People have been making New Year’s resolutions since the time of the ancient Babylonians. Usually those resolutions involve something along the lines of losing weight or saving money.
But for 2020, why not set some achievable goals for your measurement strategy? I have three suggestions to get you started. Accomplishing these goals will bring you professional joy and boost your company’s bottom line.
3 measurement resolutions all marketers should make for 2020
Resolution 1: Provide context for your metrics
We’ve all received one of those self-congratulatory emails from a colleague informing us that their latest ad campaign had 10,000 on-target impressions, or the six-second video they ran had a 60% completion rate.
Whenever I see one, the first thing I ask myself is: What do these numbers even mean? Are they good or bad? Should we be patting ourselves on the back or trying to figure out what went wrong?
That’s why the first measurement resolution I’d like us all to make for 2020 is this: Always provide context for your metrics. There are many strategies you can draw on to do this. The simplest is to use industry benchmarks.
This doesn’t tell us much: Our six-second ad had a 60% completion rate.
This tells us a lot: Our six-second ad had a 60% completion rate; the industry benchmark is 81%.
Ouch. But it’s a good “ouch.” Now, you’ll know you have to dig into your creative to see what could be improved, analyze your targeting strategy to find out what went wrong, and start researching great video ads to see what they have in common. And that will help you improve future performance — all from a tiny bit of context.
Once you have more experience, you can start setting your own goals. If your last email campaign had an average openrate of 20%, you can set a goal of 23%. Now, when you report performance, here’s what you’ll say: “Our average open rate for February 2020 was 25% compared to our goal of 23%.” Data with context — it’ll get you promoted.
Here’s one last favorite piece of context of mine: Cost.
So your fancy, contextually relevant, machine-learning based, dynamic landing pages generated 1.5 million engaged views in a month compared to just 1 million for your old static landing pages. Hurray?
Let’s add context: After factoring in development and maintenance costs, your new dynamic pages have a cost per engaged view of $5 and the old static ones have a cost per engaged view of $1.
That is the magic of context. A little bit of pixie dust that helps us make smarter decisions.
Resolution 2: Stop making common data reporting mistakes
Marketing analysts have to boil down so much complexity into simple stories. That’s why it breaks my heart to see our hard work undercut by common, easily avoidable mistakes.
So let me share my top two most-annoying reporting mistakes. They’re both easily avoidable, so you can keep your 2020 resolution accomplishment rate at 100%.
First, stop reporting percentages alone.
“Our new and improved digital campaign led to a 100% increase in sign-ups.” By itself, that information is almost entirely useless. Did the number of subscribers increase from 100 to 200? Or from 10,000 to 20,000? Slightly different scenarios, right?
Reporting percentages by themselves, without any baselines, is a mistake. At best, it suggests you’re lacking even a basic grasp of data and the role it plays in decision-making. At worst, you look sneaky, as though you’re trying to spin the data to tell a more flattering story.
Instead, marry percentages with the most relevant raw numbers. It is magical.
My second recommendation is to ensure your data reporting doesn’t contain misaligned altitudes. What do I mean by this? Here’s an example. In an analysis someone just sent me, there’s a table of metrics. In the column showing revenue, it has 12.3M, 3.5M, 145K, 2M, 12K, 674K.
Almost every number is expressed using a different altitude. This means the person reading the report will have to do extra work to interpret the data. What you should show is 12.3M, 3.5M, 0.15M, 2.0M, 0.01M, 0.67M.
Everything’s aligned at the same altitude, making the data easier to compare and reducing the processing load.
Here’s another, everyday example of misaligned altitude. We all watch YouTube videos. Check out the numbers for how many people liked or disliked this one, and how those numbers are at misaligned altitudes: – Read more