Since you’re reading a blog on advanced analytics, I’m going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing.
It truly is the bee’s knees.
You are likely aware that there are entire sub-cultures (and their attendant Substacks) dedicated to the most granular ideas around experimentation (usually of the landing page optimization variety). There are fat books to teach you how to experiment (or die!). People have become Gurus hawking it.
The magnificent cherry on this delicious cake: It is super easy to get started. There are really easy to use free tools, or tools that are extremely affordable and also good.
ALL IT TAKES IS FIVE MINUTES!!!
And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice.
Wah wah wah waaah.
How is this possible?
It turns out experimentation, even of the simple landing page variety, is insanely difficult for reasons that have nothing to do with the capacity of tools, or the brilliance of the individual or the team sitting behind the tool (you!).
It is everything else:
Company. Processes. Ideas. Creatives. Speed. Insights worth testing. Public relations. HiPPOs. Business complexity. Execution. And more.
Today, from my blood, sweat and tears shed working on the front lines, a set of two reflections:
1. What does a robust experimentation program contain?
2. Why do so many experimentation programs end in disappointing failure?
My hope is that these reflections will inspire a stronger assessment of your company, culture, and people, which will, in turn, trigger corrective steps resulting in a regular, robust, remarkable testing program.
First, a little step back to imagine the bigger picture.
This blog post was originally published as
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
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
The rapid pace of innovation and the constantly exploding collection of possibilities is a major contributor to the fun we all have in digital jobs. There is never a boring moment, there is never time when you can’t do something faster or smarter.
The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current.
This reality powers my impostor syndrome, and (yet?) it is the reason that I love working in every dimension of digital. We are at an inflection point in humanity’s evolution where in small and big ways, we can actually change the world.
With that context, this post is all about career management in the digital space. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. I would offer that the higher-order-bits in each of the three sections will provide valuable food-for-thought for anyone in a digital role.
The post has three clusters of advice. The first two are from editions of my newsletter, The Marketing – Analytics Intersect (it goes out weekly, and is now my primary publishing channel, sign up!). The third section was sparked by a question a friend who works at a digital agency asked: Will I lose my job to automation soon? (The answer was, yes.)
The Now section provides advice on how investing in growing your Analytical Thinking will contribute to greater success in the role you are in. The Next section provides advice on what you should be doing
Life is short.
It is time to point out an ugly truth, and to be the brave person that you are, the intelligent rational assessor of reality that you are, and kill all the organic social media activity by your company.
All of it.
Seems radical, but let’s take it one step at a time.
To give you a sense of the depth and breadth of ideas I’ll cover today, here are the sections in this post:
+ The Promise of Marketing Utopia.
+ The Broken Promise of Marketing Utopia, Implications.
+ The Broken Promise of Marketing Utopia: Examples.
+ Win Big: Stop Posting Content for Organic Reach On Social Channels.
+ Is the Huge Audience on Social Media Platforms Completely Useless?
+ Is the Idea of Marketing Utopia Permanently Dead?
I urge you to have an open mind. My plan is to challenge your critical thinking skills, and share lessons that will apply broadly across the professional effort you put day in and day out. Most of all, I’m excited to frame an important problem, and present solutions that will transform an important part of your marketing strategy.
The Promise of Marketing Utopia.
I hate pimping (what marketing has come to be). I adore building meaningful relationships – the kind of long-term connections where a brand truly gives a f about their customers, and gives something of value in exchange for their attention. I LOVE brands that can pull this off, and support them with my un-asked-for evangelism and precious $$$s.
Hence, you can imagine how gosh darn excited I was at the advent of Facebook and Twitter (first real social networks). There were a billion people
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
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,
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