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The Impact Matrix | A Digital Analytics Strategic Framework

digital_analytics_frameworks

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The universe of digital analytics is massive and can seem as complex as the cosmic universe.

With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where we are, how we are doing and which direction is true north.

I have experienced these challenges on numerous occasions myself. Even simple questions like “How effective is our analytics strategy?” elicit a complicated set of answers, instead of a simple picture the CxO can internalize. That’s because we have to talk about tools (so many!), work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

Soon, your digital analytics strategic framework that you hoped would provide a true north to the analytics strategy question looks like this

digital_analytics_frameworks

The frameworks above cover just one dimension of the assessment (!). There is another critical framework to figure out how you can take your analytics sophistication from wherever it is at the moment to nirvanaland.

A quick search query will illustrate that that looks something like this…

digital_analytics_maturity_models

It is important to stress that none of these frameworks/answers exist in a vacuum.

Both pictures above are frighteningly complex because the analytics world we occupy is complex. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

The Implications of Complexity.

There are two deeply painful outcomes of the approaches you see in the pictures above (in which you’ll also see my work represented as well).

1. Obvious:

No CxO understands the story we are trying to tell – or, even the fundamentals of what we do in the world of analytics. Therefore, they are inclined to remain committed to faith-based decision-making and continue to starve analytics of the attention and investment it deserves.

2. Non-obvious:

Leaders of analytics organizations do not truly appreciate the wonderful effectiveness, or gross ineffectiveness, of their analytics practice (people, process, tools). You see… None of the currently recommended frameworks and maturity models aids analytics leaders in truly understanding the bottom line impact of their work. The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects (OMG we have to get offline attribution!).

When one grasps these two outcomes – blind business leaders, blind analytics leaders – it is simply heartbreaking.

Simplifying Complexity.

The dilemma of how to simplify this complexity, to create sighted business and analytics leaders, has lingered with me for quite some time. I’ve intended to create a simple visual that absorbs the scale, complexity and many moving parts.

On this blog, you’ve seen numerous attempts by me to remedy the dilemma. To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0.  Each aimed to solve a particular dimension, yet none solved the heartache completely. Especially for the non-obvious problem #2 above.

The hunger remained.

I wanted to create a visual that would function as a diagnostic tool to determine if you are lost, trapped in a silo or wandering aimlessly. It would help you realize the extent to which analytics impacted the business bottom line today, and what your future analytics plans should accomplish.

Then one day, a magic moment.

During a discussion around planning for measurement, a peer was struggling with a unique collection of challenges. He asked me a couple of questions, and that sparked an idea.

I walked up to the whiteboard, and excitedly sketched something simple that abstracted away the complexity – and yet preserved the power of smarter thinking at the same time.

Here’s the sketch I drew in response:

impact_time_metrics_matrix_sketch

Yes, it was an ugly birth. But, to me, the proud parent, it was beautiful.

It took a sixteen hour direct flight to Singapore for the squiggly sketch to come to life – where else, in PowerPoint!

The end result was just five slides. As the saying goes: It’s not the ink, it’s the think.

I want to share the fully fleshed out, put into practice and refined, version of those four slides with you today. Together, they’ll help you fundamentally rethink your analytics practice by, 1. understanding data’s actual impact on your company today and, 2. picking very precise and specific things that should be in your near and long-term analytics plans.

The Impact Matrix.

To paint a simple picture of the big, complicated world of analytics, the whiteboard above shows a 2×2 matrix.

Each cell contains a metric (online, offline, nonline).

The business impact is on the y-axis, illustrated from Super Tactical to Super Strategic.

The time-to-useful is on the x-axis, illustrated from Real-Time to 6-Monthly.

Before we go on… Yes, breaking the x-axis into multiple time segments creates a 2×5 matrix, and not a 2×2. Consider that to be the price I’ve paid in order to make this more actionable for you. 🙂

Diving a bit deeper into the y-axis… Super Tactical is the smallest possible impact on the business (fractions of pennies). Super Strategic represents the largest possible impact on the business (tens of millions of dollars).

The scale on the y-axis is exponential. You’ll notice the numbers in light font between Super Tactical and Super Strategic go from 4 to 10 to 24 to 68 and onward. This demonstrates that impact is not a step-change – every step up delivers a massively higher impact.

impact-time-metrics-matrix-shell-sm

Diving a bit deeper into the x-axis… While most data can be collected in real-time now, not all metrics are useful in real-time.

As an example, Impressions can be collected in real-time and they can also become useful in real-time (if actioned, they can have a super tactical impact – fractions of pennies). Customer Lifetime Value on the other hand takes a long time to become useful, over months and months (if actioned, it can have a super strategic impact on the business – tens of millions of dollars).

Here is a representation of these ideas on the Impact Matrix:

impact-time-metrics-matrix-framing_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

Impressions can be used in real-time for decision-making by your display, video and search platforms (e.g., via automation). You can report Gross Profit in real-time, of course, but doing so is almost entirely useless. It should be deeply analyzed monthly to yield valuable, higher impact actionable insights. Finally, Lifetime Value will require perhaps the toughest strategic analysis, from data accumulated over months, and the action takes time to yield results – but they are magnificent.

Pause. Reflect on the above picture.

If you understand why each metric is where it is, the rest of this post will fill you with euphoric joy rarely experienced without physical contact.

The Impact Matrix: A Joyous Deep Dive.

In all, the Impact Matrix contains 46 of the most commonly used business metrics – with an emphasis on sales and marketing. The metrics span digital, television, retail stores, billboards, and any other presence of a brand you can think of. You see more digital metrics because digital is more measurable.

Some metrics apply across all channels, like Awareness, Consideration and Purchase Intent. You’ll note the most critical bottom line metrics, which might come from your ERP and CRM systems, are also included.

Every metric occupies a place based on business impact and time of course, but also in context of other metrics around it.

Here’s a magnified view that includes the bottom left portion of the matrix:

impact-time-metrics-matrix-close-up_sm

Let’s continue to internalize impact and time-to-useful by looking at a specific example: Bounce Rate. It’s in the row indicating an impact of four and in the time-to-useful column weekly. While Bounce Rate is available in real-time, it is only useful after you’ve collected a critical amount of data (say, over a week).

On the surface, it might seem odd that a simple metric like Bounce Rate has an impact of four and TV GRPs and % New Visits are lower. My reason for that is the broader influence of Bounce Rates.

Effectively analyzing and acting on Bounce Rates requires the following:

* A deep understanding of owned, earned and paid media strategies.

* The ability to identify any empty promises made to the users who are bouncing.

* Knowing the content, including its emotional and functional value.

* The ability to optimize landing pages.

Imagine the impact of those insights; it is well beyond Bounce Rates. That is why Bounce Rate garners more weight than Impressions, Awareness and other common metrics.

When designating a metric as a KPI, this is your foremost consideration: depth of influence.

With a better understanding of the Impact Matrix, here’s the full version:

impact-time-metrics-matrix-complete-sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

As you reflect on the filled out matrix, you’ll note that I’ve layered in subtle incentives.

For example, if you were to compute anything Per Human, you would need to completely revamp your identity platforms (a strategy I’ve always favored: Implications Of Identity Systems On Incentives). Why should you make this extra effort? Notice how high those metrics sits on the business impact scale!

Other hidden features.

The value of voice of customer metrics is evident by their high placement in context of the y-axis. Take a look at where Task Completion Rate by Primary Purpose and Likelihood to Recommend are, as an example. They are high in the hierarchy due to their positive impact on both the business and the company culture – thus delivering a soft and hard advantage.

You’ll also note that most pure digital metrics – Adobe, Google Analytics – sit in the tactical bottom line impact. If all you do day and night is just those metrics, this is a wake-up call to you in context of your actual impact on the company and the impact of that on your career.

At the top-right, you’ll discover my obsession with Profit and Incrementality, which form the basis of competitive advantage in 2018 (and beyond). Analyzing these metrics not only fundamentally changes marketing strategy (think tens of millions of dollars for large companies); their insights can change your company’s product portfolio, your customer engagement strategies and much more.

The matrix also includes what is likely the world’s first widely available machine learning-powered metric: Session Quality, which you’ll  find roughly in the middle. For every session on your desktop or mobile site, Session Quality provides a score between 1 and 100 as an indication of how close the visitor is to converting. The number is computed based on a ML algorithm that has learned from deep analysis of your user behavior and conversion data.

Pause. Download the full resolution version of the picture. Reflect.

It is my hope that the placement of each of the 46 metrics will help you add metrics that might be unique to your work. (Share them in comments below, add to our collective knowledge.)

With a better understanding of the matrix, you are ready to overcome the two problems that broke our hearts at the start of the post – and do something super-cool that you did not think we might.

Action #1: Analytics Program Maturity Diagnostic.

Enough theory, time to some real, sexy, work.

The core driver behind creation of the Impact Matrix was the non-obvious problem #2: How much does your analytics practice matter from a bottom line perspective?

YOU matter if you have a business impact. You’ll have a business impact if your analytics practice is sophisticated enough to produce metrics that matter. See the nice circular reference?

🙂

In our case we measure maturity not by evaluating people, process, and layers upon layers of tools, rather we measure maturity by evaluating the output of that entire song and dance.

Answer this simple question: What metrics are most commonly used to make decisions that drive actual actions every week/month/more?

Ignore the metrics produced as an experimental exercise nine months ago. Ignore the metrics whose only purpose is to float along the river of data pukes. Ignore the metrics you wish you were analyzing, but don’t currently.

Reality. Assess, reality. No point in fooling yourself.

Take the subset of metrics that actively drive action, and change the font color for them to green in the Impact Matrix.

For a large European client with a multi-channel existence, here’s what the Impact Matrix looked like after this honest self-reflection:

impact-time-metrics-matrix-analytics-program-savvy-sm

More of the digital metrics are green, because there are more digital metrics period. You can see the company’s marketing strategy spans television and other offline advertising, including retail.

You’ll likely recognize many of these metrics as the one that your analytics practice outputs every day. They represent the result of a lot of hard work by the company employees, and external analytics partners.

We are trying to answer the how much does the analytics practice matter question. You can see that more sharply now.

For this company most green metrics cluster in the bottom-left quadrant, with most having an impact of ten or under (on a y-axis scale of 1 to a ). There is one clear outlier (Nonline Direct Revenue – a very difficult metric to compute, so hurray!)

As every good consultant know, if you have a 2×2 you can create four thematic quadrants. In our case the four quadrants are called Solid Foundation, Intermediate, and Advanced:

For our company, the maturity of the analytics practice fit mostly in the Solid Foundation quadrant.

Is this a good thing?

It depends on how long the analytics practice has been around, how many Analysts the company has, how much money it has invested in analytics tools, the size of their agency analytics team, so on and so forth.

If they have a team of 50 people spending $18 mil on analytics investment each year, over the last decade, with 12 tools and 25 research studies each year… You can now infer that this is not a good thing.

Regardless, the Impact Matrix now illuminates clearly that highly influential metrics are underutilized. These are the metrics  that facilitate deeper thought, patience and analysis to deliver big bottom line impact.

Recommendation Uno:

Conduct this exercise for your own company. Identify the metrics actively being used for decision-making. Which quadrant reflects the maturity of your analytics program? With the investment in people, process, tools, and consultants, are you in a quadrant where your bottom line impact is super strategic?

Recommendation Dos:

Identify your target quadrant. In this instance the company could move bottom-right and then up. They could also move top-left and then top-right. The choice depends on business strategy and current people, process, tools reality. This should be obvious; you always want the Advanced quadrant lit up. But, you can’t go from Beginner to Advanced directly – evolution works smarter than revolution. (If your Solid Foundation quadrant is not lit up, do that first.)

Recommendation Très:

Create a specific plan for the initiatives you need to undertake to get to your next desired quadrant. You’ll certainly need new talent, you’ll need a stronger strategic leader (less ink, more think), you’ll need to identify specific analytics projects to deliver those metrics, and you’ll most definitely need funding. Divide the plan into six-month segments with milestones for accountability.

The good news is that it is now, finally, clear where you are going AND why you are going there. Congratulations!

Recommendation Cuatro:

Start a cultural shift. Share the results of your assessment, the green and black reflection of the current reality, with the entire company. Inspire each Marketer, Finance Analyst, Logistics Support Staff, Call Center Manager, and every VP to move one step up or one step to the right. If they currently measure AVOC, challenge them to move to Unique Page Views or Click-thru Rate. It will be a small challenge, but it will improve sophistication and, as you can see in the matrix, the impact on the bottom line.

Most companies wait for some Jesus-Krishna hybrid to descend from heaven and deliver a glorious massive revolution project (overnight!). These never happen. Sorry, Jesus-Krishna. Instead, what it takes is each employee moving a little bit up and a little bit to the right while the Analytics team facilitates those shifts. Small changes accumulate big bottom line impact over time.

So. What’s your quadrant? And, what’s your next right or next up move?

Action #2: Aligning Metrics & Leadership Altitude.

When offered data, everyone wants everything.

People commonly believe that more data means better results. Or, that if an Agency is providing a 40 tab, font size 8, spreadsheet full of numbers that they must have done a lot of work – hence better value for money. Or, a VP wants two more histograms that represent seven dimensions squeezed into her one page dashboard.

If more data equaled smarter decisions, they would be peace on earth.

A core part of our job, as owners of the analytics practice, is to ensure that the right data (metric) reaches the right person at the right time.

To do so, we must consider altitude (aka the y-axis).

Altitude dictates the scope and significance of decisions.  It also dictates the frequency at which data is received, along with the depth of insights that need to accompany the data (IABI FTW!). Finally, altitude determines the amount of time allotted to discuss findings.

Managers have a lower altitude, they are required to make tactical decisions – impacting say tens of thousands of dollars. VPs have a higher altitude, they are paid a ton more in salary, bonus and stock, because they carry the responsibility for making super strategic decisions – impacting tens of millions of dollars.

This problem has a beautifully elegant solution if you use the Impact Matrix.

Slice the matrix horizontally to ensure that the metrics delivered to each leader are aligned with their altitude.

impact-time-metrics-matrix-leadership-levels_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

VPs sit at decision making that is squarely in the Super Strategic realm – on our scale ~40 and higher. This collection of metrics power heavy decisions requiring abundant business context, deep thinking and will influence broad change. Analysts will need that time to conduct proper analysis and obtain the IABI.

You can also see that nearly all metrics delivered to the VPs arrive monthly or even less frequently. Another reflection of the fact that their altitude requires solving problems that will connect across orgs, across incentives, across user touch points, etc.

So. Are the metrics on your VP Dashboards/Slides the ones in Super Strategic cluster?

Or. Is your analytics practice such that your VPs spend their time making tactical decisions?

Below the VP layer, you’ll see metric clusters for slightly less strategic impact on the company bottom line for Directors. The time-to-useful also changes on the x-axis for them. Following them is the layer for managers, who make even more frequent, tactical  decisions.

The last layer is my favorite way to improve decision making: Removing humans from the process. 🙂

Recent technical advancements allow us to use algorithms – machine learning – to automate decisions made by metrics that have a Super Tactical impact. For example, there is no need for any human to review Viewability because advanced display platforms optimize campaigns automatically against this metric. In fact an expensive human looking at reports with that metric will only slow things down – eliminating the fractions of penny impact that that metric delivers.

Recommendation Cinco:

Collect the dashboards and main reports created by your analytics practice. Cluster them by altitude (VP, Directors…). Identify if the metrics being reported to each leadership layer are the ones being recommended by the Impact Matrix.

For example: Does your last CMO report include Profit per Human, Incremental Profit per Non-line Channel, % Contribution of Non-line Channels to Sales? If yes, hurray! Instead, if they are reporting Awareness, Consideration, Intent, Conversions, Bounce Rate… Sad time. Why would your CMO use his or her valuable time making tactical choices? Is it a culture problem? Is it a reflection of the lack of analytical savvy? Why?

Put simply, the big and complicated is not so big and not so complicated. This simple analysis will help identify core issues that are stymieing the contribution data can make to smarter, faster, business success.

Recommendation Seis:

Kick off a specific initiative to tackle automation. If data is available in real-time and useful in real-time, there are algorithms out there that can make decisions for humans. If there is a limitation, it is all yours (people, bureaucracy, connection points, etc.).

For the other layers, action will depend on what the problem is. It could require new leadership in the analytics team, it could require a shift in company culture, or it could require a different engagement model across layers (managers, directors, VPs). One thing adjusting the altitude will certainly require: Change in how employees are compensated.

As you notice above, the strength of the matrix is in it’s ability to simplify complexity. That does not mean that you don’t have to deal with complexity – you can be more focused about it now!

Action #3: Strategic Alignment of Analytical Effort.

One more slicing exercise for our matrix, this time for the analytics team itself.

Analytics teams face a daunting challenge when figuring out what type of effort to put into tackling the fantastic collection of possibilities represented in the Impact Matrix.

That challenge is compounded by the fact that there is always too much to do and too few people to do it with. Oh, and don’t get me started on time! Why are there only 24 hours in a day??

So, how do we ensure that each has an optimal analytical approach?

Slice the matrix vertically along the time-to-useful dimension…

impact-time-metrics-matrix-analytical-effort_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

For any metric that is useful in real-time, we have to unpack the forces of automation. Campaigns can be optimized based on real-time impressions, clicks, visits, page views, cost per acquisition etc. We need to stop reporting these, and start feeding them into our campaign platforms like AdWords, DoubleClick etc. With simple rules – ranges mostly – automation platforms can do a better job of taking action than humans.

If you are investing in machine learning talent inside your team, even narrowly intelligent algorithms they build will learn faster and surpass humans quickly for these simple decisions.

With the day-to-day sucking of life spirit reduced, tactical impact decisions automated, the analytics practice has time to focus on metrics that have a longer time-to-useful and need deeper human analysis to extract the IABI.

For metrics available weekly or within a few weeks, reporting to various stakeholders (mostly Managers and Directors) should adequately inform decisions. Use custom alerts, trigger threshold targets and more to send this data to the right person at the right time. For weekly time-to-useful metrics, your stakeholders have enough tactical context that you don’t need to spend time on deep analysis since the metrics inform the tactical decisions.

More role clarity, a thoughtful shift of the burden to the stakeholders, and more free time to focus on what really matters.

For where time-to-useful is in the month range, you are now truly heading into strategic territory. Reflect on the metrics there – challenging, strategic, Director and VP altitude. It is no longer enough to just report what happened, you need to identify why it happened and what the causal impact is for the why factors. This will yield insights that will have millions of dollars of potential impact on the company. That means, you’ll need to invest in ensuring your stories have more than just insights but also include specific recommended actions and predicted business impact. Amazingly, you’ll have just as much text as data in your output (that’s how you know you are doing it right!).

Finally, we have the pinnacle of analytics achievement. Our last vertical slice includes metrics that measure performance across customer segments, divisions and channels, among other elements. This is where meta-analysis comes into play, requiring even more time, with even more complex analytical techniques that pull data into BigQuery or similar environments where you can do your own joins, unleash R, use statistically modeling techniques (hello random forests!) to find the most important factors affecting your company’s performance.

The distribution of your analytical team’s effort across these four categories is another method of assessing maturity as well as ensuring optimal impact by the precious few analytical resources. For example: If most of your time is occupied by providing data to decision-makers for metrics in the Automate and Reporting vertical slices, you are likely in the beginner stage (and not having much impact on the business bottom line).

Recommendation Siete:

Find an empty conference room. Project all the work your team has delivered in the last 30 days on the screen. Cluster it by Automated, Reporting, Analysis and Meta-Analysis. Roughly compute what percentage of the team’s time was spent in each category. What do you see? Is the distribution optimal? And, are the metrics in each cluster the ones identified by the Impact Matrix? 

The answers to these questions will cause a fundamental re-imagination of your analytics practices. The implications will be deep and wide (people, process, tools). That is how you get on the road to true nirvanaland.

#sisepuede

At the core of the Impact Matrix is the only thing that matters: the business bottom line. Using two simple dimensions, impact and time-to-useful, you can explain simply three unique elements of any successful analytics practice. The reflections are sometimes painful, but bringing them to light allows us to take steps toward systematic improvement of our analytical practice.

That’s the power of a 2×2 (or a 2×5)!

Here’s an Excel version of the Impact Matrix for your personal use. 

As always, it is your turn now.

When your CMO asks, “How effective is our analytics strategy?”, what’s your answer? How simply can you frame it? What are the primary inputs to your near and long-term analytics evolution plans? If your VPs are getting the metrics in the Advanced quadrant, what strategies have been effective in getting you there? If you’ve successfully implemented pattern matching and advanced classification meta-analysis techniques, care to share your lessons with us?

Please share your feedback about the Impact Matrix, and answers to the above questions, via comments below. I look forward to the conversation.

Thank you.



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Orignal Article Can Be Found Here

Passive Consumption + Active Engagement FTW!

passive_consumption

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Today something complex, advanced, that is most applicable to those who are at the edges of spending money, and thus have an intricate web of internal and external teams to deliver customer engagement and business success.

The Marketing Industrial Empire is made up of number of components.

If you consider the largest pieces, there is the internal (you, the company) and the external (agencies, consultants).

If you consider entities, you’ve got your media agency, your creative agency, your various advertising agencies, your website and retail store teams, your analysts, marketers, advertising experts, the UX teams, campaign analysts, fulfillment folks, the data analysts who are scattered throughout the aforementioned entities, the CMO, CFO, and hopefully your CEO. And I’m only talking about the small portion of your existence that is your marketing and analytics.

Whether you consider the large, simplistic perspective (internal – external) or the more complex entity view, it’s really easy to see how things can become siloed very quickly.

It’s so easy for each little piece (you!) to solve for your little piece and optimize for a local maxima. You win (bonus/promotion/award). It is rare that your company wins in these siloed existence.

That’s simply because silos don’t promote consideration of all the variables at play for the business. They don’t result in taking the entire business strategy or the complete customer journey. Mining a cubic zirconia is celebrated as if it is a diamond.

Heartbreakingly, this is very common at large and extra-large sized companies. (This happens a lot less at small companies because of how easily death comes with a local maxima focus.)

So how can you avoid this? How do you encourage broader, more out-of-the-box thinking?

This might seem simplistic, but sometimes it helps to give things names. Naming things clarifies, frames, and when done well it exposes the gaps in our thinking.

Today, I want to name two of the most common silos in large and extra-large companies, in the hope that it’ll force you to see them and subsequently abandon siloed thinking and solve for a global maxima.

Name abstract ideas, draw pictures, deepen appreciation, take action.

Could not be simpler, right? 🙂

Let’s go!

The Advertising Ecosystem: Passive Consumption.

I’m randomly going to use Geico as an illustrative example because the frequency at which they are buying ads means that every human, animal, and potted plant in the United States has seen a Geico commercial at least once in the last 6 hours (contributing to Geico’s business success).

Typically the ads we see are the result of the external creative and media agencies, and their partners in the internal company team/s.

Geico purchases every kind of ad: TV spots, radio ads, billboards (OOH), digital displays (video, online,– social media), print (magazines, newspaper, your cousin’s Christmas letter), and so much more.

The teams naturally gravitate towards optimization and measurement that spans their individual mini-universes.

Was that a great ad? Can we test different spending levels in that market? What is the best way to get people to remember the delightful gecko? Can we automate the placement of display ads based on desired psychographics?

Did we get the TRPs that we were shooting for? What was the change in awareness and consideration? What was the reach/frequency for the Washington Post? How many impressions did our Twitter ads get, and how many people were exposed to our billboards?

These are important questions facets of, and delivery optimization of, the advertising. Questions like these, and adjacent others, tend to drive the entire lives of creative and media agencies/teams. For entirely understandable reasons. Siloed incentives delivering siloed local maxima results.

I cannot stress enough that these results can be positive (for the ad business and, in this case, the sales of insurance products). And yet, as a global maxima person it does not take a whole lot of effort to see a whole lot of opportunity if both the siloed incentives can siloed execution implied by the above questions can be changed.

Here’s an incredible simple way that every human seeking global maxima can look beyond the silo: “So, what happens after?

As in, what happens after the finite confines that are the scope of my responsibility/view?

To see that, the first step is to paint a picture that illustrates the current purpose (your silo), and then give it a name.

Here’s that picture for the example we are using, and the name I gave it is “passive consumption.”

passive_consumption

Over 90% of advertising is passive consumption. This means that the ad is in front of the human and they may see it or not see it.

Even on the platforms where interactivity is at its very core (Instagram, Facebook, YouTube, etc.), almost all of the advertising does not elicit any sort of interactivity. If you look at the percentages, almost no one clicks on banner ads, a small percentage on search ads, and you need only speak with a few people around you to see how many people actively engage with TV ads vs. run to the bathroom or pull out their mobile phone the moment forced-watch TV ads come on.

Keep in mind, this is not a ding against passive consumption or the hard work done by Geico’s agency and internal teams. Blasting ads on TV does cause a teeny tiny micro percentage to buy insurance – a fact provable via Matched Market Tests, Media Mix Models. The teeny tiny micro infinitesimally small number of views of brand display ads will cause outcomes. (Hold this thought, we’ll come back to that in a moment.)

So, what is the passive consumption challenge?

First, how far the vision of the creative and media agencies/teams will see (thus limiting success – global maxima). Second, trapped in the silo the vision for what will be measured and deemed as success.

The first is heartbreaking. The second ensures the death of any long-term impact.

Let me explain.

With over 90% passive consumption…. Well, passive… Smart media and advertising agencies/teams will primarily use post-exposure surveys to measure awareness (what companies provide car insurance) and consideration (which brands you would consider).

The brilliant agencies will also measure elements such as purchase intent (how likely it is that you’ll consider Geico as your next car insurance provider) and likelihood to recommend (how likely is it that you’ll recommend Geico to your family and friends).

All of these metrics will cause surveys to be sent via various mediums to people who’ve seen the TV ads, the banners on Facebook, and the video ads on YouTube. And a subset of users who were not exposed to the ads. Usually, there is anywhere between a few hundred to a thousand survey responses that will end up providing a statistically significant sample.

The scores from these responses are presented in weekly, monthly, or quarterly meetings. Segmented by marketing activity, they are the end-all be-all justification for media spending. Snapchat increased aided awareness by +23%, let us spend more there. Or, billboards in Georgetown and Austin shifted purchase intent by +2%, we should triple our spend in Chicago.

Every measurement and optimization initiative is based on this cocktail of metrics. Thus delivering a positive, but local, maxima.

Even the next best innovation in media will be based on results from the same metrics cocktail. Thus delivering a little more positive, but still local, maxima.

Why not global maxima?

Because success is determined by, innovation is driven by, measurement that is self-reported feelings.

That name captures the actual thing that is being measured (feelings) by the metrics above, and where the data comes from (self-reported) after being exposed to our advertising.

This will help your company, your agencies, understand limits. Limits in terms of what’s happening (mostly, passive consumption) and what data we are looking at (all post-exposure and self-reported).

Limits in measurement that incentivize solving for a local maxima.

Let me repeat one more time. Passive consumption measured by self-reported feelings does drive some success – else Geico would not be the financial success it is. In the short-term some campaigns are trying to drive long-term brand influence or causing a shift in public opinion or simply to remind people your brand still exists as a choice. All good. Self-reported feelings are wonderful. Appreciate that even in those cases where you are not trying to drive short-term sales, if all you have are feelings converted into metrics… You are limiting imagination.

An obsession with just passive consumption by your agencies and internal teams delivers 18 points of success. I’m saying if you think global maxima, remove limits, you can do 88 points!

The Business Ecosystem: Active Engagement.

Getting those additional 70 points success requires breaking the self-imposed creative/media/advertising silo and caring about the human behavior if people lean-in instead of passive consumption – when they take an action (a click, a phone call, a store visit).

Time to draw another picture, and give this behavior a name.

I call it… drum roll please… Active Engagement!

active_engagement

Some people, between 0.01% to 10% (so rare!), who see Geico’s online ads will visit a Geico retail store or Geico’s website.

People are actually doing something. They are walking into your store, talking to an agent, picking up the literature, calling you on the phone, clicking on to your site, watching videos, comparison shopping, and more. This is all human behavior that your tools can report for you.

A small percentage will end up buying insurance – mazel tov! –, providing perhaps the most valuable data.

The lucky thing about active engagement is that, in addition to self-reported feelings, you also get tons of highly-useful quantitative data representing human behavior.

I call this type of data: Observed Human Behavior.

If you are a part of an creative, media, or an internal company team, you have two powerful issues you can solve for: passive consumption (happens most of the time) AND active engagement (happens some of the time).

Likewise, you can seek to understand performance using self-reported data where the people reflect on how they feel, along with behavior data that represents what they actually do.

The combination of these two factors deliver the much needed Global Maxima perspective.

That is how you shatter silos. The creative agency has to care about how ads perform in their labs, in the real world, and what kind of online and offline behavior the creative is driving (end-to-end baby!). The media agency has to care about the creative and where it needs to get delivered (recency, frequency FTW!), and the bounce rate (70% ouch, 30% hurray!) and profit from each campaign. The retail experience team, the call center delight team, and the site experience team will break their silo and reach back into understanding the self-reported feelings data from the media agencies and the ideas that lead to the creative that delivered a human to them.

Everyone cares about the before and after, solving for the overall business rather than their little silo. Passive consumption plus active engagement equals global maxima. Or, self-reported feelings plus observed human behavior equals global maxima.

: )

Here’s a massively underappreciated benefit: It also encourages every employee – internal and external – to take full credit for their impact on the short and long-term effects of their effort.

It is rare to see this happen in real life, even at top American and European companies.

What’s usual is to see the three silos between creative agencies, media agencies, and company internal team. There is usually further sub-segmentation into passive consumption teams (also lovingly referred as brand agencies/advertisers) and active engagement teams (performance agencies/advertisers). The further sub-sub-segmentation into products and services (depending on the company).

They then quickly fall into their respective measurement silos, solving for the local maxima.

Change starts with naming things and drawing pictures. Gather the key leaders at your company and agency partners. Show them passive consumption and self-reported feelings along with active engagement and observed human behavior. Talk through the implications of each picture. Ask this influential audience: What can you contribute to when it comes to breaking silos?

I have yet to meet a single company where simply drawing the picture did not result in a dramatic rethinking of focus areas, responsibilities, and ultimately priorities.

Accelerating Success: Five Quick Changes.

Once you have that discussion, what should you do to truly cause a significant change in behavior?

Five Es form the core of the strategies that I end up using (please share your’s via comments below). They are:

1. Expand the scope of data your employees use.

For the people who buy your television ads, include both store and website traffic data. Break the shackles of GRPs and Frequency.

For people buying your display ads on Facebook, include page depth, bounce rate, as well as micro-conversion rates for those campaigns. Break the shackles Awareness and Views.

For people buying your videos ads on Hulu, complement Hulu’s self-reported feelings metrics with user behavior and conversion rates.

And continue going in this fashion.

2. Expand the incentives structures for your employees.

Most marketing employees, both internal and external, undertaking passive consumption initiatives are rewarded for cost per TRP, effective reach, awareness and consideration increases, etc. Whatever this bucket as an employee incentive, it can stay.

Consider adding one or two KPIs from active engagement. For example: Store visits, phone calls (as a result of that increase in consideration). Website visits, loyalty, micro-outcomes, and 25 other easily-available observed human behavior metrics are available to you pretty much in real-time.

For people who own responsibility for your stores, call center and website, take a metric or two from passive consumption and make it a small part of their incentive structure.

People respond to what they are compensated with, or promoted for. Use it to solve for a global maxima in the company and its customers.

3. Expand the time horizon for success.

This is really hard.

You buy 100 TRPs, it’s expensive, and the executives tend to start badgering you for immediate results.

The problem is that self-reported feelings data takes time, and since at least 90% of passive consumption leads to no immediate active engagement, all this does is incentivize bad behavior by your agencies and employees. Long-term objectives are thrown onto the chopping block and long-term strategies are judged on short-term success – which immediately ruins the campaign’s measurement. Oh and the audience being bombarded by your ads that are trying to deliver short-term outcomes from long-term creative and campaigns… They despise you because you are sucking, they can see that, and they instantly realize your are wasting their time.

No matter how much your wish, a Chicken won’t birth a Lion’s cub.

If you want short-term success, define the clearly as a goal, pick the right short-term self-reported feelings metric and observed behavior metric, now unleash your creative agency and their ideas (on that short-term horizon), then plead with your media agency to buy optimal placements, and ensure the retail/phone/web experience is not some soft and fuzzy experience, rather it is tied to that clear goal and success metrics. Sit back. Win.

If you want long-term success… Same as above, replace short with long. How amazing is that?

4. Expand the datasets that teach your smart algorithms.

If you’ve only visited this blog once in the last 12 months, or read just one edition of my truly amazing newsletter 🙂, Marketing <> Analytics Intersect, it is quite likely I have infected you with the passion to start investing in machine learning in order to bring smart automation to your marketing and user-experience initiatives.

If you are following my advice, make absolutely sure that you are not training your algorithms based solely on passive consumption, self-reported feelings data. It is necessary, but not sufficient.

Rich observed behavior data will provide your algorithm the same broad view of success as we are trying to provide the humans in #2 above. In fact, the algorithms can ingest way more data and complexity. Thus allowing them to solve for a super-global maxima compared to our humble abilities.

Every algorithm is only as smart as the data you use to educate it. Don’t short-change the algorithm.

5. Expand leadership comfort level with ambiguity.

For your TV efforts, there are limits to what you can measure. You have self-reported feelings data, and usually that’s about it. If you have a sophisticated world-class measurement team, you may be running some controlled experiments to measure one or two elements of active engagement observed human behavior data.

For YouTube or Hulu on the other hand, you’ll have additional self-reported feelings data, and if you follow my advice today, plenty of directly-causal observed human behavior data at your disposal.

Get very comfortable with this reality, and execute accordingly.

When some executives are not comfortable with this reality, they typically end up gravitating towards the lowest common denominator. Even in regards to strategies where more is possible (digital), they just end up using self-reported feelings data for everything.

I do understand why this is; executives are pressed for time, so the executive dashboard needs only one metric they can compare across initiatives. This instantly dumbs-down the intelligence that could help contribute to smarter decisions.

Kindly explain this to your executives, share with them the value of being comfortable with a little ambiguity that comes from using the best metric for each initiative type.

We can achieve smarter global maxima decisions if we just use different metrics in some instances.

Closing Thoughts.

The larger the company, the harder it is to solve for a global maxima. Companies need command and control. Companies worry that people are going to run wild in 15 different directions. Companies need to reward an individual, that means creating a finite role that can be defined and measured at a small level. Companies add layers upon layers to manage. Companies create org clusters (divisions). And, more.

Every one of these actions forces a local maxima. Every human can see their few pixels and have no idea what the image looks like.

Even if then the company progresses little by little, they’ll run out of luck one day. Worse some nimble small company – that does not yet have to worry about all of the above – will come eat your breakfast first, then dinner and then lunch.

The lesson in this post applies across the entire business, even if in this instance it is applied to marketing and advertising.

Paint a picture of what the local maxima execution looks like in your division – or better still company. Give these pieces a name. Then, figure out, like I’ve done above, what the connective tissue is that’ll incentivize global maxima thinking and execution.

Carpe diem!

As always, it is your turn now.

In your specific role, are you solving for the global maxima or a local maxima? How about your creative and media agencies? Your internal marketing or product teams? Has your company done something special to ensure that teams are considering both self-reported feelings and observed human behavior? Is there a magic metric you feel that’ll encourage each piece of the business success puzzle to solve for a global maxima?

Please share your wisdom, tips and secrets to success via comments below.

Thank you.



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Orignal Article Can Be Found Here

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.

Prologue.

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.

Ready?

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 Salesforce.com 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

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

Four Stories: A Decade of Writing Occam's Razor!

An off-topic post this week, to celebrate this incredible outpost you’ve helped create on the web, Occam’s Razor.

This month my beloved blog is ten years old. T. E. N!

It feels more like five. But, I’ve already celebrated the blog being five years old!

I have to admit life has been a tad bit busy lately, and it took a note from a reader to remind me of the birthday. Her note read: “…. and it is pretty impressive that you’ve managed to stay relevant for a decade, it is a very long time in digital years…”

It gave me a pause. I had to go check how long I’ve been at this.

My very first post was audaciously titled Traditional Web Analytics is Dead (05/15/06). Given that title, it is amazing that the whole thing has lasted a decade! 🙂

What is frankly shocking is how topical the content seems to be. Five minutes ago, 05/30/06, in my stream I saw a tweet by Christian Bartens referencing a post I’d written on 05/19/06! The 10 / 90 Rule for Magnificent Web Analytics Success.

So today, a little bit of reporting back to you how things have been, a little reflecting my sense of pride on the journey, and an invitation to you to contribute a little story about your experience with my beloved blog. Would you please add it to the comment below? Where are you, how long have you been reading it, what value have you found in it?

The Story In Numbers.

You’ll see in a moment just how much you have been a part of my success, I have actual numbers! 🙂 I’ll share below my journey over the last decade, what it took, I think, to keep Occam’s Razor at such high quality, and the decisions big and small it took to stay relevant and keep the brand of the blog so pure.

But, first, the numbers.

Here’s the Google Analytics trend for Sessions, or Visits as they used to be called back in the day. 🙂 A nice and steady increase in traffic until 2013, then then a flattening out.

occams razor traffic

What’s interesting is that I started the blog, very deliberately, only writing two posts a week. It was quite abnormal as most people blogged multiple times a day. Then as I grew busy after the first book, Web Analytics: An Hour A Day was published, June 2007, I started writing once a week to keep the quality high. I’d switched jobs by now and after the second book, Web Analytics 2.0, Oct 2009, I started writing every other week. Then once every three weeks, then, as you start to see the curve flatten after 2013, once a month.

What is pretty surprising is that traffic that stayed loyal kept increasing. I have 80k RSS Subscribers on Feedburner (what, it is still around!). And, there is also a feed available via Feedly, which currently has 39k Subscribers.

feedly occams razor subscribers

My rough estimation is that 200k people read the blog’s content each month.

I have always been a bit surprised about this because I only write once a month now. But, having analyzed the data in Google Analytics, it turns out a whole bunch of that traffic is reading older content.

And, people engage! There are 28 comments awaiting moderation right now, eight of them are on posts prior to 2010.

Speaking of which…

You are a massive part of my this blog is successful.

Not counting this post, I’ve written 913,661 words in ten years (I still can’t believe it has been ten years!). And, you all have contributed 939,657 words on comments!!

raw author contribution occam's razor

Honestly, it is simply unbelievable.

I’ll admit that encouraging comments, getting you to engage was a very deliberate part of my blogging strategy. I would reply personally to every single person who wrote a comment (I still do). And, it would be thoughtful. I would reply on the blog in a timely manner. Etc.

But this is well beyond my wildest imagination.

Here’s a comparison of you and myself…

conversation rate full stats occam's razor

27 comments on average per post. It used to be much bigger, but like on other blogs the comments have been impacted by social media’s evolution.

Thank you for being such an engaged audience. I will honestly tell you that when the going has gotten tough, your engagement, your questions, your kind words have been a huge motivator. Merci!

Speaking of which… One number I’m very proud of is the result of the decision my wife and I made when we published the two books. We decided that since this blog is a labor of love, that rather than me making money on it, we would donate all the proceeds we make from the book to charity.

web analytics 2

As of today, that number is slightly north of $320,000.

It is an unbelievable amount of money, I don’t think I could possibly donate that much from my other earnings. It has gone to three charities: Doctors Without Borders, The Smile Train and Ekal Vidyalaya. Of all the things that I do with this blog, this is the one I’m most proud of.

Thank you again for helping me do it.

The Story Of My Decade.

I was the Director of Research & Analytics at Intuit when I started this blog (LinkedIn). Writing was a delicate balancing act between doing a full-time job, being responsible for a team and writing in the night. I could not imagine how I did it. (And, it only got crazier and crazier!)

I then did a year of consulting, via my company ZQ Insights, with a few companies like Dell and AOL, and a little entity called Google. At the end of that year, I accepted a full-time job at Google as an Analytics Evangelist. Brett deserves my eternal gratitude for creating this wonderful position for me. My second job at Google was to as the Digital Marketing Evangelist – primarily as a result of me realizing that data was not the problem, in fact it was not even fifth on the list and I wanted to go solve the real strategic problems for the largest companies on the planet.

Avinash Kaushik

My current job at Google is perhaps my most exciting yet, leading a group of storytellers who use data and strategic business frameworks as the bedrock to do something hard and magical: Changing minds.

Along the way, I’ve been on the board of advisors of four companies (two successful exits!). It was an amazing experience each time, and as you know what does not kill you makes you stronger.

I also started Market Motive to transform education for digital disciplines with my friend John and Michael. Selling it recently to Simplilearn was a thrill, we are all so excited for the hockey stick growth that we expect MM to have now.

Market Motive was fantastic as I was also the Faculty for Web Analytics. This meant Live Class every week, new videos of the latest content, engaging with students on most days, grading their final dissertations, constantly trying to solve for the higher order bit… I cannot share how influential this was in forcing me to be not just current but two steps ahead.

A source of deep satisfaction during this decade has been the ability to influence analytics products. There are parts of Google Analytics I can point to and feel a sense of gratification that I had the privilege of working on it or initiating the creation of. There have been other tools at Google like the Keyword Tool or Webmaster Tools or even goo.gl etc. I feel so happy that, literally, millions of people in the world use something I had the privilege of working on. Beyond Google, I’ve advised, for free, many startups on their work, many of you use these tools today, bringing me great joy. Posts from this blog have also influenced many metrics, reports, and dashboards you see in other tools. In one case at least, TrueSocialMetrics, the entire tool and company started from one blog post (Best Social Media Metrics). Money cannot buy the sense of pride I feel.

The whole time, there were keynotes to be delivered around the world, new audiences to engage, deep diving into different countries, business environments, hunting for the good an the not-good, all in a constant to be the most memorable and valuable speaker for every audience! That is how you end up with more than a million miles flown in less than ten years (just on United).

Having three jobs at the same time means seventy-hour work weeks (and no keeping up with the kardashians). It was been absolutely unbelievable, an amount of professional growth, powered by curiosity I express every day to come back to you on this blog with something incredible and of value.

The Story Of Three Early Choices.

Here are some decisions that, in hindsight, had a huge impact on me and the blog.

1. I’d decided early on that I would not have any advertising on the blog, in fact I would never ask people to hire me as a consultant or speaker or anything else. I never wanted to directly make any money from the blog, that gave me the freedom to focus just on teaching by sharing my knowledge as I accumulated it.

The only commercial stuff here are the links to Market Motive or my books in the side nav. I rarely, if ever, ask you to buy either.

I think this was huge for me because I never had to pimp, and that always pollutes intent, and it brought focus. It also became easy to say yes or no to things that lead to commercial things. Guest posts. Pimping other people’s stuff. Getting you to show up at my events. Etc. Etc.

All of this made it easier to see the knowledge here is in the purest way it was intended.

2. I also decided that I would only write if I had something incredible and of value to share. Else. Shut up and post nothing.

This allowed me to serve the God of Quality beyond all else. As I got busier, I kept posting less because it would compromise quality. It also meant that I had to be very good at things before I could write about them (forcing me to be amongst the first in the industry to get into things that were not yet mainstream – mobile, social, analytics evolutions, marketing, competitive intelligence, decision making challenges etc. etc.).

This was huge for me because the reason people came, and kept coming, is because they were a little more than reasonably guaranteed to get fantastic, bleeding edge thinking in a non-pimpy environment. This is also the reason that I’ve managed to have three jobs at one time and evolve in each of them (to an extent that web analytics itself forms a much smaller part of my core).

3. I deliberately decided not to syndicate the content on this blog. This was hard for me because I know that I am lot less famous because I’ve refused to have the content of this blog on the HBS blog or one of the industry blogs or Huff Po or LinkedIn or so many other places. They are all glorious places where there is a ton of traffic and it would have benefited me.

But, the upside for me is that you can only find my content here. And, if you want to be intelligent about analytics and marketing, you’ll have to come here. My house. My terms. My customer (you!). This has turned up to be a great strategy because my presence is not fragmented all over the web and I’m not at the mercy of sites becoming famous or dying for the attention of my precious audience.

There are many other choices I’ve made, big and small, along the way. But these three have had a huge impact, and I hope as you think of your own platform (and you should have one) you’ll find them to be of value.

The Story Of Benefits To Me.

So, so, so, so many.

I have made so many brilliant friends. People out there that inspire me, Thomas and Mitch and Seth and Bryan and so many others. People that make me so happy when I see them around the world, like Marco when I visit Germany or Zoli when I’m in Hungary. My circle is huge. For an introvert to have so many people to know and to care for and engage in an exchange of ideas is an immense gift.

The blog has helped me be “famous.” As I tell my kids, medium-sized fish in a small fish-bowl. 🙂 This has brought with it so many benefits, indirectly financial and otherwise.

The blog has helped me build a unique brand for myself. For the fifth year anniversary, I’d asked folks in Social Media what three words come to mind when they think about “Brand Avinash,” this is the resulting tag cloud…

brand tag cloud non-analytics avinash kaushik-big[1]

Could a person ask for anything more? Such a gift from you all, from this blog, that I get to read those words. I was deeply touched.

But above and beyond all else, my absolute favourite benefit is the stories strangers tell me when I see them after my keynotes around the world, or in the emails they send to me.

Here’s an example:

Hello Avinash! I wanted to pass along a big thank you for your blog posts and newsletters. I enjoy reading and more importantly, learning from your experiences. I have yet to read a post from you that did’nt simultaneously educate and entertain me.

I am particularly digging your comparison of own vs. rent in the context of platforms. I am also really pleased with your recent newsletter approach.

THANK YOU for being awesome! I look forward to learning more from you in the future.

How very kind is that?

And people are so wonderful to write. Here’s another one:

Hi Avinash,

Just wanted to let you know that every time I visit your blog I spend somewhere between 30 and 60 minutes reading your articles… and 3 or 4 hours with crippling self-doubt about my own way of doing stuff.

It means you write excellent stuff and I’m actually learning something.

Cheers!

I literally LOLed! I loved that someone out there was filled with three to four hours of self-doubt. 🙂 I wrote back to check if they were back to normal after that. He said, I end up in a new and better place. 🙂

Some of my absolute favourite emails have this spirit in them…

Avinash.

You are a huge inspiration and have contributed to the intellectual, financial and emotional well being of many.

When I started reading your blog, I was living below the poverty line, and carrying the financial responsibility for my family. With the traditional world view, I had little chance of success in the job environment because I didn’t have fancy degrees nor a valuable skill set and I had entered the workforce in my mid to late 30s.

Reading your blog and books taught me to think intelligently and cut through a lot of years of work otherwise required to gain experience. It also inspired me to pursue excellence and a whole lot more.

I am sharing this with you so you can see the impact you have on lives. I know there are thousands reading your blog and many of their lives are impacted. In little ways and big ways.

I know you get a ton of email. No response needed. Just sharing parts of the story so you have visibility on the huge impact your work has on many lives.

Keep shining!

Every kind email touches me with the generosity of the words strangers write, emails like this one move me deeply.

I write because I love writing and I want to share what little I know. To learn that it has a material impact on someone gives the kind of meaning to my work that money, fame or anything else simply can’t buy. In those moments, you all make me realize that I will do a lot in my life, my kids will be my biggest legacy, but that this decade spent writing close to a million words have had an impact that I could never have imagined. Beyond a doubt a huge impact on me, and some impact on you.

In my wildest dreams on May 15th 2006 I could not have imagined that I would end up here a decade later. Not in my wildest dreams.

Thank you for your kindness. Thank you for your loyalty. Thank you for your encouragement.

I am beyond grateful.

And, I’m going to keep trying, keep learning and keep sharing. My email newsletter, I’m the last human to get into newsletters (!), The Marketing – Analytics Intersect, is the latest iteration of this.

Thank you again.

As always (!!), it is your turn now.

How long have you been reading the blog? Which post was/is your favourite? If you had to describe “brand Avinash” in three words, what would be your three words? Why do you think this blog has been successful, or relevant, for a decade?

I would love to hear from you. Merci.

Four Stories: A Decade of Writing Occam’s Razor! 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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