Almost all metrics you currently use have one common thread: They are almost all backward-looking.
If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics.
But first, let’s take a small step back. What is a metric?
Here’s the definition of a metric from my first book:
A metric is a number.
Conversion Rate. Number of Users. Bounce Rate. All metrics.
[Note: Bounce Rate has been banished from Google Analytics 4 and replaced with a compound metric called Engaged Sessions – the number of sessions that lasted 10 seconds or longer, or had 1 or more conversion events or 2 or more page views.]
The three metrics above are backward-looking. They are telling us what happened in the past. You’ll recognize now that that is true for almost everything you are reporting (if not everything).
But, who does not want to see the future?
Yes. I see your hand up.
The problem is that the future is hard to predict. What’s the quote… No one went broke predicting the past. 🙂
Why use Predictive Metrics? As Analysts, we convert data into insights every day. Awesome. Only some of those insights get transformed into action – for any number of reasons (your influence, quality of insights, incomplete stories, etc. etc.). Sad face.
One of the most effective ways of ensuring your insights will be converted into high-impact business actions is to predict the future.
Consider this insight derived from data:
The Conversion Rate from our Email campaigns is 4.5%, 2x of Google Search.
Now consider this one:
The Conversion Rate from our Email campaign is
One of the business side effects of the pandemic is that it has put a very sharp light on Marketing budgets. This is a very good thing under all circumstances, but particularly beneficial in times when most companies are not doing so well financially.
There is a sharper focus on Revenue/Profit.
From there, it is a hop, skip, and a jump to, hey, am I getting all the credit I should for the Conversions being driven by my marketing tactics? AKA: Attribution!
Right then and there, your VP of Finance steps in with a, hey, how many of these conversions that you are claiming are ones that we would not have gotten anyway? AKA Incrementality!
Two of the holiest of holy grails in Marketing: Attribution, Incrementality.
Analysts have died in their quests to get to those two answers. So much sand, so little water.
Hence, you can imagine how irritated I was when someone said:
Yes, we know the incrementality of Marketing. We are doing attribution analysis.
You did not just say that.
I’m not so much upset as I’m just disappointed.
Attribution and Incrementality are not the same thing. Chalk and cheese.
Incrementality identifies the Conversions that would not have occurred without various marketing tactics.
Attribution is simply the science (sometimes, wrongly, art) of distributing credit for Conversions.
None of those Conversions might have been incremental. Correction: It is almost always true that a very, very, large percentage of the Conversions driven by your Paid Media efforts are not incremental.
Attribution ≠ Incrementality.
In my newsletter, TMAI Premium, we’ve covered how to solve the immense challenge of identifying the true incrementality delivered by your Marketing budget. (Signup, email me for a link to that newsletter.)
Today, let me unpack the crucial
There has been a lot of heartbreak around the world with the CV-19 pandemic.
This chart, from NPR, illustrates some cause for optimism. It shows the 7-day average new cases per day across the world.
It is crucial to acknowledge what’s hidden in the aggregated trend above: The impact on individual countries is variable.
A large percentage of humans on the planet remain under threat. We don’t nearly have enough vaccines finding arms. We have to remain vigilant, and commit to getting the entire planet vaccinated.
Recent worries about Covid were increased by the proliferation of virus variants around the world. Variant B.1.1.7 was first identified in the UK. Variant B.1.351 was first identified in South Africa. Variant P.1 in Brazil has 17 unique mutations. The variant identified in India, B.1.617.2, had a particularly devastating impact (see the blue spike above). There are multiple “variants of interest” in the United States, Philippines, Vietnam, and other countries.
A particularly dangerous thing about variants is that they are highly transmissible (evolution, sadly, in action).
Some journalists rush to point out, hey, the death rate remains the same.
I believe this is a mistake. It imprecisely minimizes the danger, and results in some of our fellow humans feeling a false sense of hope. This is possibly due to a lack of mathematical savvy.
As Analysts, you can appreciate that a lay individual might not quite understand the complexity behind infection rates, and the impact on death rates. At the same time all of us, journalists and Analysts have to figure out how to communicate this type of insight in a way that everyone can understand.
This reality is similar to what we face in our business environment every single
Like you, I consume a whole lot of reports every day – company data, public data.
Many are acceptable, some are very good and all the rest leave me extremely frustrated with both the ink and the think.
People make so many obvious mistakes. Sometimes repeatedly.
Just yesterday I was quietly seething because none of visuals included in the report contained any context to understand if the performance I was looking at was good or bad.
The graph could be going up, down, all around and I as a consumer had the job of figuring if something was good, bad or worth ignoring.
The heartbreaking part is that most executives will take a look, realize the difficulty in interpretation in 15 – 20 seconds, and go back to shooting from the gut. Even if the report has hidden gold.
In a move that might not surprise you, I sat down with the person for 90 minutes going visual by visual, table by table, directing changes that would ensure everything had context.
A report usually has a hard time explaining why something is going awry or going really well. (That is why you have job security as an Analyst!)
A report can usually be very good at clearly highlighting what is going well or badly.
Your #1 job is to make sure your reports don’t fail at this straightforward responsibility.
So today a simple collection of tips that you can use to up-level your reports – to allow them to speak with a clear, and influential, voice.
For many of you a reminder of what you might have let slip, for others a set of new things to implement as you aim for your next promotion.
#1. Context, Context,
The gap between a bad and good data visualization is small.
The gap between a good and great data visualization is a vast chasm!
The challenge is that we, and our HiPPOs, bring opinions and feelings and our perceptions of what will go viral to the conversation. This is entirely counter productive to distinguishing between bad, good, and great.
What we need instead is a rock solid understanding of the updraft we face in our quest for greatness, and a standard framework that can help us dispassionately assess quality.
Let’s do that today. Learn how to seperate bad from good and good from great, and do so using examples that we can all relate to instantly.
We’ll start by looking at the two sets of humans who are at the root of the conflict of obsessions and then learn to assess how effective any data visualization is in an entirely new way. If you adopt it, I guarantee the impact on your work will be transformative.
The Conflict of Obsessions.
There are two parties involved in any data visualization.
1. Analyst/Data Visualizer. As I’ve passionately shared frequently on this blog, we, Analysts, are all in the business of persuasion. We work against that desired outcome because when we work on creating a data visualization, here are our top-of-mind concerns/desires/perspectives:
How can I cram as much as I can into the graphic?
What can I include to ensure everyone clearly gets just how much work I did?
How much of my agenda do I need to make overt, and how much can I make covert?
Is there something I can add to increase the chances that this will go viral and result in fame and glory?
Ok. I’m only teasing.
But, as an
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.
First… The Analytics top ten things to focus on to elevate your game this year…
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
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.