels. Wait. What!

Some of you know that I created the phrases Reporting Squirrels and Analysis Ninjas to emphasize the difference between those that puke data and those that puke insights with actions attached to them.

Here is my slide the first time I presented the concept in a keynote…

reporting squirrels analysis ninjas

Cute, right? 🙂

While companies, medium and large, often need both roles, I’ve massively pushed for every company to want more Analysis Ninjas and for analysts to have careers where they can rapidly undertake metamorphosis from Reporting Squirrels to Analysis Ninjas (after all the difference in salary is tens of thousands of dollars).

If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis.

With that as context, you can imagine how heart-broken I was when Jane shared the following visual from a study done by Econsultancy and Lynchpin. It contains the answers to the question which analytics skills are most in demand…

econsultancy analytics skills

Checkout the y-axis… what do you see as the common pattern across them all?

Just data puking.

One row after another of data puking skills.


Almost nothing that quite captures the value of Analysis Ninjas! N. O. T. H. I. N. G.

I did a random Google search and got this list of analytical skills:

+ Understanding relationships between numbers

+ Interpreting mathematical information

+ Visual perception of information

+ Ability to organize information

+ Pattern recognition and understanding trends

+ Argumentation and logical thinking

+ Ability to create insightful data visualizations

+ Hypothesis development and design of experimentation

+ Strategic thinking skills

And, that is just a random list!

None of these are in demand?

Look at the list in the graph, what kind of purple squirrel with ant legs and an elephant’s nose that nobody needs is Lynchpin describing?

This did not happen at Econsultancy, but the data did cause introspection at my end.

And, my first question was the one that is also top of mind of all readers of Occam’s Razor… Is the world so dark that the only “analytical” skills that are valued are directly tied to data puking and you should immediately shut down your Analysis Ninja efforts?

Let me share three thoughts for your consideration, then some guidance on how to do surveys right, and end with a call to arms for all of you and the “data people” you choose to work with.

Three thoughts that explain the Econsultancy/Lynchpin graph.

1. The survey design is at fault.

The otherwise well-respected Econsultancy and Lynchpin dropped the ball massively in creating the list of answers for the respondents to choose from.

I have to admit, I believe this is a major flaw (and not just for this question in the entire report). What is disappointing is that they have done this for nine years in a row!

It poses these questions…

How is it that in nine years no one at these organizations realized they were simply asking people to rank data puking answers? Did the survey list the skills Econsultancy and Lynchpin hire for and value in their own analysts?

The graph illustrates data for three years… Was the fact that almost nothing changed in three years in terms of priority not trigger a rethink of the options provided for this question? Anyone reading the report at the two companies creating it should have thrown a red flag up and said hey guys, the respondents keep rating the answers the same, maybe we are not asking the right question or providing the best choices for our respondents to pick.

More on how to avoid this flaw in your surveys, of any kind, below.

2. The survey is targeted to the wrong folks.

They might be the wrong folks to accurately judge what analytical skills and how to appreciate the value of each skill as they rank them. That could explain the results (not the answer choices though).

Econsultancy/Lynchpin provides this description in the report: “There were 960 respondents to our research request, which took the form of a global online survey fielded in May and June 2016. Respondents included both in-house digital professionals and analysts (56%) and supply-side respondents, including agencies, consultants and vendors (44%).”

The survey was 76% from the UK and EU. Respondents were solicited from each company’s database as well as Social Media.

Here is the distribution provided in the report:

econsultancy lynchpin survey audience

On paper it looks like the departments are to be what you would expect. It is difficult to ascribe any blame to the folks who got the survey. There is a chance that there is a UK and EU nuance here, but I don’t think so.

3. It is our fault.

My first instinct in these cases is to look into the mirror.

Perhaps we have not succeeded as much as we should when it comes to show casing the value of true data analysis. Perhaps all the people involved in all digital analytics jobs/initiatives, inside and outside companies, are primarily data pukers, and none of them have skills to teach companies that there is such a thing as data analysis that is better.

Then, you and I, and especially our friends in UK and EU, need to work harder to prove to companies that CDPs (customized data pukes, my name for reporting) do not add much value, the rain of data does not drive much action. You and I need to truly move to the IABI model were we send very little data, and what little we send out is sent with copious amounts of Insights from the data, what Actions leaders need to take, and the computation of the Business Impact.

The more we deliver IABI, by using our copious analytical skills, the more the leaders will start to recognize what real analytical skills are and be able to separate between Reporting Squirrels and Analysis Ninjas.

Bottom-line… I would like to blame the competency at Econsultancy and Lynchpin, especially because I believe that truly, but I must take some responsibility on behalf of the Analysis Ninjas of the world. Perhaps we suck more than we would like to admit. I mean that sincerely.

Bonus #1: Lessons from Econsultancy/Lynchpin Survey Strategy.

There are a small clump of lessons from my practice in collecting qualitative feedback that came to fore in thinking about this particular survey. Let me share those with you, they cover challenges that surely the E+L team faced as they put this initiative together.

If your survey has questions that cease to be relevant, should you ask them again for the sale of consistency as you have done this survey for nine years?

There is a huge amount of pressure for repeated surveys to keep the questions the same because Survey Data Providers love to show time trends – month over month, year over year. It might seem silly that you would keep asking a question when you know it is not relevant, but there is pressure.

This is even worse when it comes to answer choices. Survey Creators love having stability and being able to show how things have changed, and they keep irrelevant/awful/dead answers around.

If you are in this position… You will be brave, you will be a warrior, you will be the lone against-the-tide-swimmer, and you will slay non-value-added stuff ruthlessly. You will burn for from the ashes shall rise glory.

If you are the Big Boss of such an initiative, here is a simple incentive to create, especially for digital-anything surveys: Give your team a standard goal that 30% of the survey questions for any survey repeated each year have to be eliminated and 10% new ones added.

Your permission will 1. force your employees to think hard about what to keep and what to kill (imagine that, thinking!) 2. create a great and fun culture in your analytical (or reporting 🙁 ) team and 3. push them to know of the latest and greatest and include that in the survey.

If I feel I have a collection of terrible choices, do you have a strategy for how I can identify that?

This does not work for all questions of course, but here is one of my favourite choice in cases where the questions relate to organizations, people skills, and other such elements.

Take this as an example…

skills gap question

How do you know that this is a profoundly sub-optimal collection of choices to provide?

For anyone with even the remotest amount of relevant experience, subject matter expertise, it is easy to see these are crazy choices – essentially implying purple squirrels exist. But, how would you know?


Start writing down how many different roles are represented in the list.

That is just what I did…

skills gap question roles test

It turns out there are at least five roles in a normal company that would possess these skills.

So. Is this a good collection of skills to list? Without that relevant information? If you still go ahead and ask this question, what are you patterning your audience to look for/understand?

Oh, and I am still not over that in looking for what analysis skills are missing in the company, no actual analytical skills are listed above! Ok, maybe statistical modeling smells like an analytical skill. But, that’s as close as it gets.

I share this simple strategy, identifying the number of different roles this represents, to help you illuminate you might have a sub-optimal collection of choices.

There are many other strategies like this one for other question. Look for them!

If your survey respondents are not the ideal audience for a question, what’s your job when crafting the survey?

J. K. I.

Just kill it.

If you don’t want to kill it… Personally interview a random sample of 50 people personally (for a 1,000 people survey). Take 10 mins each. Ask primitive basic questions about their job, their actual real work (not job title), and their approximate knowledge. If these 50 pass the sniff test, send the survey. Else, know that your survey stinks. JKI.

I know that I am putting an onerous burden on the survey company, taking to 50 people even for 10 mins comes at a cost. It does. I am empathetic to it. Consider it the cost of not putting smelly stuff out into the world.

If your survey respondents won’t be able to answer a question perfectly, what is a great strategy for crafting questions?

Oh, oh, oh, I love this problem.

It happens all the time. You as the survey creator don’t know what you are taking about, the audience does not quite know what they are talking about, but there is something you both want to know/say.

Here’s the solution: Don’t do drop down answers or radio button answers!

The first couple times you do this, ask open ended questions. What analytical skills do you think you need in your company? Let them type out in their own words what they want.

Then find a relatively smart person with subject matter expertise, give them a good salary and a case of Red Bull, and ask them to categorize.

It will be eye opening.

The results will improve your understanding and now you’ll have a stronger assessment of what you are playing with, and the audience will not feel boxed in by your choices, instead tell you how they see the answers. (Maybe, just maybe be, they’ll give you my list of analytical skills above!)

Then run the survey for a couple years with the choices from above. In year four, go back to the open text strategy. Get new ideas. Get smarter. Rinse. Repeat.

I would like to think I know all the answers in the world. Hubris. I use the strategy above to become knowledgeable about the facts on the ground and then use those facts (on occasion complemented by one or two of my choices) to run the survey. This rule is great for all kinds of surveys, always start with open-text. It is harder. But that is what being a brave warrior of knowledge is all about!

If your survey results cause your senior executives, or random folks on the web, to question them, what is the best response?

The instinct to close in an be defensive, to even counter-attack, is strong.

As I’m sure your mom’s taught you: Resist. Truly listen. Understand the higher order bit. Evolve. Then let your smarter walk do the talking.

Simple. Awfully hard to do. Still. Simple.

Bonus #2: The Askers-Pukers Business Model.

The biggest thing a report like Econsultancy/Lynchpin’s suffers from is that this group of individuals, perhaps even both these companies, see their role in this initiative as Askers-Pukers.

It is defined as: Let us go ask a 960 people we can find amongst our customers and on social media a series of questions, convert that into tables and graphs, and sell it to the world.

Ask questions. Puke data. That is all there is in the report. Download the sample report if you don’t have a paid Econsultancy subscription. If you don’t want to use your email address, use this wonderful service:

Even if you set aside the surveying methodology, the questions framing, the answer choices and all else, there is negative value from anything you get from Askers-Pukers, because the totality of the interpretation of the data is writing in text what the graphs/tables already show or extremely generic text.

Negative value also because you are giving money for the report that is value-deficient, and you are investing time in reading it to try and figure out something valuable . You lose twice.

Instead one would hope that Econsultancy, Lynchpin, the team you interact with from Google, your internal analytics team, any human you interact who has data sees their role as IABI providers ( Insights – Actions – Business Impact).

This is the process IABI providers follow: Ask questions. Analyze it for why the trends in the data exist (Insights). Identify what a company can/should do based on the why (Actions). Then, have the courage, and the analytical chops, to predict how much the impact will be on the company’s business if they do what was recommended.

Insights. Actions. Business Impact.

Perhaps the fatal flaw in my analysis above, my hope above, is that I expected Econsultancy and Lynchpin to be really good at business strategy, industry knowledge, on the ground understanding of patterns with their massive collection of clients. Hence, knowing what actually works. I expected them to be Analysts. Instead, they perhaps limit their skills inside the respective company to be Askers-Pukers.

Both companies are doing extremely well financially, hence I do appreciate that Askers-Pukers model does work.

But for you, and for me, and for anyone else you are paying a single cent for when it comes to data – either data reported from a survey, data reported from your digital analytics tool, data reported from other companies you work with like Facebook or Google or GE – demand IABI. Why. What. How Much. If they don’t have that, you are talking to the wrong people. Press the escape button, don’t press the submit order button.

[Isn’t it ironic. Econsultancy and Lynchpin did exactly what their survey has shown for nine years is not working for companies in the UK: Reporting. The outcome for both of them is exactly the same as the outcome for the companies: Nothing valuable. This is explicitly demonstrated by their full report.]


I hope you see that this one survey is not the point. E + L are not the point. What their work in this specific example (and you should check other examples if you pay either company money) illuminates is a common problem that is stifling our efforts in the analytics business .

This applies to E+L but it applies even more to your internal analytics team, it applies extremely to the consultants you hire, it applies to anyone you are giving a single cent to when it comes to data.

Don’t hire Askers-Pukers. Don’t repeat things for years without constantly stress-testing for reality. Don’t make compromises when you do surveys or mine Adobe for data. Don’t create pretty charts without seeing, really looking with your eyes, what is on the chart and thinking about what it really represents.

Applied to your own job inside any company, using Google Analytics or Adobe or iPerceptions or Compete or any other tool… don’t be an Asker-Puker yourself. Be an IABI provider. That is where the money is. That is where the love is. That is where the glory is.

Carpe Diem!

As always, it is your turn now.

Is your company hiring Reporting Squirrels or Analysis Ninjas? Why? Is the work you are doing at your company/agency/consulting entity/survey data provider, truly Analysis Ninja work? If not, why is it that it remains an Asker-Puker role? Are there skills you’ve developed in your career to shift to the person whose business is why, what, how much ? Lastly, when you do surveys, of the type above or others, are there favourite strategies you deploy to get a stronger signal rather than just strong noise?

Please share your life lessons from the front lines, critique, praise, fun-facts and valuable guidance for me and other readers via comments.

Thank you. Merci. Arigato.

PS: I hope this post illuminates the valuable content The Marketing – Analytics Intersect shares each week, sign up here .

Smarter Survey Results and Impact: Abandon the Asker-Puker Model! is a post from: Occam’s Razor by Avinash Kaushik

Source: Avinash

Be Real-World Smart: A Beginner's Advanced Google Analytics Guide

NectarBeing book smart is good. The outcome of book smart is rarely better for analytics practitioners then folks trying to learn how to fly an airplane from how-to books.

Hence, I have been obsessed with encouraging you to get actual data to learn from. This is all the way from Aug 2009: Web Analytics Career Advice: Play In The Real World! Or a subsequent post about how to build a successful career: Web Analytics Career Guide: From Zero To Hero In Five Steps. Or compressing my experience into custom reports and advanced segments I’ve shared.

The problem for many new or experienced analysts has been that they either don’t have access to any dataset (newbies) or the data they have access to is finite or from an incomplete or incorrect implementation (experienced). For our Market Motive Analytics training course, we provide students with access to one ecommerce and one non-ecommerce site because they simply can’t learn well enough from my magnificent videos. The problem of course is that not everyone is enrolling our course! 🙂

All this context is the reason that I am really, really excited the team at Google has decided to make a real-world dataset available to everyone on planet Earth (and to all intelligent life forms in the universe that would like to learn digital analytics).

The data belongs to the Google Merchandise Store, where incredibly people buy Google branded stuff for large sums of money (average order value: $115.67, eat your heart out Amazon!). And, happily, it has almost all of the Google Analytics features implemented correctly. This gives Earth’s residents almost all the reports we would like to look at, and hence do almost all the analysis you might want to do in your quest to become an Analysis Ninja. (Deepak, would you kindly add Goal Values for the Goals. Merci!) You’ll also be able to create your own custom reports, advanced segments, filters, share with the world everything you create, and all kinds of fun stuff.

For consultants and opinion makers you no longer have to accept any baloney peddled to you about what analytics tool is the best or better fit for your company/client. Just get access to this data and play with the actual GA account along with Adobe and IBM and WebTrends et. al. and suddenly your voices/words will have 10x more confidence informed by real-world usage. No NDA’s to sign, no software to install, no IT resources required. Awesome, right?

In this post I’ll highlight some of my favourite things you can do, and learn from, in the Store dataset. Along the way I’ll share some of my favourite metrics and analytics best practices that should accelerate your path to becoming a true Analysis Ninja. I’ve broken the post into these sections:

I’m sure you are as excited as I am to just get going. Let’s go!

How to get Store Dataset Access?

It is brilliantly easy.

Go to the Analytics Help Demo Account page. Read the bit in the gray box titled Important. Digest it.

Then click on this text: ––>ACCESS DEMO ACCOUNT<––

Looks scary in the all caps, right? That is just how the Google Analytics team rolls. 🙂

You’ll see a tab open, urls will flip around, in two seconds you’ll see something like this on your Accounts page…

google analytics accounts view

Click on 1 Master View and you are in business.

If you ever want to remove access to this real-world data, just go back to the page above and follow the five simple steps to self-remove access.

Jump-Start Your Learning.

You can start with all the standard reports, but perhaps the fastest way for you to start exploring the best features is to download some of the wonderful solutions in the Google Analytics Solutions Gallery.

You’ll find my Occam’s Razor Awesomeness bundle there as well.

It is a collection of advanced segments, custom reports, and dashboards. You’ll have lots of features incorporated in them. You can customize them to suit your needs, or as you learn more, but you won’t have to start with a blank slate.

You can also search for other stuff, like custom reports or attribution models.

Another tip. If you are a complete newbie (welcome to our world!), you probably want to start your journey by reading about each type of report, and then looking at the Overview report in each section in Google Analytics. At this point you’ll be a little confused about some metric or the other. That’s ok. Go, read one of the best pages in the Analytics help center: Understanding Dimensions and Metrics. Go back into GA, you’ll understand a whole lot more.

This is a beginner’s advanced guide, so I’m going to do something different. Through my favourite reports, often hard to find in your company’s GA dataset, I’m going to push you beyond other beginner’s guides. I’ll also highlight frameworks, metrics, custom reports, and other elements I feel most Analyst’s don’t poke around enough.

1. Play with Enhanced Ecommerce Reports.

It is a source of great sadness for me that every single site is not taking advantage of Enhanced Ecommerce tracking and analysis . It is a complete rethink of ecommerce analysis. The kind of reports and metrics you’ll get straight out of the box are really amazing.

Go to the Reporting section of our Store Demo account, click on Conversions in the left nav, then Ecommerce, and now Overview. You’ll see in an instant the very cool things you can track and analyze…

ecommerce overview

With a little bit of smart tagging you can track your internal promotions (buy one Make America Great Again hat and get one Stronger Together hat free!), transactions with coupon codes, affiliate sales and more. Very nicely summarized above.

Next go to the report with new things that will help you drive smarter merchandizing on your mobile and desktop websites. Go to Shopping Analysis and click on Shopping Behavior…

shopping behavior analysis google analytics

I adore this report.

Most of the time when we do funnel analysis we start at the Cart stage (third bar above). We rarely hold people responsible for Traffic Acquisition accountable, we rarely hold people responsible for Site Design and Merchandizing accountable. The former are promoted on silly metrics like Visits or Visitors or (worse) Clicks. The latter are promoted based on silly metrics like PageViews.

The first bar to the second shows the number of visits during which people went from general pages on your website to product pages (places were there is stuff to be sold, add to cart buttons). A lame 26%. See what I mean. Insightful. How are you going to make money if 74% of the visits don’t even see a product page!

The second bar the third is even more heart-breaking, as if that were possible. Of the sessions with pages with product views, how many added something to cart. A lousy 17%. One. Seven. Percent! On a site were you can do nothing except buy things.

See what I mean? Question time for your Acquisition, Design and Merchandizing team.

Do you know answers like these for your website? That is why you need Enhanced Ecommerce.

I won’t cover the last two bars, most of you are likely over indexing on funnel analysis.

Practice segmentation while you are here. Click on + Add Segment on top of this report, choose Google (or whatever interests you)…

google traffic segment

And you can analyze acquisition performance with a unique lens (remember you can’t segment the funnel that exists in the old ecommerce reports which is still in your GA account!)…

shopping behavior analysis google traffic

A little better. Still. You spend money on SEO and PPC. It should be a lot better than this. If this were your data, start with questioning your PPC landing page strategy and then move to looking at your top SEO landing pages, and then look at bounce rates and next page analysis for those that stay.

I can honestly spend hours on just this report digging using segmentation (geo, media, new and loyal customers, all kinds of traffic, product page types and so on). It has been a great way to immediately influence revenue for my ecommerce engagements.

While you are here, you can play and learn to use the new funnel report… it is called Checkout Behavior Analysis…

checkout behavior analysis google analytics

Much simpler, so much easier to understand.

You can also, FINALLY, segment this report as well. Try it when you are in the Store demo account.

Take a break. A couple days later come back and checkout the new Product Performance and Product List Performance reports. The latter is particularly useful as an aggregated view for senior executives. In case of the Store data, the first report has 500 rows of data, the second just 45. Nice.

I wanted to flag three metrics to look at in the Product Performance report.

Product Refund Amount is $0.00 in this dataset, but for your company this is a great way to track refunds you might have issued and track were more of that is happening.

I love Cart-To-Detail Rate (product adds divided by views of product details) and Buy-to-Detail Rate (unique purchases divided by views of product-detail pages). Remember I was so upset above about the poor merchandizing. Using the sorting option on these two columns I identify where the problem is worse and where I can learn lessons from. Very cool, try it.

I could keep going on about more lovely things you’ll find in the Enhanced Ecommerce reports, but let me stop here and have you bump into those cool things as, and I can say this now, you have access to this data as well!

Bonus: If you are a newbie, in your interview you’ll be expected to know a lot about Goals (I call the micro-outcomes). Explore that section. Look the Overview, Goal URLs and Smart Goals. Ignore the eminently useless Reverse Goal Path report (I don’t even know why this is still in GA after years of uselessness) and Funnel Visualization (almost totally useless in context of almost all Goals).

2. Gain Attribution Modeling Savvy.

My profound disdain for last-click reporting/analysis is well known. If you are using last-click anything, you want your company to make bad decisions. See. Strong feelings.

Yet, many don’t have access to a well set-up account to build attribution modeling savvy and take their company’s analytics the year 2013. Now, you can!

I am big believer in evolution (hence my marketing and analytics ladders of awesomeness). Hence, start by looking at the Assisted Conversions report (Conversions > Multi-Channel Funnels)…

assisted conversions google analytics

Then metric you want to get your company used to first, to get them ready for savvier attribution anything, is the metric Assisted Conversions. The last column.

Here’s the official definition: A value close to 0 indicates that this channel functioned primarily as the final conversion interaction. A value close to 1 indicates that this channel functioned equally in an assist role and as the final conversion interaction. The more this value exceeds 1, the more this channel functioned in an assist role .

Now scroll just a bit back up, stare at that column, what would your strategy be for Organic Search if it is at 0.46? What about Display advertising driving which plays primarily an “upper funnel” introducing your brand to prospects 1.58?

The change required based on this data is not just your marketing portfolio re-allocation, that is almost trivial, what’ bigger, huger, crazy-harder is changing how your company thinks. It is painful. Largely because it quickly becomes about how people’s budgets/egos/bonuses. But, hundreds of conversions are on the line as well on insights you’ll get from this data. Learn how to use this metric to drive those two changes: marketing portfolio – people thinking.

Couple bonus learnings on this report.

On top of the table you’ll see text called Primary Dimension. In that row click on Source/Medium. This is such a simple step, yet brings you next layer of actionable insights so quickly. You’ll see some surprises there.

Second, look at the top of the report, you’ll see a graph. On to top right of the graph you’ll see three buttons, click on the one called Days before Conversion…

assisted conversions days before conversion

I love this report because it helps me understand the distribution of purchase behavior much better. I profoundly dislike averages, they hide insights. This report is the only place you can see distribution of days to purchase for Assisted Conversions.

If you’ve changed the think in your company with Assisted Conversions… You are ready for the thing that gets a lot of press… Attribution Modeling!

You’ll find the report here: Conversions > Attribution > Model Comparison.

You’ll see text called Select Model next to Last Interaction. Click on the drop down, ignore all the other models, they are all value deficient, click on the only one with decent-enough value, Time Decay, this is what you’ll see…

attribution modeling last click vs time decay

Half of you reading this post are wondering why I don’t like your bff First-Interaction (it is likely the worst one on the list btw) or your bff Linear (the laziest one on the list)… worry not, checkout this post: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models .

The column you are of course looking at is % Change in Conversions. The GA team is also helping you out by helping you understand where the results are significant, green and red arrows, and where it is directional, up or down gray arrows.

This is the data you’ll use to drive discussions about a change in your marketing $$$ allocations.

Where you have CPA, it is is an even more valuable signal. And, such a blessing that the Store demo account has that data for you.

You’ll need all your brain power to understand the report above (make sure you read the models post above), and then some more to drive the change in how your company thinks. Attribution model is not a software or math problem, it is an entrenched human minds problem.

And because I’m the author of the quote all data in aggregate is crap I recommend scrolling up a bit in the attribution modeling report and clicking on the down arrow under the word Conversion….

attribution modeling goals analysis

This is admittedly an advanced thing to learn because even understanding marketing dollars plus user behavior overall is hard, this just makes it a bit more complicated because you can actually understand those two things for every goal you have individually or just ecommerce all by itself.

It is incredibly awesome to be able to do that because now you are this super-data-intelligent-genius that can move every variable in a complex regression equation very finely to have max impact on your company.

If you can master this, and IF you can evolve how your company does marketing portfolio allocation and how it thinks, then you are ready for the max you can do in Google Analytics when it comes to attribution… custom attribution modeling.

On top of the table, click on Select Model, then Create New Custom Model.

To get you going, here’s one of my models for a client…

custom attribution model

Custom attribution models are called custom because they are custom to every company. It requires an understanding for everything I’ve requested you to do above, business priorities (what the business values), and business strategy.

Creating a couple different custom attribution models, seeing how it affects the data, what decisions GA recommends, helps you have an intelligent argument with all your stake holders. Again, the decisions from this analysis will flow into changes to your marketing portfolio and how people in your company think.

Once you get into custom attribution modeling, and you spend serious amount of money on marketing online (a few million dollars at least), you are ready for the thing that actually will drive the best changes: Controlled Experiments (aka media mix modeling). Hence, it is critical that you approach your learnings in the precise steps above, don’t jump steps if at one of them you have not changed how your company thinks.

Bonus 1: You might think the above is plenty advanced. It is not. For the higher order bits, when you are all grown up, read this post and internalize the implications of it: Multi-Channel Attribution: Definitions, Models and a Reality Check

Bonus 2: The Time Lag and Path Length reports in your Multi-Channel Funnels folder are extremely worth learning about. I like Path Length more, more insightful. When you analyze the data, be sure to play with the options under Conversion, Type (click AdWords), Interaction Type and Lookback Window. With each step absorb the patterns that’ll emerge in the data. Priceless.

3. Learn Event Tracking’s Immense Value

I’m very fond of Event Tracking for one simple reason. You have to create it from scratch. When you open GA, there is no data in these reports. It can only get there if you spend time trying to understand what’s important to the business (Digital Marketing and Measurement Model FTW!), what is really worth tracking, and then through intelligent thought implementing the tracking.

I love the fact that you have to literally create data from scratch. For any beginner who is trying to get to advanced, Event Tracking will teach you a lot not just about Event Tracking but creating smart data.

Lucky for us the GA team has created some data for us to play with. Go to Behavior in the left nav, then Events, and then Top Events… This is what you’ll see…

event tracking top events

The Store team is capturing four events, you can drill down into any one of them to get a deeper peek into user behavior.

I choose Contact Us to analyze the Event Labels, I get all these strategies that people…

event tracking event lables

It would be valuable if the Event Value had been populated, which would also give us Avg. Value in the table above. Still. Understand that data, how it is collected, what it implies about user behavior is incredibly valuable.

You can also create an advanced segment for any of the events above, example Email. Then, you can apply that segment to any of other reports in Google Analytics and really get deep insights. What cities originate people who call is on the phone? What sites did they come from? How many visits have they made to the site before calling? So on and so forth.

The event tracking reports have three options on top of the report. Event, Site Usage, Ecommerce.

Try the Ecommerce tab…

event tracking ecommerce drilldown

While we did not see any event values, you can tie the sessions where the events were fired with outcomes on the site. Really useful in so many cases where you invest in special content, rich media, interactive elements, outbound links, merchandizing strategies etc. This report, in those cases, will have data you need to make smarter decisions faster.

Bonus: While you are in the Behavior section of Analytics, familiarize yourself with the Site Speed report. Start with the scorecard in the overview report. Move on to Page Timings to find the pages that might be having issues. One cool and helpful visual is Map Overlap, click the link on top of the graph on the Page Timings report. Close with the Speed Suggestions report. Your IT team needs this data for getting things fixed. Your SEO team can do the begging, if required. 🙂

4. Obsess, Absolutely Obsess, About Content

It is a source of intense distress for me that there’s an extraordinary obsession about traffic acquisition (PPC! Affiliates! Cheat Sheet for Video Ads!), and there is huge obsession with outcomes (Conversion Rate! Revenue!), there is such little attention paid to the thing that sits in the middle of those two things: Content!!

Very few people deeply look at content. Yes, there will be a top pages report or top landing pages report. But, that is barely scratching the surface.

Look. If you suck at content, the greatest acquisition strategy will deliver no outcomes.

Obsess about content dimensions and content metrics.

Since you know some of the normal reports already, let me share with you a report that works on many sites (sadly not all), that not many of you are using.

The Content Drilldown report uses the natural folder structure you are using on your website (if you are) and then aggregates content on those folders to show you performance. Here is what you’ll see in the Store demo account you are using…

content drilldown report google analytics

Nice, right? You are pretty much seeing all of the content consumption behavior in the top ten rows!

A pause though. This report is sub-optimally constructed. It shows Pageviews (good), Unique Pageviews (great) and then three metrics that don’t quite work as well: Average Time on Page, Bounce Rate, % Exit (worst metric in GA btw if anyone asks in an interview)…

content drilldown report google analytics 2

At a folder level these really help provide any decent insights, and might not even make any sense. Think about it. Bounce Rate for a folder?

Good time for you to learn simple custom reporting.

On top of the report, right under the report title, you’ll see a button called Customize. Press it. Choose more optimal metrics, and in a few seconds you’ll have a report that you like.

This is the one I created for my use with valu-added content metrics that work better: Average Session Duration, Cart-to-Detail Rate (as it is an ecommerce site) and Page Value (to capture both ecom and goal values at a page level)…

content drilldown custom report google analytics

Much better, right? Would you choose a different metric? Please share it via comments below.

Ok. Unpause.

Even a quick eyeballing of the report above already raises great questions related to overall content consumption (Unique Pageviews), merchandizing (Cart-to-Detail Rate) and of course money.

You can now easily drill-down to other more valuable bits of content and user experience.

I click on the first one, most content consumption, to reveal the next level of detail. I can see that Apparel is the biggest cluster of content, with pretty decent Cart-to-Detail Rate…

content drilldown 2 custom report google analytics

Depending on the business priorities I can ask questions like how come the summer olympic games stuff no one seems to want (and we spent $140 mil on an Olympics sponsorship, kidding).

At the moment the company has a huge investment in Google Maps branding, so we can look at how various brands are doing… YouTube FTW!

content drilldown 3 custom report google analytics

Maps is not doing so well. You can see how this data might make you curious if this list is what your business strategy is expecting will happen? Or, is this how we prioritize content creation? I mean, Go! People are interested in something esoteric like Go (programming language in case you are curious) rather than Nest! What a surprise.

That is what this type of content analysis is so good at.

You can continue to follow the rabbit hole by the way and get down to the individual pages in any folder, like so…

content drilldown 4 custom report google analytics

Ten percent Cart-to-Detail Rate is pretty poor, compared to some of the others above. Time to rethink if we should even be selling this combo! If not that, definitely time to look at the page and rethink copy, images, design, and other elements to improve this key metric.

The above custom report is really easy to create, for Subscribers of my newsletter I’ll also email a downloadable link for this and other custom reports below.

Bonus: Most people stop at what the reports show in the default view. The GA team does a great job of adding good think and express it all over the standard reports. For example, in context of our discussion here, try the Content Grouping primary dimension. Here you see what happens to the report when I switch to Brands (Content Group)…

all pages report content groupings

Even more useful than what was there before, right? So, how does GA get this data? As in the case of Event Tracking above, the Analyst and business decision making combination are thoughtfully manufacturing data. In this case using the immensely valuable Content Groupings feature. Invest in learning how to use it in the Store demo account, learn how to create content groupings to manufacture useful data. When you interview for higher level Analytics role, or for a first time Analyst role, you’ll stand out in the interview because this is hard and requires a lot of business savvy (ironic right, you stand out because of your business savvy in a Data Analyst interview!).

5. SEO & PPC, Because You Should!

Ok, you’ve waited long enough, time to talk about the thing you likely spend a ton of time on: Acquisition.

Since you likely already know how to report Traffic Source and how to find the Referring URLs and Sessions and… all the normal stuff. Let me focus on two things that are a bit more advanced, and will encourage you to learn things most people likely ignoring.

The first one I want you to immerse yourself in when you are in the Store data is Search Engine Optimization. You know that this is hard because when you go to Acquisition > Campaigns (what!) > Organic Keywords you will see that 95% are labeled “(not provided)”. This report is completely useless.

You do have other options to analyze SEO performance. Here’s the advanced, advanced, lesson: Search: Not Provided: What Remains, Keyword Data Options, the Future.

But, you also have some ability in Google Analytics itself to do keyword level analysis for Google’s organic search traffic. Go to Acquisition > Search Console > Queries. This report shows you the top thousands of keywords (4,974 precisely today in the Store report today). The data is available because the team has configured the Search Console data to connect with GA.

Here’s what you’ll see…

organic search queries report google analytics

I sort the data by Clicks, because Impressions is a lot less valuable, and with Clicks I get something closer to Sessions (though they are very different metrics). I immediately value CTR as a metric in this context, you can see the variations above. This is perfect immediate data for SEO discussions.

Average Position is also interesting, perhaps more so for my peers in the SEO team. As a Business Analyst I value Average Position a lot less in a world of hyper-personalized search.

My next data analysis step is to take this data out of GA (click Export on top of the report) and play with it to find macro patterns in the data. I’ll start with something simple as creating tag clouds, using Clicks or CTR as contextual metrics. I’ll classify each keyword by intent or other clusters to look for insights.

Try these strategies, can you find weaknesses in the Google Store’s SEO strategy? How do your insights compare to what you just discovered in the content analysis in terms of what site visitors actually want? Really valuable stuff.

What you cannot do with this data is tie it to the rest of the data in GA for these visitors. You cannot get conversions for example, or Page Depth etc. This is heart-breaking. But, see the not provided post I’ve linked to above for more strategies and meanwhile you can do some cool things in Google Analytics when it comes to SEO.

Bonus: In the Search Console reports, I also find the Landing Pages report is also helpful because you can flip the center of universe, for the same metrics as above, to landing pages rather than keywords. The insights you get will be helpful for your SEO team but more than that it will be critical for your site content team.

A quick note on the above… for the current data you’ll see the Landing Pages report looks a little weird with no data in the Behavior and Conversion columns. Something weird is going on, on my other accounts there is data. The team can fix this in the very near future.

Next, spend a lot of time in the AdWords section.

Both because Paid Search if often a very important part of any company’s acquisition strategy, and because at the moment there are few digital acquisition channels as sophisticated and complex as AdWords. When you are getting ready for your interviews, being good at this, really good, is a great way to blow your interviewer away because most people will know only superficial stuff about AdWords.

As if those reasons were not enough, in Google Analytics AdWords is a great place to get used to the complexity that naturally arises from mixing two data sources. In almost all GA AdWords reports the first cluster of data (pink below) will come from AdWords and the second cluster (green brace) is the normal collection of metrics you see in GA…

adwords plus google analyitcs

This will naturally prod you into trying to understand why are Clicks different from Sessions? After-all it is a click that kicks off a session in GA when the person arrives. It is internalizing these subtle nuances that separate a Reporting Squirrel from an Analysis Ninja.

Above view is from the Campaigns report. I usually start there as it gives me great insights into the overall PPC strategy for the company.

While you are learning from this report, here’s a little smart tip… Click on the Clicks link on top of the graph you see (you’ll see it along with Summary, Site Usage, Goal Set 1, and Ecommerce), you’ll get a different set of metrics you should know intimately as well…

camapign clicks deeper outcomes

The combination of CPC and RPC is very important. It is nice that they are right next to each other in this view.

When you look at Store data I also want you to live-see why ROAS not even remotely a useful metric. It looks alluring. Return On Ad Spend. That sounds so awesome, surely it is in some holy books! No. It is not.

For now, invest in understanding what is is measuring, what the data shows, is that good or bad, and what’s missing. When you already to move to advanced-advanced stage, read this post: Excellent Analytics Tip #24: Obsess About Real Business Profitability

Once I’ve exhausted the value in Campaign reports (drilling-up, drilling-down, drilling-around), it is time to shift into detail. While it might seem that the very next step will be the AdWords Keyword report, it is not. I like going to the Search Query report first.

In AdWords context, Keyword is what you buy from Google. Search Query on the other hand is what people are actually typing into Google when your search ad shows up (triggered by the Keyword of course).

Here are the two reports from the Store account, you can clearly see why I like starting with the Search Query report….

search keywords vs search queires reports

I would much rather learn to anchor on what people are typing and then go into the Keyword view to see what I can learn there. The Search Query performance report helps me re-think my AdGroups, Match Types, bidding strategies and more. It also helps me optimize the landing pages, both from a content they contain and what ads I recommend send traffic there.

You could spend three months in these reports just learning and finessing your PPS savvy, so I’ll leave you to that. 🙂

Bonus: Shopping Campaigns are incredibly successful for most ecommerce properties. Spend time in that report in the AdWords section, drilling-down and segmenting, to learn what makes these campaigns distinct and if you were tasked to identify insights how would you go about it.

6. Develop a Smarter Understanding of Your Audiences

Having grown up on cookies, we have typically have had a finite understanding of our audiences. This has slowly changed over time, most recently with the awesomeness of User-ID override empowering us to understand a person. Still, most of the time we are not great at digging into Audiences, and their associated behavior.

Hence, to assist with your evolution from beginner to advanced, three often hidden areas of Google Analytics for you to explore now that you have access to real data.

Go to Audience > Interests > In-Market Segments.

Here’s the official definition of what you are looking at: Users in these segments are more likely to be ready to purchase products or services in the specified category. These are users lower in the purchase funnel, near the end of the process.

I’ve developed an appreciation of this report as I think of my performance marketing strategies, especially the ones tied to Display advertising. Far too often we rely on just PPC or email and don’t use Display in all of the clever ways possible. This repor, leveraging insights from my users, help me understand how to do smarter Display.

in-market segements google analytics

You can drill down to Age by clicking on the in-market segment you are interested in, and from there for each Age group you can drill-down to gender.

Per normal your goal is to identify the most valuable ones using micro and macro-outcomes for your business.

After I’ve mastered in-market segments by adding near term revenue to my company and helping shift the thinking about Display in my company, I move to leverage the data in the Affinity Categories. Also a report in this section. Affinity categories are great for any display or video advertising strategies you have to build audiences around See Intent (See – Think – Do – Care Business Framework). A bit more advanced from a marketing perspective (you would have had to master strategy #2, attribution, above).

For the second hidden area, go to Audience > User Explorer.

This lovely beast shows something you think you are dying to see. It is also something I really don’t want you to obsess about (except if you are a tech support representative). But you want it. So. Here it is…

user explorer report google analytics

What you are looking at is a report that shows you the behavior of an individual user on your website, as identified by an anonymous Client-ID. You can loosely think of it as a person, though it is more complicated that. If you have implemented User-ID override (congratulations, you deserve a gold star!), then you areas close to a person as you’ll ever be.

Because this is everyone on your website, there is no wrong place to start and a hundred thousand terrible places to waste time. You can literally watch each person! See, what I mean when I say I don’t want you to get obsessed about this?

On the rarest of rare occasions I look at this report, my strategy is to understand the behavior of “Whales”, people who spend loads of money on our website (why!). I sort the above report by Revenue, and then look over the users who form the first few rows. The data, fi you do it in the Store account for the person who’s at the top at the moment, looks like this…

user explorer report google analytics detail

The report is sorted from the last hit (08:16 above) to the first hit (which you don’t see above, the person browsed a lot!). You can quite literally watch the behavior, over just five minutes, that lead to an order of $2,211.38! You surely want to know what this person purchased (Men’s Cotton Shifts FTW!), what pages did they see, where did they come from, how did they go back and forth (this person did) and so on and so forth.

Looking at the top few of these Whales might help know something about a product merchandizing strategy, a unique source, or how to change your influence with your acquisition strategy to get a few more of these people. There will always only be handful of folks.

The higher order bit is that the best analytical strategy is to analyze micro-segments rather than individuals. Small groups with shared attributes. You can action these, at scale. Nothing in your marketing, site content delivery, servicing at the moment has the capacity to react to an individual’s behavior in real time. And if you can, you don’t have enough visitors. Hence, obsess about micro-segments. That is a profitable strategy.

The spirit above is also the reason why I don’t mention real-time reporting in this guide. Simply not worth it. (For more, see #4: A Big Data Imperative: Driving Big Action)

For the third hidden area, ok, not so hidden but to expose all your analytical talent, go to Audience > Mobile > Devices.

With greater than 50% of your site traffic coming from mobile platforms, this audience report obviously deserves a lot of attention (in addition to segmenting every single report for Mobile, Desktop, Tablet).

The problem is that the report actually looks like this…

mobile analytics

It is poorly constructed with repetitive metrics, and an under-appreciation for mobile user behavior (why the emphasis on Do outcomes when Mobile has primarily a See-Think intent clusters?). It makes for poor decision making.

So. Time to practice your custom reporting skills. (Oh, if you as an Analyst only use custom reports, you are closer to being an Analysis Ninja.)

Scroll back to the top of the Mobile Devices report and click on the Customize button. On the subsequent page, pick the metrics you best feel will give you insights into Acquisition, Behavior and Outcomes. While you are at it, you’ll see just one dimension in this report, Mobile Device Info, you can add other drill-down dimensions you might find to be of value. I added Screen Resolution (matters so much) and then Page (to analyze each Page’s performance by resolution).

Here’s what the report’s Summary view looks like for me…

smart mobile analytics

Nice, right? Smarter, tighter, more powerful.

My obsession is with people on mobile devices and not just the visits. Hence Users come first. Then, paying homage to See and Think intent, my focus is on Pages/Session. For the same reason, my choice for success is goals and Per Session Value (ideally I would use Per Session Goal Value, but as you saw in the opening this account does not have Goal Values). I would delete the Revenue, it is there mostly in case your boss harassed you. Delete it later.

Depending on the role, Acquisition, Behavior or Outcomes, I have everything I need to start my mobile analysis journey.

As I recommended with AdWords analysis above, the tabs on top of the report hold more analytical insights for you…

smart mobile analytics site usage

You will discover that you’ll have to go and practice your custom reporting skills on all these tabs as there are sub-optimal elements on all three of them. For example with Site Usage, I added Think intent metrics. For Goals and Ecommerce tabs there are fewer and more focused metrics. Now almost all of the stuff I need to make smarter decisions from my mobile data is in one place.

This exercise requires a lot of introspection and understanding business needs as well as what analysis makes sense. That is how we all move from Reporting Squirrels to Analysis Ninjas! 🙂

As with the above custom report, I’ll email a downloadable link to the Subscribers of my newsletter The Marketing – analytics Intersect. You can contrast your choices with my choice of metrics and dimensions.

Bonus: If you present screenshots from GA to your management team, make sure you take advantage of the option to show two BFF trends. In my case above you can see I choose to pair mobile Sessions with Goal Completions (again to put the stress on See – Think intent).

7. Icing on the Cake: Benchmarking!

One final beginner’s advanced recommendation.

You just finished looking at a whole bunch of mobile metrics. How do you know if the performance of the Google Merchandizing Store is good or bad? Yes, you do see trends of past performance. But, how about with others in your industry? Others who have your type and size of website?

I’ve convinced that most of the time without that competitive / ecosystem context, Analysis Ninjas are making incomplete decisions.

The cool thing is, you can get benchmarking data in Google Analytics.

Audience > Benchmarking > Devices.

And now you have a really strong sense for what is good performance and what is non-good performance…

benchmarketing report device category

You might have come to one set of conclusions doing the analysis in the mobile section above, and I suspect that now you have very different priorities with the lens pulled back to how the ecosystem is doing.

And, that’s the beauty.

There’s a lot more you can do with benchmarking. You can explore the advanced-advanced version here when you are ready: Benchmarking Performance: Your Options, Dos, Don’ts and To-Die-Fors!

I hope you have fun.

That is it. A beginner’s advanced guide that hopefully accelerates your journey to become an Analysis Ninja.

As always, it is your turn now.

Have already gotten access to the Store demo account? What elements recommended above had you not explored yet? Which ones do you find most easy/frustrating to get actionable insights from? Are there strategies that you use as an Analysis Ninja that are not covered above?

Please share your recommendations, frustrations, :), joyous strategies and guidance with all of us via comments below.

Thank you.

Be Real-World Smart: A Beginner’s Advanced Google Analytics Guide is a post from: Occam’s Razor by Avinash Kaushik

Source: Avinash

Ad Block Tracking With Google Analytics: Code, Metrics, Reports

PartialYou don’t use an ad blocker, right? Of course not! You would never want to take away the opportunity a content creator has online to monetize their work via ads.

I know that at least some of you think I’m being sarcastic. I am not, and this post is all about getting the data to show you that I am indeed not being sarcastic.

I am insanely excited that we can track ad blocking behavior in Google Analytics, so easily. This post covers these key elements:

Here’s how this post unfolds…

While you could call on your favorite IT BFF to do this for you, let me encourage you by saying that if I can do this all by myself…. You can do it too! Honestly, it is that easy.

Excited? Let’s go!

1. Ad block: #wth

The reason you might think I was being sarcastic above is that there is such venom in the media (of course the media!) about people who use ad blockers, and an incredible amount of hoopla around how the only reason media is dying is the awful people using ad blockers in their web browsers.

The reality is not quite that cut and dry.

First, plant me firmly in the column of people who believe that using an ad blocker is a personal choice, each person makes the moral decision they are most comfortable with. Second, I believe that the let me make cheap money by spraying and praying some of the most awful intelligent-deficient ads in large numbers is a contributing factors to users wanting to use ad blockers. Third, the profound lack of empathy for the user experience, especially on mobile, is another huge contributing factor.

If you are getting the feeling that I’m holding publishers, large and medium companies with large people, platforms and budgets to do more in this debate, you would be right. I am not excusing the users (see above, and more below).

Let me make this real for you, by looking at two specific examples.

Here’s a tweet by my wonderful friend Mitch Joel.

mitch joel adblocking tweet

Can you blame him for wanting to install an ad blocker?

And, now, whose fault is it? Both the non-intelligent advertiser and the non-intelligent publisher. Neither wants to grow up and consider using the data available for Mitch to be smarter about advertising. Both simply want cheap let me not work all that hard for it money.

Take thi second example from Forbes magazine on mobile… They are absolutely well within their rights to create a firewall around their site for people who use ad blockers…

Forbes Ad-Light

But think about their text in green for a moment.

On a mobile platform should Forbes not have pity on its users and load the unwanted ads as fast as it can? On a mobile device, which we use more often in deeply private (beds) and openly public (subway) situations should they not auto-play videos?

They should. They can block their site, they can ask everyone to pay for content, they can ask people to pay for no-ads. It is their right. But, the above is close to a ransom note from a corporation to exhibit basic human decency.

In all the discussions for inherent awfulness of users of ad blocking solutions, this perspective never gets as much play. After all the people with the ink are the ones with the non-intelligent cheap money seeking existences.

I hope in the discussion of ad blocking, this gets some play. Relevance in advertising is possible, it is even exciting. Consider the See-Think-Do-Care business framework, discern the intent of your audience and ensure that you ad content, ad targeting and ad landing page are aligned with your audience’s intent. You will be rich. They will be happier.

Speaking for me. I simply pay to get rid of advertising wherever I find it is awful (including at my employer, or powered by my employer’s platforms). I love YouTube Red (and get Google Play Music for free!!). I’m happy to pay for Google Contributor. I’m curious to see the evolution of Optimal, a startup working on a similar solution. I actively control my Google Ads (sadly other platforms are not this kind), I spend a lot of time on The New York Times and The New Yorker, pay for both. And, of course I would not be as smart about digital marketing as I am without paying for Baekdal Plus.

You see different models people use to make money there. You can make money with content. You just need to create something incredible of value and want to make intelligent money.

Conversely if your strategy is to not change, not think about the value exchange carefully enough, not innovate, then blocking off access to content of an entire country will still fail. I give you Sweden.

Let me get off my high horse, and let’s get some tracking going!

2. Technical how-to implement enhanced code guidance (Google Tag Manager or direct)

Tracking ad blocking behavior is quite simple.

If you have implemented analytics.js, via the standard recommended approaches, you can use the strategy below to simply update the code on your website manually. The code defines a simple plugin and then requires that plugin, passing it the custom dimension index that you’ll create to capture ad blocking status.

All you need to do is make sure you replace the two bits in the red…

adblock analyticsjs tracking avinash

I don’t trust WordPress to render code cleanly, always mucks something up. Please right click and save this text file: adblock_analyticsjs_tracking_avinash.txt

If you want to see a working example of this, just do View Source in your browser and check out my implementation of the above code. You’ll notice my UA id as well as the fact that I’ve set the dimensionIndex as 1 (which will also be true for you if you are not using Custom Dimensions yet).

So, what’s the code above doing? For security reasons, JavaScript code is not allowed to know what extensions are running on a user’s browser. This means we can’t be 100% sure if the user has an ad blocker installed, but we can make a pretty good guess. The way this code works is it creates an HTML element with the class name “AdSense” and temporarily adds it to the page. If the user has an ad blocker installed, the element will be invisible, so if that’s true after the code inspects the element, then we can be reasonably sure the user has some sort of ad blocker installed.

If you’re using GTM and the Universal Analytics tag, you can configure the tag to set the custom dimension via a user-defined custom JavaScript variable. The following function can be passed as that JavaScript and it will share whether ad block is enabled in the browser…

adblock gtm tracking avinash

Please right click and save this text file for your use: adblock_gtm_tracking_avinash.txt

Honestly, that is all you need to do when it comes to things that touch your site.

Let now go and configure the Google Analytics front-end.

3. Setting Google Analytics front end elements (custom dimensions, segments)

Reporting for the ad blocking behavior of your users won’t automatically show up in Google Analytics. We’ll have to do two things to get set up.

Setup the custom dimension.

You need to have Admin privileges to do this step (if you don’t have it, beg someone for it! :)).

Click on the Admin link in the top navigation.

On the resulting page, in the middle pane, called PROPERTY, you’ll see a link called Custom Definitions (weirdly marked with a Dd), click on it.

Then, click on Custom Dimensions.

Then, the beautiful red button called + New Custom Dimension.

Here’s the configuration…

ads blocked custom dimension

Hit Save, and you are done with this part.

ads blocked custom dimension final

Now you also know where the ZZ value of 1 in {dimensionIndex: ZZ} came from. Above.

Setup an advanced segment.

Go to any report in Google Analytics. On top of the main graph, you’ll see a button called + Add Segment, click on it.

Now, click on the red button named + New Segment.

On the left-side of the create segment window, you’ll see a list of choices, under Advanced click on Conditions.

In the box named Filter, in all likelihood the first button you’ll see will read Ad Content, click on it.

You’ll see a search box, type in Ads (of whatever you named your custom dimension above) and you’ll see a Custom Dimension called Ads Blocked. Click. If your boss prohibits you from searching, you can also scroll to the Custom Dimensions category and choose from there.

The next choice you’ll make is to change Contains to Exactly Matches, and finally in the box just type in 1. And, here’s the end result…

ads blocked advanced segment

With this quick step… you are ready to rock and roll with data.

Before we go further, want to guess how many users of this blog, a self-described tech-savvy audience, use ad blockers?

Do I hear 80%?

Do I hear a 70%?

The answer will surprise you, it surprised me!

4. Five Reports and KPIs that deliver critical insights from ad blocking behavior

One caveat… This blog does not have any advertising on it. My books and my startup have links in the right nav, but most people won’t think of them as pimpy ads as they are both mine and the books are inspired by the content from this blog. Hence, when I analyze the data below I might not find the type of insights between folks who use ad blocker and people who don’t use ad blockers because on this site…. there is no advertising.

I’m going to teach you what types of reports and things to look for once you implement the above code. You are going to find fantastic insights from this analysis (like I do when I do this on sites that have lots of ads). But, you might not necessarily see them in the pictures I’m going to show you below – the pictures are just to teach you.

The very first simplest thing you’ll do is figure out:

Q1. How many Users are blocking ads?

You can go to the first report you see when you log into Google and choose your Ads Blocked advanced segment (from above), and you’ll be in business.

I LOVE custom reports [Five Smart Downloadable Custom Reports ]. I used one of my simpler acquisition custom report, and after I apply the segment, this is what it looks like…

google analytics ad block reporting overview

Roughly 14% is the answer.

I have to admit I was pretty darn shocked. Most of my experience suggested that the minimum would be 50%, and perhaps even as high as 75% because of the attributes of the audience that reads this blog.

So much for experience!

This the the fun part about data. It beats experience / opinion / hot air / gut feelings etc.

Go get your own data. Don’t wait for a newspaper, guru, pontificator-in-chief give you a “best practice.”

Also above, you can see bounce rates (I expect that it will be much more different on your site, remember I have no ads here so it would not really dirve big differences). And, you can see the all important metric of Conversion Rate.

Nice report right? Acquisition, behavior, outcomes!

Q2. What is the difference in content consumption between people who block ads and those that don’t?

Simple. Go to the Behavior folder, click on Overview, and bada bing, bada boom…

ads blocked content consumption

There seems to be slight difference between the time that people stay on the site if they use ad blockers. On your site, if you have loads of ads, I suspect you’ll see a much larger difference.

Also look at the contrasts between Pageviews and Unique Pageviews.

Q3. Given the difference in privacy concerns across countries, is the ads blocking rate materially different across the world?

Go to Audience, Geo, Location…. In the bread crumbs on top of the table in this report, I choose Continent (simply to show you the whole world in a small table in the space I have available here)…

google analytics ads blocked location

As you might have expected, Europe is the highest (but not by all that much). This was really fascinating for me because regardless of if I have ads on my site or note, this data is unaffected by the behavior things I was concerned about above. I would have expected Europe or Germany to have way, way higher than ad blocking then I saw in my blog’s data.

Q4. Money! Do I have higher Per Session Goal Value from people who block ads?

Here’s the theory behind this question: If the users are blocking ads they are having a better experience. And, if they are having a better experience, then it is more likely they deliver more goal value per session.

I created a quick and simple custom report for this (standard Google Analytics reports are so cluttered!).

Here’s the main graph that allows me to reflect on long term trends…

ads blocked acquisition overview

For me at least, I would call it a wash.

Your mileage might wary because you’ll actually have ads.

I consider this metric, Per Session Goal Value, to be critical for publishers and hence likely the best one you can use to measure the various implications on you from people’s use of ad blockers.

Next, you’ll look at the scorecard in the table, it gives you three simple metrics that will give context to PSGV…

ads blocked acquisition scorecard

And, finally of course you’ll look to see if our KPI, PSGV, is influenced by the traffic source…

ads blocked acquisition detail

You can see the obvious differences above, it will give you a peek into the heads of the people coming to your site and it will also help you optimize your ad targeting and ad content strategies for Paid Media and even your Earned Media.

Q5. Do people who use ad blocking technologies end up being more loyal customers?

This is a very intriguing question to ask if you are a publisher. Does the recency and frequency change for people who use ad blockers?

Again, that is based on the hypothesis that if you are using ad blockers then supposedly you are having the best experience on the site, it should make your recency and frequency have a different (better!) profile.

I end up using this data to figure out, if the difference is material, to figure out how to consider monetizing these folks (“$5 for an ad-free experience, and you support us and keep us alive!”).

There are other reports you can look at as well, but the collection of KPIs and reports above help you get pointed in the right direction.

And, that’s a wrap!

As always, it is your turn now.

Do you track ad blocking behavior on your website today? If you use a different coding strategy, would you care to share it with us? How many people use ad blockers website, and what type of site is it? Do you see material differences in how people with ad blockers behave (bounce rates, depth of visit, per session goal value etc.) when compared to people who don’t use ad blockers? Loaded question, do you block ads in your default browser?

Please share your strategies, successes, failures, lessons and advice via comments below.

Thank you.

PS: If you have not signed up for my pithy and insightful newsletter, I would love to have you as a subscriber. Sign up here: The Marketing-Analytics Intersect.

Ad Block Tracking With Google Analytics: Code, Metrics, Reports is a post from: Occam’s Razor by Avinash Kaushik

Source: Avinash

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