Your analysis provides clear data that the campaign was a (glorious) failure.
It could not be clearer.
The KPI you chose for your brand campaign was Trust, it had a pre-set target of +5. The post-campaign analysis that compares performance across Test & Control cells shows that Trust did not move at all. (Suspiciously, there are indications that in a handful of Test DMAs it might have gone down!)
Every so often, the story is just as simple as that.
You do the best you can with a marketing campaign (creative, audience, targeting, channels, media plan elements like duration, reach, frequency, media delivery quality elements like AVOC, Viewability, etc.), and sometimes the dice does not roll your way when you measure impact.
You would be surprised to know just how frequently the cause for failure is things that have nothing to do with the elements I mentioned above. In future Premium editions we’ll cover a bunch of these causes, today I want to cover one cause that is in your control but often a root cause of failure:
Judging a fish by its ability to climb a tree!
AKA: You picked the wrong KPI for the campaign.
[Note 1: I’m going to use the phrase Success KPI a lot. To ensure clear focus, clear postmortems and clear accountability, I recommend identifying one single solitary metric as the Success KPI for the initiative. You can measure seven additional metrics – say for diagnostic purposes -, but there has to be just one Success KPI. Close accountability escape hatches.]
[Note 2: Although the guidance in this article applies to companies/analytics teams of all sizes, it applies in particular to larger companies and large agencies. It is there that the
If there is one thing the universe agrees on, it is that you should just provide data… You should provide INSIGHTS!!!
In the 807,150 (!) words I’ve written on this blog thus far, at least 400,000 have been dedicated to helping you find insights.
In posts about advanced segmentation, in posts about how to build strategic dashboards that don’t suck, in encouraging you to reimagine how you pick metrics to obsess about using the magnificent Impact Matrix, and on and on and on.
Go for insights!
In time, I’ve come to hate the word insights.
In our world – marketing research and analytics – that word has come to represent data puking.
It has come to represent telling people, with dozens of reports or eighty slides, that water is wet.
I’ve observed, during my work across the world, when we deliver insights, we mostly deliver to our audiences things in-sight – things they can already see!
As in, the blue line is 20% above the red line. I CAN SEE THAT! Or, life-time value of California purchasers is 3x when compared to those who reside in Georgia. Oh, please, I can also see that on the table with my eyes.
This, unsurprisingly, ends up being a massive waste of your incredible talent, and an insult to the intelligence of our audience (the people who pay your salary).
This blog post was originally published as an edition of my newsletter TMAI Premium. It is published 50x/year, and shares bleeding-edge thinking about Marketing, Analytics, and Leadership. You can sign up here – all revenues are donated to charity.
The last time I changed jobs, I wanted to change the aspiration of what our talented team and I should shoot
I was reading a paper by a respected industry body that started by flagging head fake KPIs. I love that moniker, head fake.
Likes. Sentiment/Comments. Shares. Yada, yada, yada.
This is great. We can all use head fake metrics to calling out useless activity metrics.
[I would add other head fake KPIs to the list: Impressions. Reach. CPM. Cost Per View. Others of the same ilk. None of them are KPIs, most barely qualify to be a metric because of the profoundly questionable measurement behind them.]
The respected industry body quickly pivoted to lamenting their findings that demonstrate eight of the top 12 KPIs being used to measure media effectiveness are exposure-counting KPIs.
A very good lament.
But, then they then quickly pivot to making the case that the Most Important KPIs for Media are ROAS, Exposed ROAS, “Direct Online Sales Conversions from Site Visit” (what?!), Conversion Rate, IVT Rate (invalid traffic rate), etc.
Wait a minute.
Most important KPI?
No siree, Bob! No way.
Take IVT as an example. It is such a niche obsession.
Consider that Display advertising is a tiny part of your budget. A tiny part of that tiny part is likely invalid. It is not a leap to suggest that it is a big distraction from what’s important to anoint this barely-a-metric as a KPI. Oh, and if your display traffic was so stuffed with invalid traffic that it is a burning platform requiring executive attention… Any outcome KPI you are measuring (even something basic as Conversion Rate) would have told you that already!
Conversion Rate obviously is a fine metric. Occasionally, I might call it a KPI, but I have never anointed it as the Most Important KPI.
In my experience, Most Important
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
Like many of you, I am both an employee and a people leader.
At different points of the day, sometimes from one minute to the next, I have to switch gears so that I can be fully present as both a good employee and a good people leader. This constant quest for excellence, from one email to the next, from one meeting to the next as context changes is… taxing.
From observing behavior closely, and from my own experimentation and failure, I’ve noticed consistent patterns in what great employees do and great bosses do. In my long professional career, I’ve tried to emulate these patterns and to build on them as I try to deliver a non-normal impact to my employers.
While obsessing about Marketing and Analytics here on Occam’s Razor, I want to share the habits and behaviors encoded in these patterns so that you can have a non-normal impact in your chosen field as well.
[At the end of this post, you’ll find my guidance summarized in a printable infographic.]
I’ll cover the ten good employee patterns:
1. Never bring problems.
2. Be thorough.
3. Care about little things.
4. Look beyond the near future, see the full landscape of opportunity.
5. Create your personal board of directors.
6. Do at least one thing outside your immediate team/scope.
7. Invest in yourself.
8. Ask for more responsibility, vs. asking for a promotion.
9. Stay weird.
10. Solve for the company, not just your boss.
And follow that with six great boss patterns:
1. If you hire Michelangelo to paint the Sistine Chapel…
2. Explain strategy. And, critically, why that strategy.
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,
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
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
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