Category: Op-Ed

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

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

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

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

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

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

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

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

The Implications of Complexity.

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

1. Obvious:

2. Non-obvious:

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

Simplifying Complexity.

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

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

The hunger remained.

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

Then one day, a magic moment.

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

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

Here’s the sketch I drew in response:

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

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

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

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

The Impact Matrix.

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

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

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

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

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

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

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

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

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

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

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

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

Pause. Reflect on the above picture.

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

The Impact Matrix: A Joyous Deep Dive.

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

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

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

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

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

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

Effectively analyzing and acting on Bounce Rates requires the following:

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

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

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

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

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

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

Other hidden features.

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

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

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

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

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

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

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

Action #1: Analytics Program Maturity Diagnostic.

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

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

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

🙂

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

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

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

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

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

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

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

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

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

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

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

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

Is this a good thing?

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

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

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

Recommendation Uno:

Recommendation Dos:

Recommendation Très:

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

Recommendation Cuatro:

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

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

Action #2: Aligning Metrics & Leadership Altitude.

When offered data, everyone wants everything.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Recommendation Cinco:

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

Recommendation Seis:

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

Action #3: Strategy for Analytical Effort.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Recommendation Siete:

#sisepuede

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

That’s the power of a 2×2 (or a 2×5)!

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

As always, it is your turn now.

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

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

Thank you.

The post The Impact Matrix | A Digital Analytics Strategic Framework appeared first on Occam’s Razor by Avinash Kaushik.

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 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!

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.

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:

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.

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:

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:

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

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:

Here’s a comparison on Honda’s site…

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…

This shows hunger and desire to win… Their text:

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

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!

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!)…

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…

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 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 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…

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…

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.

The post Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates! appeared first on Occam’s Razor by Avinash Kaushik.

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.”

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!

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.

2. Expand the incentives structures for your employees.

3. Expand the time horizon for success.

4. Expand the datasets that teach your smart algorithms.

5. Expand leadership comfort level with ambiguity.

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.

The post Breaking Silos: Passive Consumption + Active Engagement FTW! appeared first on Occam’s Razor by Avinash Kaushik.

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

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

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

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

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

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

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

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

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

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

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

Ready?

Let’s go!

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

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

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

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

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

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

Here are some things that stood out for me:

1. Graphing choices can exaggerate or undersell reality.

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

Start at zero. Please.

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

This is very subtle.

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

Besides, this false precision also clutters the graph.

3. Remove the distractions, ruthlessly.

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

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

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

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

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

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

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

You don’t need to go this far.

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

Simple. No funny business. 

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

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

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

Read. Don’t scroll. Absorb.

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

Here are some things that stood out for me:

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

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

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

5. Experiment with visualization options, even in Excel!

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

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

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

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

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

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

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

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

Here’s what stood out for me:

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

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

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

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

Why have two fat bars?

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

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

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

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

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

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

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

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

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

This one flummoxed me.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I hate pie charts. I really do.

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

Here’s the scientific reason:

That is well on display below…

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

8. Eat Pies, Don’t Share Them!

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

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

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

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

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

Here’s an example that illuminates that clearly.

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

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

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

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

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

The elimination of the overall average makes the table tighter.

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

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

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

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

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

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

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

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

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

One final touch.

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

Here’s that version…

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

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

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

Persuading anyone in these circumstances is a herculean task.

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

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

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

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

Ouch. Ouch.

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

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

10. Let the higher order bit be your anchor.

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

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

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

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

Simpler, right?

We can do one better.

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

The colors help focus the attention even more.

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

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

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

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

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

11. Why. Your job is to answer why!

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

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

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

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

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

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

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

A challenge for you to tackle.

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

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

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

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

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

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

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

As always, it is your turn now.

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

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

Thank you.

The post Closing Data’s Last-Mile Gap: Visualizing For Impact! appeared first on Occam’s Razor by Avinash Kaushik.

If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage.

It all starts with sharp focus.

Consider these three scenarios…

Your boss is waiting for you to present results on quarterly marketing performance, and you have 75 dense slides. In your heart you know this is crazy; she won’t understand a fraction of it. What do you do?

Your recent audit of the output of your analytics organization found that 160 analytics reports are delivered every month. You know this is way too many, way too often. How do you cull?

Your digital performance dashboard has 16 metrics along 9 dimensions, and you know that the font-size 6 text and sparkline sized charts make them incomprehensible. What’s the way forward?

If you find yourself in any of these scenarios, and your inner analysis ninja feels more like a reporting squirrel, it is ok. The first step is realizing that data is being used only to resolve the fear that not enough data is available. It’s not being selected strategically for the most meaningful and actionable insights.

As you accumulate more experience in your career, you’ll discover there are a cluster of simple strategies you can follow to pretty ruthlessly eliminate the riffraff and focus on the critical view. Here are are five that I tend to use a lot, they are easy to internalize, take sustained passion to execute, but always yield delightful results…

1. Focus only on KPIs, eliminate metrics.

Here are the definitions you’ll find in my books:

Time on Page is a metric. As is Impressions. So are Followers and Footsteps, Reach and Awareness, and Clicks and Gross Ratings Points.

Each hits the bar of being “interesting,” in a tactical oh that’s what’s happening in that silo soft of way. None, passes the simple closely tied to overall business success standard. In fact, hold on to your hats, a movement up or down 25% in any of those metrics may or may not have any impact on your core business outcomes.

Profit is obviously a KPI, as is Likelihood to Recommend. So too are Installs and Monthly Active Users, Orders and Loyalty, Assisted Conversions and Call Center Revenue.

Each KPI is of value in a strategic oh so that is why we are not making money or oh so that is why we had a fabulous quarter sort of way. A 25% movement in any of those KPIs could be the difference between everyone up and down getting a bonus or a part of the company facing layoffs. Often, even a 5% movement might be immensely material. What metric can say that?

When you find yourself experiencing data overload, don an assassin’s garb, identify the metrics and kill them. They are not tied to business success, and no senior leader will miss them. On the ground, people will use metrics as micro diagnostic instruments, but they already do that.

A sharp focus on KPIs requires concentrating on what matters most. Every business will have approximately six KPIs for a CEO. Those six will tie to another six supplied to the CMO.

After you go through the assassin’s garb process above, if it turns out that you have 28 KPIs… You need help. Hire a super-smart consultant immediately!

2. Focus only on KPIs that have pre-assigned targets.

This is a clever strategy, I think you are going to love it.

Turns out, creating targets is insanely hard.

You have to be great at forecasting, competitive intelligence, investment planning, understanding past performance, organization changes and magic pixie dust (trust me on that one).

Hence, most companies will establish targets only for the KPIs deemed worthy of that hard work.

Guess what you should do with your time? Focus on analysis that is worth your hard work!

Start by looking at your slides/report/dashboard and identify the KPIs with established targets. Kill the rest.

Sure, there will be howls of protest. It’ll be John. Tell him that without targets you can’t identify if the performance is good or bad, a view every CEO deserves.

John will go away and do one of two things:

You win either way. 🙂

An added benefit will be that with this sharp focus on targets, your company will get better at forecasting, competitive intelligence, investment planning, org changes, magic pixie dust and all the other things that over time become key assets. Oh, your Finance team will love you!

Special caution: Don’t ever forget your common sense, and strive for the Global Maxima. It is not uncommon for people to sandbag targets to ensure they earn a higher bonus. If your common sense suggests that the targets are far too low, show industry benchmarks. For example, the quarterly target may be 400,000 units sold. Common sense (and company love) tell you this seems low, so you check actuals to find that in the second month, units sold are already 380,000. Suspicion confirmed. You then check industry benchmarks: It is 1,800,000. WTH! In your CMO dashboard, report Actuals, Target and Benchmark. Let him or her reach an independent, more informed, conclusion about the company’s performance.

3. Focus on the outliers.

Turns out, you are the analyst for a multi-billion dollar corporation, with 98 truly justifiable KPIs (you are right: I’m struggling to breathe on hearing that justification, but let’s keep going). How do you focus on what matters most?

Focus your dashboards only on the KPIs where performance for that time period is three standard deviations away from the mean.

A small statistics detour.

If a data distribution is approximately normal then about 68 percent of the data values are within one standard deviation of the mean, about 95 percent are within two standard deviations, and about 99.7 percent lie within three standard deviations. [Wikipedia]

By saying focus on only reporting on KPIs whose performance is three standard deviations from the mean, I’m saying ignore the normal and the expected. Instead, focus on the non-normal and the unexpected.

If your performance does not vary much, consider two standard deviations away from the mean. If the variation is quite significant, use six (only partly kidding!).

The point is, if performance is in the territory you expect, how important is it to tell our leaders: The performance is as it always is.

Look for the outliers, deeply analyze the causal factors that lead to them, and take that to the executives. They will give you a giant hug (and more importantly, a raise).

There are many ways to do approach this. Take this image from my January 2007 post: Analytics Tip #9: Leverage Statistical Control Limits…

Having an upper control limit and a lower control limit makes it easy to identify when performance is worth digger deeper into. When you should freak out, and when you should chill.

Look for outliers. If you find them, dig deeper. If not, move on permanently, or at least for the current reporting cycle.

Use whichever statistical strategies you prefer to find your outliers. Focus sharply.

4. Cascade the analysis and responsibility for data.

In some instances you won’t be able to convince the senior leader to allow you to narrow your focus. He or she will still want tons of data, perhaps because you are new or you are still earning credibility. Maybe it is just who they are. Or they lack trust in their own organization. No problem.

Take the 32 metrics and KPIs that are going to the CMO. Pick six critical KPIs for the senior leader.

Cluster the remaining 26 metrics.

You’ll ask this question:

You might end up with eight for the VPs. Great.

Now ask this question:

You might end up with 14 for the directors.

Awesome.

Repeat it for managers, then marketers.

Typically, you’ll have none remaining for the Marketers.

Here’s your accomplishment: You’ve taken the 32 metrics that were being puked on the CMO and distributed them across the organization by level of responsibility. Furthermore, you’ve ensured everyone’s rowing in the same direction by creating a direct line of sight to the CMO’s six KPIs.

Pat yourself on the back. This is hard to do. Mom is proud!

Print the cascading map (CMO: 6 > VPs: 8 > Directors: 14 > Managers: 4), show it to the CMO to earn her or his confidence that you are not throwing away any data. You’ve simply ensured that each layer reporting to the CMO is focused on its most appropriate best sub-set, thus facilitating optimal accountability (and data snacking).

I’ll admit, this is hard to do.

You have to be deeply analytically savvy. You have to have acquired a rich understanding of the layers of the organization and what makes them tick. You have to be a persuasive communicator. And, be able to execute this in a way that demonstrates to the company that there’s real value in this cascade, that you are freeing up strategic thinking time.

You’ll recognize the overlap between the qualities I mention above and skills that drive fantastic data careers. That’s not a coincidence.

Carpe diem!

5. Get them hooked on text (out-of-sights).

If everything else fails, try this one. It is the hardest one because it’ll demand that you are truly an analysis ninja.

No senior executive wants data. It hurts me to write that, but it is true.

Every senior executive wants to be influenced by data and focus on solving problems that advance the business forward. The latter also happens to be their core competence, not the former.

Therefore, in the next iteration of the dashboard, add two more pieces of text for each metric:

When you do this, you’ll begin to showcase multiple factors.

For the pointless metrics, neither the Why nor the What will have impact. The CMO will kill these in the first meeting.

For the decent metrics, it might take a meeting or three, but she’ll eventually acknowledge their lack of value and ask you to cascade them or kill them.

From those remaining, a handful will come to dominate the discussion, causing loads of arguments, and resulting in productive action. You’ll have known these are your KPIs, but it might take the CMO and her team a little while to get there.

After a few months, you’ll see that the data pukes have vanished. If you’ve done a really good job with the out-of-sights and actions, you’ll notice notice that the focus has shifted from the numbers to the text.

Massive. Yuge. Victory.

If more examples will be of value, I have two posts with illuminating examples that dive deeper into this strategy…

Strategic Dashboards: Best Practices, Tips, Examples | Smart Dashboard Modules: Insightful Dimensions And Best Metrics

You don’t want to be a reporting squirrel, because over time, that job will sap your soul.

If you find yourself in that spot, try one of the strategies above. If you are desperate, try them all. Some will be easier in your situation, while others might be a bit harder. Regardless, if you give them a shot, you’ll turn the tide slowly. Even one month in, you’ll feel the warm glow in your heart that analysis ninjas feel all the time.

Oh, and your company will be data-influenced — and a lot more successful. Let’s consider that a nice side effect. 🙂

Knock ’em dead!

As always, it is your turn now.

Have you used any of the above mentioned strategies in your analytics practice? What other strategies have been effective in your company? What is the hardest metric to get rid of, and the hardest KPI to compute for your clients? Why do you think companies keep hanging on to 28 metric dashboards?

Please share your ideas, wild theories, practical tips and examples via comments.

Thank you.

Five Strategies for Slaying the Data Puking Dragon. is a post from: Occam’s Razor by Avinash Kaushik