Deliver Step Change Impact: Marketing & Analytics Obsessions

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes things better – I’m borrowing the concept from mathematics and technology, from “step function”).
In those moments – common around new years or new annual planning cycles – the difference between delivering an incremental change vs. a step change is the quality of ideas you are considering. In this post, my hope is to both enrich your consideration set and encourage the breadth of your goals.
My professional areas of interest cover Customer Service, User Experience and Finance, though here on Occam’s Razor my focus is on influencing incredible Marketing through the use of innovative Analytics. To help kick-start your 2019 step change, I’ve written two “Top 10” lists, one for Marketing and one for Analytics – consisting of things I recommend you obsess about.
Each chosen obsession is very much in the spirit of my beloved principle of the aggregation of marginal gains. My recommendation is that you deeply reflect on the impact of the 10 x 2 obsessions in your unique circumstance, and then distill the ten you’ll focus on in the next twelve months. Regardless of the then you choose, I’m confident you’ll end up working on challenging things that will push your professional growth forward and bring new joy from the work you do for your employer.
Ready?
First… The Analytics top ten things to focus on to elevate your game this year…
The Step

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

I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well.
A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights.
The last-mile gap is the distance between your trends and getting an influential company leader to take action.
Your biggest asset in closing that last-mile gap is the way you present the data.
On a slide. On a dashboard in Google Data Studio. Or simply something you plan to sketch on a whiteboard. This presentation of the data will decide if your trends and insights are understood, accepted and inferences drawn as to what action should be taken.
If your data presentation is good, you reduce the last-mile gap. If your data presentation is confusing/complex/wild, all the hard work that went into collecting the data, analyzing it, digging for context will all be for naught.
With the benefits so obvious, you might imagine that the last-mile gap is not a widely prevalent issue. I’m afraid that is not true. I see reports, dashboards, presentations with wide gaps. It breaks my heart, because I can truly appreciate all that hard work that went into creating work that resulted in no data-influence.
Hence today, one more look at this pernicious problem and a collection of principles you can apply to close the last-mile gap that exists at your work.
For our lessons today, I’m using an example that comes from analysis delivered by the collective efforts of a top American university, a top 5 global consulting company, and

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Create High-Impact Data Visualizations: Nine Effective Strategies

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

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The Very Best Digital Metrics For 15 Different Companies!

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

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Artificial Intelligence: Implications On Marketing, Analytics, And You

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).
Let’s go!
AI | Now | Local Maxima.
AI also seems so out there, so hard to grasp. Let me fix that for you.
Here’s a

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It's Not The Ink, It's The Think: 6 Effective Data Visualization Strategies

Ten years, and the 944,357 words, are proof that I love purposeful data, collecting it, pouring smart strategies into analyzing it, and using the insights identified to transform organizations.
In the quest for that last important bit, I am insanely obsessive about 1. simplification and 2. pressing the right emotional buttons.
The reasons are that we all like complexity, it gives us energy :), we tend to be logical, and we often treat data output as the end when in reality the data output is just the start of the process that results in actions that deliver business impact.
Very often the output of our work with Big Data or Small Data, Google Analytics or R, will end up in a few cells of a spreadsheet or a table in Word/Keynote/PowerPoint. The stakes for this output are higher when we are in front of the Senior Leadership of any company, we have but a few minutes to communicate what we have to. Hence my two obsessions above.
In this post, with lots of pictures and real-world data examples, I want to share 6 different strategies you can leverage in service of simplification and pressing the right emotional buttons. Along our journey, I’ve also sprinkled in 15 universal truths that will bring you joy.
Here are the sections in this post:

An important assumption.
Death at the last-mile.
    1. Rebel against crapification via cluttering.
    2. Don’t fragment data, don’t forget higher order bits.
    3. Obsess with deleting information provided.
    4. Don’t run away, make the tough choices.
    5. So what? So What?? So WHAT!
    6. Sell smarter,

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Five Key Elements For A Big Analytics Driven Business Impact

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization.
And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!) there is a new mobile app to track or a new digital experience to problem-solve or a crazy online to offline campaign that upends everything unleashes a new layer of tactical activity.
In a world when your work will never be done, how do you assess that the core things necessary are present? How do you ensure that your can zig-zag with business strategy? What guarantees that agility and innovation are present in your analytics practice?
I believe there are five elements that have to be persistently present in the primordial soup at any company that expects amazing life to spring forth.
You’ll be surprised, there’s only one tool in that mix. It is not even an analytics tool. My reason for that is simple… At this point, it honestly does not matter which web analytics tool you use as long as it is a tool that is under active development by your vendor. Yes, some tools can dance on their left foot and others can only do so with their right foot. Not as important as you might think.
My recommended five elements are much more primal, their presence powers brilliant life to constantly evolve.
Here’s a little back story.
I was asked a few weeks back: “What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions?” I think the answer expected was my view related to

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Five Key Elements For A Big Analytics Driven Business Impact

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization.
And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!) there is a new mobile app to track or a new digital experience to problem-solve or a crazy online to offline campaign that upends everything unleashes a new layer of tactical activity.
In a world when your work will never be done, how do you assess that the core things necessary are present? How do you ensure that your can zig-zag with business strategy? What guarantees that agility and innovation are present in your analytics practice?
I believe there are five elements that have to be persistently present in the primordial soup at any company that expects amazing life to spring forth.
You’ll be surprised, there’s only one tool in that mix. It is not even an analytics tool. My reason for that is simple… At this point, it honestly does not matter which web analytics tool you use as long as it is a tool that is under active development by your vendor. Yes, some tools can dance on their left foot and others can only do so with their right foot. Not as important as you might think.
My recommended five elements are much more primal, their presence powers brilliant life to constantly evolve.
Here’s a little back story.
I was asked a few weeks back: “What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions?” I think the answer expected was my view related to

Read more...

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

Today’s post comes from a source of deep pain. Analysis Ninjas are valued less than I would prefer for them to be.
The post is also sourced from a recent edition of my newsletter, The Marketing – Analytics Intersect. I send it once a week, and it contains my insights and recommendations on those two topics. Like this blog, the newsletter is geared towards being instantly actionable (rather than just theory-smart, which is pretty cool too). Do sign up if you want to deliver a small electric shock of simulation to your brain each week.
TMAI #41 covered a graph that resulted from a survey done by Econsultancy and Lynchpin. I received a ton of responses for it, and great discussion ensued. It prompted me to write this post, essentially an expanded version of TMAI #41. I’ve added new insights, recommendations, and two bonus lessons on how to do surveys better and a direct challenge to your company’s current analytics strategy.
If your heart is weak, you can stop reading now. I promise, I won’t mind one bit. I heart you. If you are open to being challenged… then here are the short-stories inside this post…

The World Needs Reporting Squirrels. Wait. What!
Three thoughts that explain the Econsultancy/Lynchpin graph.
Bonus #1: Lessons from Econsultancy/Lynchpin Survey Strategy.
Bonus #2: The Askers-Pukers Business Model.
Bottom-line.

Let’s go and challenge our collective thinking!
The World Needs Reporting Squirrels. Wait. What!
Some of you know that I created the phrases Reporting Squirrels and Analysis Ninjas to emphasize the difference between those that puke data and those that puke insights with actions attached to them.
Here is my slide the first time I presented the concept in a

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

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

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