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Creating A Sense Of Urgency For Higher Conversions Rates!

timbuk2_closer

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By every indicator available, ecommerce is continuing to grow at an insane speed. Although it may seem impossible to imagine with ecommerce already totaling up to 5% of overall commerce, there’s astronomical growth still to come.

Still, I’m heartbroken that some the simplest elements of ecommerce stink so much.

It is 2018—why are there still light gray below-the-fold add to cart buttons?

#youarekillingme

There are numerous subtle issues as well. One strategic issue is illustrated by Timbuk2.


timbuk2_closer

Timbuk2 pays a huge margin to its resellers to sell their messenger bags. These resellers, in turn, give a bigger cut to Amazon, who then sells the Timbuk2 bag for 30% off. Yet, when I want to pay full price on www.timbuk2.com, I have to buy a minimum of $99 to get free shipping!

I understand channel conflict, Timbuk2, but this is just plain not being hungry. You could win bigger by cultivating higher more profitable direct relationships, especially when the old world order of commerce is collapsing all around you.

And I’m ignoring the extremely light gray font reviews…on a shade grayer background!

timbuk2_reviews

Painful.

(I really want to buy the Closer Laptop bag. The small one in Jet Black looks cool. I refused to buy it because I don’t want to reward a lack of ecommerce imagination. I am one person, I know it is not going to really hurt them, but I don’t know how else to protest a brand I love.)

Pause. Deep breath.

I do get excited about this stuff. My heart bleeds digital.

There is an ocean of opportunities when it comes to elevating ecommerce. In this post, I want to focus my passion and zero in on something that is difficult to solve for, yet immensely profitable: Inserting a sense of urgency into the shopping process.

I don’t mean: BUY IT NOW OR ELSE!

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount.

Sizing the Opportunity.

In order to have the same passion to take advantage of this magical opportunity (nudge, nudge) you’ll first want to understand how inefficient your current shopping process is.

Do two things, they’ll bring you to your knees:

1. Go look at your ecommerce conversion rate. It shows you how often you win. 🙂 Your overall conversion rate is likely to be around 2%. You don’t need an advanced degree in math to compute that 2% winning is 98% not winning!

Do something simple. Increase current conversion rate by 25%, quantify how much increased revenue there will be. Yes, that additional $6 mil is not as hard to accomplished for an imaginative focused team – in fact you can get that from implementing half of the recommendations in this blog post.

Bonus: The best computation of conversion rate is orders divided by users (the default in your analytics tool is sessions). This will bring your conversion rate up (yea!!). Still. Big opportunity. And, yes, I did say a decade ago that you should look at the opportunity size within all your website visitors. You should. Still. The conversion headroom is massive.

Google Analytics Ecommerce Reports

2. Go to the Multi-Channel Funnels folder in Google analytics and look at two other yummy reports: Time Lag and Path Length.

They report two dimensions of speed: How long does it take for a human to convert? How many visits does it take for a human to convert?

My preferred choice is Path Length; it is rich and actionable.

This data you’ll see, the analysis you’ll do, will scare you. It will also create a sense of urgency to do something about it!

These two recommendations will help you compute the opportunity size for your management team.

Aim for quintupling revenue, obviously, but calculating just 25% improvement will give you all the budget you need from your management to insert urgency into the shopping process. Present a yummy spreadsheet that quantifies the cost of inaction, how much money you’ll lose by not delivering a 25% improvement every week. It will be heartbreaking, and now you are ready for progress!

Welcome to Nudging.

Nudging has plenty of different definitions. Mine is simple:

A gentle incentive that creates a shift in behavior.

Another insistence of mine that you’ll note below: Nudges are based on a deep understanding of user experience. They solve for the user first, and all of the hard work is done by the company (you!).

In the long run that’ll also create a positive revenue outcome for you. Win-Win.

Below is a collection of nudges, curated from my global experiences, influenced by research and data I’ve access to.

My goal with these recommendations is to have a big impact on your ecommerce existence, and to spark your creativity as you go out and change the world.

Let’s go have some fun nudging people.

1. In-stock status.

It mildly irritates me when sites don’t use this nudge.

How many hotel rooms, cameras, seats in a theater, are left?

Only 15 left in stock. Have that right under the price.

How about: Last run! Be one of the last 9 people to own this credenza design.

OMG! Click, click, click!

Or, 1 in-stock in the REI store next to your office.

Nudge. Nudge.

target_in_stock_status

I’ll admit that you need to have a well-integrated logistics platform to make these ideas work. But given the decade we are in, if you have not already done that, you are facing an existential crisis. Please stop reading this post, pull in your agency and internal teams urgently to figure out how to dig your company out of this deep hole.

If you have a well-integrated logistics platform already, then all I’m asking for is this: lock your online and offline IT folks in a nice Four Seasons suite for 72 hours with your User Researchers, and BAM! Money will start falling from the sky.

Speaking of the Four Seasons, consider how sad their nudging strategy is vs. the one that booking.com has on display:

four_seasons_vs_booking_nudges

All the data you need for this nudge… You already have. That’s what makes the Four Seasons strategy, and that of most sites, so heartbreaking.

Convert the inventory status into a conversion boosting nudge.

2. Life of current price.

It physically pains me how rarely this nudge is used.

Dynamic pricing is everywhere. Why not share that information with the shopper?

This price is guaranteed for the next 18 hours.

This price reflects the highest discount in the past 24 weeks.

Limited-time offer applied to the price you see.

Seasonal promotion! Expires Friday.

Reflects special pricing for our highest-tier Frequent Flyers.

Price has reduced by 14% since your last visit.

I’m sure you’ll find language and phrasing that works perfectly for you (see PS at the end of this post). There is a nugget tied to a unique dimension for your dynamic pricing strategy. Please find it, please use it.

Here’s an example from The Golf Warehouse:

the_golf_warehouse_limited_time_pricing

Here’s another one from Overstock that shows two time based nudges…

overstock_time_bound_sale_time_to_ship

You can take advantage of other dimensions related to pricing that are unique to your digital strategy.

This one comes from YouTube TV: Lock-in this monthly rate for life.

YouTube TV’s price just went up from $35 to $40 (they added more channels). Everyone who’d signed up at $35 was grandfathered at that price – until they cancel!

Yet, this incredible benefit was not a part of YouTube TV’s merchandizing strategy from day one. You can imagine that a whole bunch of additional people (me!) would have jumped on board. Instead not only do I not have YouTube TV, I am sad/upset. Double loss.

You have an entire staff of economists, financial analysts, directors and VPs spending so much time on finding the perfect price to charge an individual. Why not convert that immense hard work into a nudge that creates a sense of urgency?

3. Direct competitor comparisons.

38% cheaper than Nordstrom.

Sometimes, by using one of the multitude of price aggregators, you can have an understanding of where your pricing is at an item level. Where the match is in your favor, why not use that as a nudge?

You can have the comparison for as long as it is valid. You don’t even need to specify a time—people are familiar with FOMO.

Only at B&H, this item comes with a free LG Watch!

First, who does not like free stuff?

Second, who does not like believing they are getting a special deal?

Three, who does not freak out that if they don’t buy it right away, this “insane deal” will disappear?

Me. I did that. At B&H. 🙂

Again, your merchandizing team is working hard to procure these amazing bundles for your customers, so why are they not a core part of your nudge strategy?

Costco Special: Get an extra year of warranty!

Our average delivery times to California are 50% faster than Amazon.

Save $150 on installation compared to Best Buy!

Our return rates are 40% lower than Wayfair.

You catch my drift.

Here’s just one example from SugarCRM:

sugar_crm_comparison

Here’s a comparison on Honda’s site…

toyota_honda

No, actually it is from Toyota’s site.

They know that if their car is more expensive, with worse mileage etc., better to be upfront as the customers are looking for that information…

You can also go deeper when it comes to implementing the spirit of this nudge. Kendrick Astro Instruments has the normal table based competitor comparison, additionally they also have a detailed comparison with images to give you more detail…

kendrick_astro

This shows hunger and desire to win… Their text:

This image displays the quality of Kendrick’s cabling that we use on all Premier and FireFly heaters. Our cabling remains flexible in cold weather (down to -40° C), are all labeled for easy identification and all have metal RCA connectors..

This is the text next to their competitor’s image (which you can view in higher resolution):

This image displays a competitor’s cabling. It is a PVC coated RCA patch cord. PVC gets very stiff in the cold and as a result, makes it an awkward component to use at the telescope. As well, due to the lack of flexibility and give in the cold, it can defocus camera lenses.

Not all that hard to see how this nudge drives higher conversion rates.

Your employees stand up at 11:00 AM each day and sing the company song. There is a line in there about your company’s unique value proposition. Something so special, it stands out against everyone you compete with.

Why let that be your little secret? Why don’t you convert that into a nudge?

Consider how much louder your 11:00 AM company sing-a-long will be when your employees see you laying it out there and going head to head with your competitors.

4. Delivery times based on geo/IP/mobile phone location.

Amazon does this really well.

Each item’s estimated delivery time to you depends on the closest warehouse to your home address. So that Timbuk2 bag might be delivered to me the next day, but it would take two days to get to Carissa in Alabama.

Amazon shows this best delivery time for me right next to the price.

More often than not, I see that Prime One-Day or Prime Same-Day and, as if by magic, I find my mouse glide toward the Order Now button!

amazon_nespresso

The closeness of the customer to your delivery environments remains an infrequently used strategy in creating an urgency nudge.

Another dimension of the delivery time nudge is order in the next 4 hours and get it tomorrow with fast shipping!

In our instant gratification culture, who can resist that?

You are $39 away from overnight shipping has been done to death. (If you are in this category, know that the last “secret” of ecommerce is that figuring out how to weaponize shipping – and free returns – is a powerful conversion increasing engine. Not easy, but your business model has to change to survive.)

But. If you are still in that world—don’t worry, I still love you—know that a behavioral shift from an emphasis on cost to an emphasis on the benefit will make a huge difference.

Add another $39 to your order and get your order 48 hours faster!

This takes advantage of the person’s location, your warehouse location, and your shipping policy, and frames it all as a positive nudge.

A couple more examples to inspire you.

Love these delicious sandals on Express. My wife thinks I’ll look prettier in the red, I think the Mustard really looks like my color. 🙂

I love the nudge they have built-in showing how many in my size are in stock (only one!)…

express_sandal_one_in_stock

Not wanting to risk it, I click on the Find in Store link you see at the bottom of the page.

I get a interstitial that shows me availability of the sandal by geographic location…

express_sandal_location

Here’s the lovely part… I did not have to do anything. Express did a reverse lookup based on my IP Address, matched that with their stores, then checked their ERP system for inventory and got me the answer. All inside one second.

Nudge, nudge!

One more.

Dominos will now deliver a pizza to you wherever you are. Literally wherever. In a park, in the dark woods, under a bridge. They look up your mobile location (with your permission), and they’ll come find you.

Assuming you want pizza that bad.

There are still websites that ask you to choose your country when you land. In this day and age, for the sake of Zeus, I hope that is not you.  But, how inventively are you using the location nudge?

Significantly higher revenue awaits.

5. Social cues to the rescue.

The last couple of months have not been great for social networks. I’m sure something beneficial will come to the entire digital ecosystem from all this.

A minority might believe that the whole social media thing is going to die. It is not. Community and sharing are core to who we are as humans. It is not going to change. (And, you still need a place for guilty pleasures: indulging in the latest Kardashian-West clan developments!)

Stretch your imagination and it is not hard to come up with some super-clever nudges that incorporate aggregate non-PII information that is public.

People have shared this blouse 18 times in the last hour on Instagram.

80 people in California have booked this destination in the last 30 days.

1,846 Pins for this closet on Pinterest.

Our most tweeted style of underwear!

800 plusses on Google+.

Ok, so maybe not Google+ (I was genuinely excited about it, I am sad it died). But you get the idea.

Social cues (/proof) can help create a sense of urgency for a whole host of companies. Yet, I bet you’ve rarely seen the use of this aggregated information to deliver nudges.

Here’s a simple example of aggregated non-PII based social cue, from, a site you’ve seen me express adoration for in the past, ModCloth. Every product has a little heart sign, visitors to the site vote their love which helps me make more confident decisions…

modcloth_midi_skirt

ModCloth also allows their customers to contribute something you might consider PII, their photos. These make perhaps the ultimate social proof as I can see the skirt I want (mustard again FTW!) on different body sizes…

modcloth_midi_skirt_user_pictures

ModCloth has a whole lot of social proof strategies. They have a Style Gallery, #ModClothSquad, #MarriedinModCloth etc.

Think expansively about social proof.

Naked Wines has a lovely widget next to each of their wines that shows the would buy again rate…

naked_wines

And, they show you historical sales and would buy it again rates.

Checkout the Kimbao Sauvignon Blanc you can see sales and would buy it again rates since 2011. At 91%, the rate is highest this year. Sweet. Add to Basket!

Another team thinking expansively about leveraging social proof are the excellent folks at Basecamp. If you scroll to the bottom of their web pages you’ll see…

basecamp_customers_trend

Completely non-PII based social proof, a simple cumulative trend of the number of customers. What better way to convince you to use them than this lovely up and to the right trend?

One final, massively underutilized, social proof nudge for you to consider.

Every smart ecommerce strategy has an individual-level referral program bolted on from the very start. Your current customers refer your products and services to their friends, family, and complete strangers—in exchange for a little benefit for themselves.

It is rare, however, to see the use of that referral information as a nudge.

Your friend Alex will receive $5 if you order in the next 24 hours.

The site is keeping track of the referral (to pay your friend Alex his bounty). They have all the information they need to create the above line of text. Why not use it?

Read Diana’s review of this product.

Diana, of course, referred the product to you, and that insight is in the URL you used to get to the site. The site is simply going the extra mile to surface Diana’s review, as it will likely be more meaningful to you than the other 29.

I love Patagonia; I value the brand’s ethos so deeply. And, when I say love, I mean LOVE. Two of the three pieces of clothing I’m wearing right now are from Patagonia. Yet there does not seem to be any strategy at Patagonia to help me (and you and other brand lovers) to create social cue nudges.

Humans inherently want to share, they want to show off, and they want to pass on recommendations/deals to their community. Got social nudges?

6. Personalization. Yes, from 1995!

Do you remember what I did during the last visit to your website?

No PII, just off the anonymous first-party permission-based cookie. Did you use that to change the site’s home page?

And, if you have a GDPR compliant login mechanism…Does your machine learning-powered ecommerce platform leverage the lifetime of my site experience, complaints, purchases, etc., to anticipate my activity?

Do the pages on your site wrap around my objectives, rather than your static and pimpy ones?

Is your entire sales strategy obsessed with the Do, or does it also obsess about the See, Think and Care bits of the complete human experience?

Personalization is the ultimate nudge—to create ecommerce-related urgency and to bring your brand closer to the customer over the lifetime of their experience with you.

That’s because personalization means truly caring. Personalization requires a huge investment in understanding. Personalization is translating that individual human-level understanding into anticipation. Personalization means helping. And when you do it right, personalization means you pimp with relevance—the best kind.

The desire to personalize across the complete human experiences kicks off the processes that fundamentally alter how you treat every human. The reason it works, when done right, is that deep down, we want people to care about us. And yes, we will end up doing more business with people who show that they care for us. Really care. The ultimate nudge.

So. If you own www.canada.ca or www.sainsbury.co.uk using PII or non-PII information… Does your site actively learn and then change? If not, why not?

One huge challenge we had to overcome in delivering personalization was employee capabilities. Employees are terrible at being able to imagine the expanse of possibilities when it comes being able to understand each human and being able to react to each human. Mercifully, Machine Learning (/Artificial Intelligence) will help us solve this challenge with incredible results.

Bottom-line.

You can pray that your conversion rates increase.

Alternatively, you can take advantage of the data you have access to, the permissions your users have given you, and the competitive advantages you’ve worked so hard to create and use them to create nudges that solve for delivering delight to your customers and more revenue to your company.

Your choice?

Nudging FTW!

As always, it is your turn now.

If you’ve tried one of the above six strategies to create a nudge, what was the outcome for your company? If you’ve seen a strategy for creating urgency that you love, will you please share it? What challenges have you run into in trying to personalize experiences? Nudging also works in our personal lives—have you tried it? 🙂

Please share your critiques, brilliant ideas and experience scars via the comments below.

PS: My doctor reminds me during every annual visit that I need to take more walks outside in the sun to make up for a vitamin deficiency. Turns out I spend too much time in my office or auditoriums. The sun is right there. I just need to take a walk. I still do it less than I should. Such is the case with A/B testing. The tools are free and abundant. You know they are the best way to win arguments with your HiPPOs or your cubicle mates. Yet, you don’t use them. I’m off to take a walk in the beautiful California sun, you go implement my recommendations for nudges as A/B tests—it is the only way to unlock the kind of imagination required to create profitable happy customer experiences.



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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 Artificial Intelligence Opportunity: A Camel to Cars Moment

Two_Focus_AreasOver the last couple years, I’ve spent an increasing amount of time diving into the possibilities Deep Learning (DL) offers in terms of what we can do with Artificial Intelligence (AI). Some of these possibilities have already been realized (more on this later in the post). And, I could not be more excited to see them out in the world.

Through it all, I’ve felt there are a handful of breath-taking realities that most people are not grasping when it comes to an AI-Powered world. Why the implications are far deeper for humanity than we imagine. Why in my areas of expertise, marketing, sales, customer service and analytics, the impact will be deep and wide. Why is this not yet another programmatic moment. Why the scale at which we can (/have to) solve the problems is already well beyond the grasp of the fundamental strategy most companies follow: We have a bigger revenue opportunity, but we don’t know how to take advantage? Let’s buy more hamster wheels, hire more hamsters and train them to spin faster!

Today I want shed some light on these whys, and a bit more. My goal is to try to cause a shift in your thinking, to get you to take a leadership role in taking advantage of this opportunity both at a personal and professional level.

I’ve covered AI earlier: Artificial Intelligence: Implications On Marketing, Analytics, And You. You’ll learn all about the Global Maxima, definitions of AI/ML/DL, and the implications related to the work we do day to day. If you’ve not read that post, I do encourage you to do so as it will have valuable context.

In this post, I’ve organized my thoughts into these six clusters:

There is a deliberate flow to this post, above. If you are going to jump around, it is ok, but please be sure to read the section below first. You won’t regret it.

Ready to have your mind stretched? Let’s go!

What’s the BFD?

I’m really excited about what’s in front of us. When I share that excitement in my keynotes or an intimate discussion with a company’s board of directors, I make sure I stress two especially powerful concepts that I have come to appreciate about the emerging AI solutions: Collective Continuous Learning + Complete Day One Knowledge.

They are crucial in being able to internalize the depth and breadth of the revolution, and why we strengths AI brings are a radical shift beyond what humans are capable of.

The first eye-opening learning for me came from the Google Research team’s post on Learning from Large-Scale Interaction.

Most robots are very robotic because they follow a sense-plan-act paradigm. This limits the types of things they are able to do, and as you might have seen their movements are deliberate. The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models).

The one-handed robots in this case had to learn to pick up objects.

Initially the grasping mechanism was completely random – try to imagine a baby who barely knows they even have a hand at the end of their shoulder. Hence, you’ll see in the video below, they rarely succeed at the task at hand. 😉

At the end of each day, the data was collected and used to train a deep convolutional neural network (CNN), to learn to predict the outcome of each grasping motion. These learnings go back to the robot and improve its chances of success.

Here’s the video…

[youtube https://www.youtube.com/watch?v=iaF43Ze1oeI?ecver=1]
(Play on YouTube)

It took just 3,000 robot-hours of practice to see the beginnings of intelligent behavior.

What’s intelligent behavior of a CNN powered one-handed robot?

Among other things, being able to isolate one object (a stapler) to successfully pick-up a Lego piece. You’ll see that at 15 seconds in this video…

[youtube https://www.youtube.com/watch?v=l8zKZLqkfII?ecver=1]
(Play on YouTube)

Or, learning how to pick up different types of objects (a dish washing soft sponge, a blackboard eraser, or a water glass  etc.).

I felt a genuine tingling sensation just imagining a thing not knowing something and it being able to simply learn. I mean pause. Just think about it. It started from scratch – like a baby – and then just figured it out. Pretty damn fast. It truly is mind-blowing.

There were two lessons here. The first related to pure deep learning and its amazingness, I was familiar with this one. The second was something new (for me). This experiment involved 14 one-handed robot arms. While not a massive number, the 14 were collectively contributing data from the start – with their many failures. The end of day learnings by the convolutional neural network were using all 14. And, the next day, all 14 started again with this new level of collective wisdom.

For a clear way for me to capture this lesson, I call this Collective Learning.

It is very powerful.

Think of 14 humans learning a new task. Peeling an apple. Or, laying down track for a railroad. Or, programming a new and even more frustrating in-flight entertainment menu for Air Canada (who have the worst one known to mankind).

Every human will do it individually as well as they can – there will be the normal bell curve of competency. It is entirely possible, if there are incentives to do so, that the humans who are better in the group will try to teach others. There will be great improvement if the task is repetitive and does not require imagination/creativity/intrinsic intelligence. There might be a smaller improvement if the task is not repetitive and requires imagination/creativity/intrinsic intelligence.

In neither case will there be anything close to Collective Learning when it comes to humans.

Humans also do not posses this continuous closed loop: Do something. Check outcome (success or failure). Actively learn from either, improve self. Do something better the next time.

Collective Continuous Learning. An incredible advantage that I had simply not thought through deeply enough.

Here’s the second BFD.

Machine Learning is already changing lots of fields, the one I’m most excited about is what’s happening in healthcare. From the ability to speed up discovery of new medicines to the unbelievable speed with which Machine Learning techniques are becoming particularly adept at diagnosis (think blood reports, X-rays, cancers etc.). 

An example I love. 415 million diabetic patients worldwide are at risk of Diabetic Retinopathy (DR) – the fastest growing cause of blindness. If caught early, the disease is completely treatable. The problem? Medical specialists capable of detecting DR are rare in many parts of the world where diabetes is prevalent.

Using a dataset of 128,000 images Google’s  Accelerated Science Team trained a deep neural network to detect DR from retinal photographs. The results delivered by the algorithm (black curve) were slightly better than expert ophthalmologists (colored dots)…

diabetic_retinopathy_deep_learning_algorithm_results

Specifically the algorithm has a F-score of 0.95 and the median F-score of the eight expert ophthalmologists was 0.91.

As richer datasets become available for the neural network to learn from, as 3D imaging technology like Optical Coherence Tomography becomes available all over the world to provide more detailed view of the retina, just imagine how transformative the impact will be.

Literally millions upon millions of people at risk of blindness will have access to AI-Powered technology that can create a different outcome for their life  – and their families.

#omg

A recent incredible article on this topic is in my beloved New Yorker magazine: A.I. VERSUS M.D. You *should* read it. I’ll jump to a part of the article that altered my imagination of possibilities.

An algorithm created by Sebastian Thrun, Andre Esteva and Brett Kuprel can detect keratinocyte carcinoma (a type of skin cancer) by looking at images of the skin (acne, a rash, mole etc.). In June 2015 it got the right answer 72% of the time, two board-certified dermatologists got the right answer for the same images 66% of the time.

Since then, as they outlined in their report published in the prestigious journal Nature, the algorithm has gotten smarter across even more skin cancer types – and consistently performs better than dermatologists.

Most cancers are fatal because they are detected too late, just imagine the transformative impact of this algorithm sitting in the cloud easily accessible to all humanity via their five billion smartphones. This dream come true: low-cost universal access to vital diagnostic care.

Oh, and here’s a profoundly under-appreciated facet of all this. These health algorithms (including and beyond the one above), are incredible at corner cases, the rare long-tail anomalies. They don’t forget what they have seen once or “rarely.”

This is just a little bit of context for the key point.

A dermatologist in a full-time practice will see around 200,000 cases during her/his lifetime. With every case she sees, she’ll ideally add to her knowledge and grow her diagnostic skills.

Our very human problem is that every new dermatology resident starts almost from scratch. Some textbooks might be updated (while comfortably remaining a decade of more behind). Some new techniques – machines, analytical strategies – might be accessible to the resident. But, the depth and breadth of knowledge acquired by the dermatologist at the end of her career with 200k cases, is almost completely inaccessible to the new resident. Even if they do a residency at an hospital or with a old dermatologist, a newly minted dermatologist will only be a little better than when the old one left school.

Consider this instead: The algorithm above processed 130,000 cases in three months! And every day it will get smarter as it’ll have access to the latest (and more) data. Here though is the magical bit. Every single new algorithm we bring online will have total access to all knowledge from previous algorithms! It’s starting point will be, what I call, Complete Day One Knowledge.

As it gets more data to learn from, as it has access to more compute power, it will get smarter and build upon that complete knowledge. The next version of the algorithm will start with this new high mark.

There is nothing equivalent to Complete Day One Knowledge when it comes to humans.

Combine having Complete Day One Knowledge with Collective Continuous Learning (networked hardware or software all learning at the same time) and it should take you five seconds to realize that we are in a new time and place.

Whatever form AI takes, it will always have access to complete knowledge and through the network each instance will make all others smarter every single instance/moment of its existence.

Humans simply can’t compete.

That’s the BFD.

Stop. Think. If you disagree even slightly, scroll back up and read the post again.

It is imperative that you get this not because of what will happen in 10 years, but what is happening today to the job you have. If you still disagree, scroll down and post a comment, I would love to hear your perspective and engage in a conversation.

deepmind_relational_reasoning

Bonus 1: There is an additional valuable lesson related to open-loop grasp selection and blindly executing it vs. incorporating continuous feedback (50% reduction in failure rates!). The two videos are worth watching to see this in action.

Bonus 2: While we are on the subject of objects… Relational reason is central to human intelligence. Deepmind has had recent success in building a simple neural network module for relational reasoning. This progress is so very cool. Additionally, I was so very excited about the Visual Interaction Network they built to mimic a human’s ability to predict. (If you kick a ball against the wall, your brain predict what will happen when the ball hits the wall.) The article is well worth reading: A neural approach to relational reasoning. Success here holds fantastic possibilities.

Wait. So are we “doomed”?

It depends on what you mean by doomed but: Yes. No. Yes, totally.

Artificial Intelligence will hold a massive advantage over humans in the coming years.

In field after field due to Collective Continuous Learning and Complete Day One Knowledge (not to mention advances in deep learning techniques and hardware :)), AI will be better at frequent high-volume tasks.

Hence, the first yes.

Neuralink at the moment is a concept (implantable brain-computer interface). But many experts (like Ray Kurzweil) believe some type of connection between our human brain and “intelligence, data, compute power in the cloud” will be accessible to humans.

I humbly believe that when that happens, over the next few decades (think 2050), humans could get to parity with AI available at that time. We might even have an advantage for some time (if only because I can’t let go of the thought that our brains are special!).

Hence, the no.

As we head towards the second half of the current century, AI will regain the lead again – and keep it for good. I don’t have the competency to judge if that will be AGI or Superintellignece or some other variation. But, with all other computing factors changing at an exponential rate it is impossible that intelligence will not surpass the limitations of humans and human brains (including the one with a version of Neuralink).

Here’s just one data-point from Jurgen Schmidhuber: Neural networks we are using for Deep Learning at the moment have around a billion neural connections compared with around 100,000 billion in the human cortex. Computers are getting 10 times faster every 5 years, and unless that trend breaks, it will only take 25 years until we have a recurrent neural network comparable with the human brain. Just 25 years.

Hence, the yes totally.

I have a personal theory as to what happens to humans as we look out 150 – 200 years. It is not relevant to this post. But, if you are curious, please ask me next time you see me. (Or, sign up for my weekly newsletter: The Marketing < > Analytics Intersect)

AI: A conversation with a skeptic.

Surely some of you think, to put it politely, that I’m a little bit out there. Some of you’ve heard the “hype” before and are deeply skeptical (AI went through a two decade long tundra where it failed to live up to every promise, until say 2010 or so). Some of you were promised Programmatic was AI and all it did was serve crap more efficiently at scale!

I assure you, skepticism is warranted.

Mitch Joel is the Rock Star of Digital Marketing, brilliant on the topic of media, and a very sweet human being. Amongst his many platforms is a fantastic podcast called Six Pixels of Separation. Our 13th podcast together was on AI. Mitch played the role of the resident skeptic and I played the role of, well, the role you see me play here.

If you can think of a skeptical question on this topic, Mitch asked it. Give the podcast a listen…

[soundcloud url=”https://api.soundcloud.com/tracks/338674346″ params=”color=ff5500″ width=”100%” height=”166″ iframe=”true” /]

(Play at Six Pixels of Separation)

As you’ll hear multiple times, a bunch of this is a matter of thinking differently about the worldview that we’ve brought with us thus far. I share as many examples and metaphors I could to assist you in a journey that requires you to think very differently.

If you are still skeptical about something, please express it via comments below. Within the bounds of my competency, I’ll do my best to provide related context.

Ok, ok, ok, but what about the now? (Professional)

While I look at the future with optimism (even 150 years out for humans), what I’m most excited about is what Machine Learning and Deep Learning can do for us today. There are so many things that are hard to do, opportunities we don’t even know exist, the ability to make work that sucks the life out of you easier, better, smarter, or gone.

In a recent edition of my newsletter, TMAI, I’d shared a story and a call to arms with specific recommendations of what to do now. I’ll share it with you all here with the hope that you’ll jump-start your use of Machine Learning today…

I lived in Saudi Arabia for almost three years. Working at DHL was a deeply formative professional experience. My profound love of exceptional customer service, and outrage at awful customer experiences, can be directly sourced to what I learned there.

Saudi Arabia is a country that saw massively fast modernization. In just a few years, the country went from camels to cars. (I only half-jokingly say that Saudis still ride their cars like camels – and it was scary!).

Think about it for a moment.

From camels to cars. No bicycles. No steam engines. None of the other in-betweens other parts of the world systematically went through to get to cars. They were riding camels, then they were riding cars. Consider all the implications.

We stand at just such a moment in time in the business world. You know just how immersed and obsessed I am with Artificial Intelligence and the implications on marketing and analytics. It truly is a camels to cars type moment in my humble opinion (it might even be a camels to rockets moment, but let me be conservative).

Yet, executives will often give me examples of things they are doing, and they feel satisfied that they are with it, they are doing AI. When I probe a bit, it becomes clear very quickly that all they are doing is making the camels they are riding go a little faster.

That all by itself is not a bad thing – they are certainly moving faster. The problem is they are completely missing the opportunity to get in the car (and their competitors are already in cars).

It is important to know the difference between the two – for the sake of job preservation and company survival.

Here are a handful of examples to help you truly deeply internalize the difference between these two critical strategies…

If you are moving from last-click attribution to experimenting with first-click or time-decay, this is trying to make your camel go faster. Using ML-Powered Data-Driven Attribution and connecting it with your AdWords account so that action can be taking based on DDA recommendations automatically, you are riding a car.

(More on this: Digital Attribution’s Ladder of Awesomeness)

If you are moving to experimenting with every button and dial you can touch in AdWords so that you can understand how everything works and you can prove increase in conversions while narrowly focusing on a few keywords, you are making your camel go faster. Switching to ML-powered Smart Targeting, Smart Creative and Smart Bidding with company Profit as the success criteria, for every relevant keyword identified automatically by the algorithm, you are riding a car.

Staffing up your call center to wait for calls from potential customers is making your camel go faster. Creating a neural-network that analyzes all publicly available data of companies to identify which ones are going to need to raise debt, and proactively calling them to pitch your company’s wonderful debt-financing services is riding a car.

Hand picking sites to show your display ads via a x by x spreadsheet that is lovingly massaged and now has new font and one more column on Viewability, is making your camel go faster. Leveraging Machine Learning to algorithmically figure out where your ad should show by analyzing over 5,000 signals in real time for Every Single Human based on human-level understanding (die cookies die!), is riding a fast car.

(To see a delightful rant on the corrosive outcomes from a Viewability obsession, and what you might be sweeping under the carpet, see TMAI #64 with the story from P&G.)

Asking your Analysts to stop puking data, sorry I mean automate reporting, and send insights by merging various data sets is making the camel go faster. Asking your Analysts to just send you just the Actions and the Business Impact from those Actions is riding a car. Asking them to shift to using ML-powered products like Analytics Intelligence in GA to identify the unknown unkonwns and connecting that to automated actions is riding a rocket.

If you are explicitly programming your chatbot with 100 different use cases and fixed paths to follow for each use case to improve customer service, that is making the camel go faster. If you take the datasets in your company around your products, problems, solutions, past successful services, your competitors products, details around your users, etc. etc. and feed it to a deep learning algorithm that can learn without explicit programming how to solve your customer’s service issues, you are riding a car.

I, literally, have 25 more examples… But, you catch my drift.

I do not for one moment believe that this will be easy, or that you’ll get a welcome reception when you present the answer. But, one of two extremely positive outcomes will happen:

1. You’ll get permission from your management team to stop wasting time with getting the camel to go faster, and they’ll empower you to do something truly worth doing for your company. Or…

2. You’ll realize that this company is going to suck the life out of your career, and you’ll quietly look for a new place to work where your life will be filled with meaning and material impact.

Win-Win.

Hence, be brutally honest. Audit your current cluster of priorities against the bleeding edge of possible. Then answer this question: Are you trying to make your camel go faster, or jumping on to a car?

While Machine Learning has not solved world hunger yet, and AGI is still years away, there are business-altering solutions in the market today waiting for you to use them to create a sustainable competitive advantage.

Ok, ok, ok, but what about the now? (Personal)

If this post has not caused you to freak-out a tiny bit about your professional path, then I would have failed completely. After all, how can the huge amount of change mentioned above be happening, and your job/career not be profoundly impacted?

You and I have a small handful of years when we can create a personal pivot through an active investment of our time, energy and re-thinking. If we miss this small window of opportunity, I feel that the choice will be made for us.

This blog is read by a diverse set of people in a diverse set of roles. It would be difficult to be personal in advice/possibilities for each individual.

Instead, here’s a slide I use to share a collection of distinct thought during my speaking engagements on this topic…

machines_humans_jobs_avinash

In orange is a summary of what “Machines” and humans will be optimally suited for in the near-future. (Note the for now.) Frequent high-volume tasks vs. tackling novel situations.

In green, I’m quoting Carlos Espinal. I loved how simply and beautifully he framed what I imagine when I say tackle novel situations.

Over the last 24 months, I’ve made an whole collection of conscious choices to move my professional competencies to the right of the blue line. That should give me a decade plus, maybe more if Ray is right about Cloud Accessible Intelligence. Beyond that, everything’s uncertain. 🙂

Summary.

I hope you noticed I ended the above paragraph with a smiley. I’m inspired by the innovation happening all around us, and how far and wide it is being applied. I am genuinely excited about the opportunities in front of us, and the problems we are going to solve for us as individuals, for our businesses, for our fellow humans and for this precious planet.

In my areas of competence, marketing, analytics, service and sales, I can say with some experience that change is already here, and much bigger change is in front of us. (I share with Mitch above how long I think Analysts, as they are today, will be around.) I hope I’ve convinced you to take advantage of it for your personal and professional glory.

(All this also has a huge implication on our children. If you have kids, or play an influencing role in the life of a child, I’d shared my thoughts here: Artificial Intelligence | Future | Kids)

The times they are a changin’.

Carpe diem!

As always, it is your turn now.

Were Collective Learning and Complete Day One Knowledge concepts you’d already considered in your analysis of AI? Are there other concepts you’ve identified? Do you think we are doomed? Is your company taking advantage of Deep Neural Networks for marketing or analytics or to draw new value from your core back-office platforms? What steps have you taken in the last year to change the trajectory of your career?

Please share your insights, action-plans, critique, and outlandish predictions for the future of humanity, :), via comments below.

Thank you.

The Artificial Intelligence Opportunity: A Camel to Cars Moment is a post from: Occam’s Razor by Avinash Kaushik


Source: Avinash

Stop All Social Media Activity (Organic) | Solve For A Profitable Reality

Life is short.

It is time to point out an ugly truth, and to be the brave person that you are, the intelligent rational assessor of reality that you are, and kill all the organic social media activity by your company.

All of it.

Seems radical, but let’s take it one step at a time.

To give you a sense of the depth and breadth of ideas I’ll cover today, here are the sections in this post:

I urge you to have an open mind. My plan is to challenge your critical thinking skills, and share lessons that will apply broadly across the professional effort you put day in and day out. Most of all, I’m excited to frame an important problem, and present solutions that will transform an important part of your marketing strategy.

Let’s go!

The Promise of Marketing Utopia. 

I hate pimping (what marketing has come to be). I adore building meaningful relationships – the kind of long-term connections where a brand truly gives a f about their customers, and gives something of value in exchange for their attention. I LOVE brands that can pull this off, and support them with my un-asked-for evangelism and precious $$$s.

Hence, you can imagine how gosh darn excited I was at the advent of Facebook and Twitter (first real social networks). There were a billion people there, spending a meaningful amount of time on these wonderful platforms. Excitedly, brands could have a presence (a “page”) where they could contribute meaningful updates (info-snacks) in order to be a part of the organic conversations people were already having by the tens of millions.

Daily meaningful brand connections would be converted into brand familiarity, shifts in brand perception, feeding brand loyalty. #orgasmic

If you were a travel company, meaningful would now translate into helping feed wanderlust. The company could contribute info-snacks about where people should go, exposing the coolest places in the world, helping people travel better via tips, pictures, videos… you know… communicating travel love. The one thing a travel company would have in common with travel customers. The most imaginative travel marketers could even extend this opportunity to helping connect the purpose of their existence, selling tickets and hotel rooms, to helping people create moments of happy by crafting day/s of escape from the rough and tumble of life.

Glorious, right? If you work at Expedia or Cathay Pacific, does that not make you want to come to work and, for at least a part of your employment, create meaning? How rare is that!

If you were Cisco, meaningful would mean sharing info-snacks whose entire purpose could be to get Engineers promoted. Share tips, ideas, schematics, usage shortcuts, creative implementations, solutions to top problems that hold Engineers back… you know… understanding your audience deeply and give them something of value in exchange for their attention. The most imaginative B2B marketers could even figure out how to be a part of solving some of the deepest entrenched problems in the industry (STEM education, equal opportunity, + +) and in turn add an entire value-system to their brands.

Amazing, right?

Marketing based on something real, rather than a coupon or company brochure.

The Broken Promise of Marketing Utopia, Implications. 

None of the above transpired on Social platforms.

Businesses of all types, including Google (SMB, Main), got on amazing platforms like Facebook (and Weibo, Instagram, Pintrest etc.) and started pimping. All that their collective imagination could manifest in a Utopia-possible environment was: LOOK ME I AM SO PRETTY!! BUY NOW!!!

Stuff that is a turn off.

Consider the Google’s first FB page above, it is a complete disaster with not a single post in the last six months being of even five seconds of value to any small business. That page, or the main one, is not an overt Buy Now, but if you think critically like the tough Marketer I want you to be you’ll have a hard time finding a single post that’s solving for Google’s human customers. Almost every single one is pimping Google (or pimping random research Google has commissioned – to pimp Google!). The non-value is so transparent, yet they post every single day something that basically is solving for Google (although only God knows what that is). If someone bothers to interact with the post, the posted comment is a spam or totally useless. Yet. They keep posting. Polluting utopia.

Google is not unique in not understanding the promise, checkout your company’s FB page.

This strategy by businesses lead to what I now call the Zuck Death Spiral. ZDS.

Real humans on Social platforms quickly got turned off by these low-grade Social contributions/posts by companies. That meant humans (us!) refused to engage with them. This was noticed by Team Zuck, who started to slowly turn down the presence of company posts in User feeds. This lead to less Reach for brands. Which in turn lead to even fewer customer interactions for content posted by brands. Which was duly noted once more by Team Zuck. Which… you know where this is going, tightened the screws on organic Reach even more. And, here we are in a barren desert for brands on FB.

Most brands get less than 1% Reach via their organic contributions on social platforms. And, less than 1% engagement of any kind from that less than 1% reached (identified using the best social media metrics: Conversation Rate, Amplification Rate, Applause Rate).

ZDS is solving for FB, as FB should, and it is an attempt to solve for FB’s users.

So… If all you can do is overtly or covertly pimp… And, pimping is not cheap (that Google page, and your company’s page, has pictures, videos, an agency deployed, internal company employees with a “social media execution checklist”, senior leadership time committed, and more)… And, all it does is get you 1% Reach, max, with almost no engagement… Why do you still have an active (organic) social media effort?

Why is this reality not smacking some sense into your marketing strategy?

The Broken Promise of Marketing Utopia: Examples. 

Is it difficult to check if your brand is caught up in the Zuck Death Spiral? No.

Do you have access to any data to measure how deeply non-impactful your organic Social Media efforts are? OMG, yes.

Everything you need, data and information, to do an audit is public.

All you have to do is visit your company’s Facebook page (or Instagram, LinkedIn, Pinterest, etc. presence).

Let me show you what to look for. Let’s start with Expedia. They have 6.4 million Likes as of today. Go look at any post on the page if you are an Expedia employee.

expedia_facebook

First thing you’ll look at is the Applause Rate (likes, other emotions, you’ll see it right under the photo). That number is 75. Divide that by 6,462,977 (potential audience size today).

0.00113%. That’s a painful stab in your heart.

Next Conversation Rate (comments, you’ll see a total at the end of your posts). 7. Divide that by 6,462,977. A sad 0.00011%.

Finally, my favorite sign that you truly added value to a human rather than pimp, Amplification Rate (shares). 3/6,462,977. At this point you are weeping with me: 0.00005%.

To give you some context as to how insanely lame these numbers are, Expedia.com received 59,400,000 Visits in May 2017. This post accomplished 75+7+3. More people walk into the Expedia lobby in Bellevue, WA, every second of every minute.

You might be screaming that is not fair Avinash, the Zuck Death Spiral ensures that a tiny fraction of 6,462,977 are seeing Expedia’s posts! Very fair point. But, is the Social Media Budget at Expedia not justified based on the potential from 6,462,977? Would Expedia commit it’s multi-million-dollar budget to Social Media based on the potential to engage 75+7+3 people on Planet Earth?

One final point. Brand destruction.

Pretty much every single comment on pretty much every single Expedia post is a complaint about how horrible Expedia is (from personal experience I know this is not true). If your Facebook presence is solely to inspire people (see Trish Sayler above) to create clever rhymes about how bad you are… Why are you on Social Media?

Ignore the active smearing of the Expedia brand, let’s go back to data: Is it worth have 75 | 7 | 3 as the value delivered from an organic Social Media strategy for a company with 54,900,000 Visits?

My answer is an emphatic no. Expedia should immediately cease 100% of its organic Social activity.

1/100th of the Social Media budget could be spent on any other random digital strategy to get 75+7+3, and have zero brand destruction!

Oh. And while I’m focusing on Facebook for the sake of simplicity, everything in this post applies to all other Social Media channels. The Utopia failures. The lack of imagination. The small numbers. The uselessness.

Here for example is a post on Twitter by Expedia:

expedia_twitter

The numbers: 9 | 2 | 2. Divided by 391,000 (followers).

You can do the math and assess dent in the universe this content contribution from Expedia is making.

Almost nothing. Technically, perhaps less than nothing.

I hate making recommendations based on outliers, please know that Expedia is the norm. Hence, the title of this blog post.

Here’s a B2B example, a company I think well of… Cisco.

cisco_facebook

Go through the same analysis.

Your numbers are 31 | 1 | 3. Divided by 845,921.

Would you spend a single hard-earned Cisco router and switches dollar to get this as the return from a multi-million dollar Social Media budget?

Like my company, your company, and Expedia, Cisco gets no value from their organic Social Media efforts. Technically, Cisco is getting negative returns once you account for the people, process, tools, agency, leadership investments.

Let’s switch gears and look at a B2C company with a massively positive opportunity to leverage the word Social in every way on these platforms… Chick-fil-A.

chick-fil-a_facebook

Better numbers, as you might expect.

1k | 89 | 73. Divided by 7,775,155.

Consider it. Chick-fil-A could buy the most remnant TV inventory on a channel least watched by humans during the middle of the night and get better Reach. And they can also measure how many of them walked into a Chick-fil-A in the next 12 hours.

Does the above number justify custom videos, images, active posting by Click-fil-A on Facebook?

One final example to bring this home.

ProjectManager.com is a lovely tool. It is wonderful that they use folks like Jennifer Bridges, Susanne Madsen and others to create very helpful Project Management videos on YouTube. It seems they are a medium-sized business.

Here’s their Facebook page:

project_manager_facebook

69 | 0 | 25. Divided by 62,951.

Pound for pound, better performance than all three (four including Google) companies above. Shame on them.

Still. Are the resulting Applause Rate, Conversation Rate and Amplification Rate enough for a smaller business to use it’s precious marketing dollars on this Social Media strategy/impact?

Consider this as well for all brands… There is no native discovery model on these Social channels. Your content will live for 20 minutes and then it is dead. Not just because of ZDS, but also because there is no Search behavior by users or a method that would deliver Serendipitous Discovery of content you post.

Unlike say on YouTube, or your Blog, where your Subscribers will see the content right away, and then through Bing and Yandex and YouTube itself people will find your content when relevant and keep viewing it. Your content there has a live beyond 20 minutes.

Win Big: Stop Posting Content for Organic Reach On Social Channels. 

Given the numbers above, and be sure to check any other Social Media channel your company is actively investing in, I hope you have the input you need to apply your critical thinking skills.

Let me give you one final push: You have better alternatives to drive short and long-term Profitability for your company (rather than investing in organic Social Media).

Here’s an example.

I write an insightful newsletter with the singular aim of improving your salary. The Marketing < > Analytics Intersect. You should sign up. It is a companion to this blog, I write once a week there and once a month here.

One year into it’s existence, TMAI has 21,246 Subscribers.

Measuring Open Rates for email is difficult (the tiny pixel ESPs use to track opens are not executed by default for most email programs). Even with that flaw in reporting, TMAI has Open Rates of around 9,000 (9,895 precisely for the last one).  Around 1,000 people (912 for the last one) take an action that is of value to me.

A random person, me, can get 9,000 opens of my content, at least a thousand active engagements with my brand whenever I want. I have over 1,000,000 Social Media followers across the five platforms (Twitter, Facebook, LinkedIn, Google+, Instagram). I can’t even get 1/100th the impact.

My simple unsexy email newsletter strategy crushes the on paper potential of one million Social Media followers.

And, beyond the impact… I also directly own the relationships with my 21,246 Subscribers, I own the data, the relationship exists on my platform, and I can use it as creatively I want to use it with no limitation on type of content (text or video or dancing penguin gifs).

Why should your company be on Social Media 5x per day to get a lousy 20 interactions with your brand? How is that acceptable ROI from your investment in a 5 person Social Media team, a Social Media Agency, a Social Media analytics tool, a Social Media auto-posting tool and more?

Could you not get 100x ROI from the 0.25 person that’s running your email newsletter?

Could you not just take all that Team, Agency, Tool, money, throw it into AdWords or AOL Display Ads and not get massively higher ROI, of any kind, in 10 minutes?

Could you not get better ROI taking all that money and buying remnant inventory on your local Television channel?

Could you not get better ROI if you just took that money and bought free lunch for the employees in your building every other day?

OMG, you most definitely can.

So. Why are you on Social Media?

Is it fun to shout in a vacuum?

Why does it not feel dirty to go waste your shareholder’s money?

Stop it then.

Welcome to the world of higher standards for impact delivered. Feel cleaner and prouder coming to work every day as a Marketer/CMO.

Is the Huge Audience on Social Media Platforms Completely Useless? 

NO!

There are a couple of billion people on Facebook (and billions or hundreds of millions on other Social channels). From an advertising perspective, that’s still an audience that might be of value to your business.

Kill your organic Social strategy completely, switch to a paid Social Media strategy.

Buy advertising from Facebook. I’ll make it easy, click this link!

Buy advertising from Twitter. From Snapchat. LinkedIn. Oh and WeChat and Line.

This simple switch from the fuzzy Organic goals to concrete Paid goals will give the one thing your Social Media Marketing strategy was missing: Purpose.

It is now easy to define why the heck are you spending money on Social Media? To drive short and medium-term brand and performance outcomes.

Fabulous.

Set aside the useless metrics like Impressions and 3-second Video Views. Set aside hard to judge and equally useless Like and Follow counts. Measure the hard stuff that you can show a direct line to company profit.

Define a purpose for the money you are spending.

For the clients I’ve worked with across the world, expressed behavior of the users suggests that the largest cluster of intent is See. There is a little bit of Think and a little bit of Care. (This is why Social marketing strategies that target Do intent yield extremely poor results.)

[Bonus Read: See-Think-Do-Care Business Framework]

If the purpose is to execute See and Care intent marketing strategies (in the old world sometimes incompletely referred to as brand marketing), you can use the following amongst my favorite metrics to deliver accountability:

1. Unaided Brand Recall
2. Likelihood to Recommend
3. Lift in Purchase Intent
4. Shift in Brand Perception (negative to neutral, neutral to positive, positive to proactive evangelism)
5. Lifetime Value

Humans have measured these using primary and secondary research methods for 3,500 years. Quite easy to do the same for your newly focused paid Social advertising efforts.

[Bonus Read: Brand Measurement: Analytics & Metrics for Branding Campaigns]

If on the other hand the purpose of your paid Social advertising is to target Think and/or Do intent, you should measure the impact using the following across your digital – and pan-digital presence:

1. Recency & Frequency
2. Loyalty
3. Task Completion Rate
4. Assisted Conversions
5. Macro-Outcomes Rate
6. Economic Value

We have measured these for a long time on the web. You can use your quantitative tools to measure most of these (Google Analytics, Adobe, True Social Metrics). And. You can measure these for your ecommerce, non-ecommerce, B2B, B2C, pure content, non-profit, or whatever else kind of delicious business you are running.

Now, you’ll hold your agency and employees accountable for delivering business profitability for your Social efforts just as you do for any other advertising effort – Search or TV or Email.

Just as you would do in all those other cases, do more paid Social advertising if the metrics show a business impact and improve/eliminate your paid Social efforts if they don’t.

It will mean a different Social content strategy, different targeting strategy (leveraging rich Social signals), and a different landing page/app strategy. Proper end-to-end user and business optimization. Nirvana, delivered by that magical word… Purpose.

The path to your salary and job promotion is also now crystal-clear. Right?

Is the Idea of Marketing Utopia Permanently Dead? 

I’ve seen the near-future, and I believe we’ll get to Utopia Marketing.

The fact that companies don’t know how to be human, how to take even 20% of their people plus budget and invest optimally in understanding humans and deliver something of value to those humans is deeply heartbreaking.

Yes, I can blame the short-term quarterly focus of the CMOs and the SELL, SELL, SELL MORE incentives they create for you to earn your bonus. But still, how heartbreaking is it that not even 1% of us could convince our CMOs to allow us to do what Social was actually good at? How sad is it that we have such little influence? I blame us.

Still. I am optimistic that Marketing Utopia, as I’ve imagined it at the top of this post, is not dead. I think the solution will be to get rid of the humans from the process!

What? Human marketing by getting rid of humans?

Yes. Hear me out.

I think AI/Machine Learning will solve this problem.

Today, humans and their limited ability to process data, and the finite incentives in place, are the reason we burned Utopia to the ground. We simply can’t process billions of signals across tens of millions of touch points across millions of people, and figure out the best message at every moment and its short, medium, and long-term business value.

Current advances in ML already give me hope that algorithms will understand intent a billion trillion times better than your current employees AND these algorithms will have the inherent capabilities to process billions of data points to truly understand complex patterns of user behavior and a robust understanding across all that to know exactly what delivers business profit.

Companies can then take the equivalent of their Brand and Social budgets and allow smarter algorithms to deliver the right message to the right person at the right time across all clusters of intent. All the while, optimizing for long-term business profitability.

It will help that Machine Learning is not embolden to trivial company politics. 🙂

[Bonus Read: Artificial Intelligence: Implications On Marketing, Analytics, And You]

Bottom-line.

While I’m recommending you stop doing something, hearing no is not super-inspiring, I hope you’ll see that my goal is help you think more critically about where you spend your personal time and your company’s money.

I also hope you’ll see how the shift in strategy I’m recommending brings Social in line with your other advertising efforts, allowing for a ton more focus on your Social efforts and a billion times more accountability.

Finally, I hope you feel optimistic that around the horizon lurk technological solutions that will allow for the manifestation of the beautiful humanity that exists in your company (even if we have to take human employees out of the equation to get there – don’t worry, they’ll still, for now, be responsible for the novel elements required).

Demand more from Social, because Social can deliver more. It just happens to be paid Social.

Oh… And if you’ve chosen to define your professional career as a Social Media Analyst or a Social Media Guru or a Social Media Marketer, I respectfully offer that you should rethink your strategy. You likely already see deep pressure on the possibilities in front of you, and on your compensation growth. This will only get more severe. Figure out how to expand your skill-set, and then scope of influence/impact, so that you can delete the first two words from each of those titles and retain the last one. If you are remotely good at what you do, you’ll be in a recession-proof digital career. The opportunity is there, your career trajectory and compensation growth will be up and to the right.

As always, it is your turn now.

If you’ve achieved sustained success from your organic Social Media content strategy, would you please share your example? If you disagree and believe Marketers should invest in organic Social despite poor Reach, ApR, CoR, and AmR, would you please share how you see value/impact? If you’ve successfully dumped organic and pivoted to paid Social, please share stories of your victory. Are you as optimistic as I am that Machine Learning based intelligence will solve optimally for the Utopia opportunity?

I look forward to hearing your smart perspectives and cogent challenges.

Thank you.

Stop All Social Media Activity (Organic) | Solve For A Profitable Reality 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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