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).
No CxO understands the
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
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:
Metric: A metric
Today, a simple lesson that so many of us miss at great peril. In fact in your role, at this very moment, your company is making a mistake in terms of how it values your impact on the business.
The lesson is about the limitation of optimizing for a local maxima, usually in a silo.
We are going to internalize this lesson by learning from Microsoft. It is a company I love (am typing this on my beloved ThinkPad X1 Carbon Gen 5, using Windows Live Writer blogging software!). I bumped into the lesson thanks to their NFL sponsorship.
If you were watching the Oakland Raiders beating the hapless New York Giants (so sad about Eli) this past Sunday, you surely saw a scene like this one:
Quarterback Geno Smith using his Microsoft Surface tablet to figure out how he added two more fumbles to this career total of 43. Or, maybe it was him replaying the 360 degrees view of the three times he was sacked during the game.
The Surface tablet is everywhere in an NFL game. Microsoft paid $400 million for four years for the rights, and just renewed the deal for another year (for an as yet undisclosed sum).
For all this expense, you’ll see players and coaches using them during the game (as above). The Surface branding also gets prominent placement on the sidelines – on benches, on movable trollies and more. It is all quite prominent.
Here’s one more example: Beast mode!
I adore Mr. Lynch’s passion. Oh, and did you notice the Surface branding?
Now, let’s talk analytics and accountability.
NFL ratings are down, but an average game still gets between 15 m – 20 m viewers. That is
I believe deeply in the value of making data accessible.
In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). There is something magical about taking the tons and tons of complexity that lurks in our data, being able to find the core essence, and then illustrate that simply. The result then is both a mind and heart connection that drives action with a sense of urgency. #winning
While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. As we have all learned, tools matter a lot less than what we do with the tool.
In this post I want to inspire you to think differently. I’ve curated sixteen extremely diverse visualization examples to do that. By design none of them from the world of digital analytics, though I’ll stay connected to that world from a how could you use this idea perspective. My primary goal is to expand your horizon so that we can peek over and see new possibilities.
To spark your curiosity, the visuals I’ve worked hard to find for you cover the US debt, European politics, lynching and slavery, pandemics, movies, gun control, drugs and health, the Chinese economy, and where we spend our lives (definitely review this one!).
The sixteen examples neatly fall into nine strategies I hope you’ll cultivate in your analytics practice as you create data visualizations:
1: The Simplicity Obsession
2: If Complex, Focus!
3: Venn Diagrams FTW!
4: Interactivity With Insightful End-Points
5: What-if Analysis Models
6: Turbocharging Data Visuals with
The very best analysts distill, rather than dilute. The very best analysts focus, when most will tend to gather. The very best analysts are display critical thinking, rather than giving into what’s asked. The very best analysts are comfortable operating with ambiguity and incompleteness, while all others chase perfection in implementation / processing / reports. The very best analysts are know what matter’s the most are not the insights from big data but clear actions and compelling business impact from usually a smaller subset of key data.
The very best analysts practice the above principles every day in every dimension of their jobs. It is that practice that I try to discern when I do job interviews. When I see evidence of them in any candidate, my heart is filled with joy (and the candidate’s inbox is filled with a delightful job offer).
This post shares one application of the above skills. People ask me this seemingly simple question all the time: What Key Performance Indicators should we use for our business?
I usually ask them back: What are you trying to get done with your digital strategies?
There is no golden metric for everyone, we are all unique snowflakes!
That then takes us down the very best way to answer that question, to use the five-step process to build out the Digital Marketing and Measurement Model.
But, what if we did not have that opportunity? What if I was pushed to answer that question with just a cursory glance at their digital existence?
While it is a million times less than ideal, I can still come up with something good based on my distillation skills, application of critical thinking, comfort in operating
A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity.
While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016. Primarily because we got our first real everyday access to products and services that used some form of AI to delight us. No more theory, we felt it!
I’m going to take a very long walk with you today. This topic has consumed a lot of my thinking over the last year (you’ll see the exact start date below). It’s implications are far and wide, even in the narrow scope that I live in (marketing, analytics, influence). I have so much to tell you, stuff I’m scared about, and so much I’m excited about.
Here are the elements I’ll cover:
+ AI | Now | Local Maxima.
+ AI | Now | Global Maxima.
+ What the heck is Artificial Intelligence?
+ Machine Learning | Marketing.
+ Machine Learning | Analytics.
+ Artificial Intelligence | Future | Kids.
+ Artificial Intelligence | Worry about Humanity.
Through it all, my goal is to make the topic accessible, get you to understand some of the key terms, their implication on our work, our jobs, and in a bonus implications on the future we are responsible for (your kids and mine).
AI | Now | Local Maxima.
AI also seems so out there, so hard to grasp. Let me fix that for you.
A story where data is the hero, followed by two mind-challenging business-shifting ideas.
At a previous employer customer service on the phone was a huge part of the operation. Qualitative surveys were giving the company a read that customers were unhappy with the service being provided. As bad customer service is a massive long-term cost – and short-term pain –, it was decided that the company would undertake a serious re-training effort for all the customer service reps and with that problems would get solved faster. To ensure customer delight was delivered in a timely manner, it was also decided that Average Call Time (ACT) would now be The success metric. It would even be tied to a customer service rep’s compensation creating an overlap between their personal success and the company’s success.
What do you think happened?
There is such a thing as employees that don’t really give a frek about their job or company, they just come to work. You’ll be surprised how small that number is. (Likewise, the number of employees that go well above the call of duty, look to constantly push personal and company boundaries is also quite small.) Most employees work diligently to deliver against set expectations.
Reflecting that, in our story, most customer service reps, re-trained, took the phone calls with the goal of driving down Average Call Time. They worked as quick as they could to resolve issues. But, pretty quickly customers with painful problems became a personally painful problem for an individual customer service rep. They hurt ACT, and comp. Solution? If the rep felt the call was going too long, self-preservation kicked in and they would hang up on the customer. Another
Culture is a stronger determinant of success with data than anything else. Including data.
[People + Process + Structure] > [Data + Technology]
It seems hard to believe. Yet, it is so fantastically true. At least for now. At least until AGI takes over.
Why is this formula material?
The first part of the equation, for better or for worse, improves in an evolutionary manner. The second part of the equation most frequently improves in a revolutionary manner.
The challenge for Senior Leaders is that revolutions seem a lot more attractive and hence they charge full speed ahead. This results in frustration, derailed careers and a massive amount of money flushed down sad places.
Revolutions in our context, almost always fail. Evolution works. Hence, it is dangerous to overlook the super critical importance of P+P+S.
You want to win big with data, with marketing, with transformative digital yada yada and blah blah, evolve. Do so at the fastest pace you can put in place for transformation of the left-side of the above equation, and use the same pace to evolve the right-side of the above equation.
This will ensure that the people, process and structure will be smart enough to take advantage of the smart and wizbang tech.
Maybe this metaphor will help make this real.
You can’t give a toddler a Harley Davidson motor cycle. The moment your start the motorcycle, the toddler is going to start crying. It is not the mistake of the toddler, she is just a toddler after all. It is not the mistake of the Harley, it is a very cool motorcycle. The mistake is yours.
The toddler needs something to steady her, something she can push, something to exercise her legs to
Being book smart is good. The outcome of book smart is rarely better for analytics practitioners then folks trying to learn how to fly an airplane from how-to books.
Hence, I have been obsessed with encouraging you to get actual data to learn from. This is all the way from Aug 2009: Web Analytics Career Advice: Play In The Real World! Or a subsequent post about how to build a successful career: Web Analytics Career Guide: From Zero To Hero In Five Steps. Or compressing my experience into custom reports and advanced segments I’ve shared.
The problem for many new or experienced analysts has been that they either don’t have access to any dataset (newbies) or the data they have access to is finite or from an incomplete or incorrect implementation (experienced). For our Market Motive Analytics training course, we provide students with access to one ecommerce and one non-ecommerce site because they simply can’t learn well enough from my magnificent videos. The problem of course is that not everyone is enrolling our course!
All this context is the reason that I am really, really excited the team at Google has decided to make a real-world dataset available to everyone on planet Earth (and to all intelligent life forms in the universe that would like to learn digital analytics).
The data belongs to the Google Merchandise Store, where incredibly people buy Google branded stuff for large sums of money (average order value: $115.67, eat your heart out Amazon!). And, happily, it has almost all of the Google Analytics features implemented correctly. This gives Earth’s residents almost all the reports we would like to look at, and hence do almost all