Predictive analytics—the ability to accurately measure and predict marketing’s impact on the business—isn’t a capability that can be created overnight. We can’t just go out, hire the right talent, buy the right technology, and immediately begin accurately predicting the outcomes of our actions,, and declare things like, “If we increase our investment in TV and out-of-home advertising (OOH) by 12%, we can expect a 20% increase in retail foot traffic.”
Those kinds of predictive analytics are only possible after we’ve developed a certain set of capabilities—and in a certain sequence.
In this sense, marketing measurement is a journey. It’s a journey of maturity that we must progress through in roughly five stages:
In my experience, there are no shortcuts on this journey. Also from my experience, I know that most of us are stuck in stage 2—the “awkward stage” of manual integrated reporting.
Because performing in the omnichannel marketplace means working across so many channels, our data is uniquely messy. We get our data from a host of various execution and tracking tools, which means data comes to us in various formats (spreadsheets, slide decks, emails, etc.), and each system names things differently, measures differently, and reports differently. The manual effort it takes to reconcile all that data is so daunting that we just don’t do it very often, if at all.
Stage 2, in particular—manual integrated reporting—is one of the most difficult stages to move through as we journey toward a more mature measurement capability. Most of us are diligently trying to advance, but the vast majority of us get stuck in cut-and-paste wasteland trying to cobble some sense out of all the reports our various execution tools spit out.
Knowing Whether You’re Stuck in Stage 2
How do you know if your marketing organization is stuck in the Stage 2 wasteland?
Here are seven telltale signs:
- It’s almost August, and you’re just wrapping up the Q1 omnichannel performance report.
- Your team literally calls reporting “hind-sighting.”
- You and your team spend so much time preparing the data for reporting, you have no time left for analysis or insight.
- Your retainer with your agency for “reporting services” is nearly as large as your media budget.
- You’re worried that your rock star marketing analyst is going to get fed up and quit.
- The Marketing Intelligence team might as well just rename itself the Marketing Cutting and Pasting and Data Plumbing department.
- Ad hoc questions from the CEO or CFO like “What did we get out of our summer ad blitz campaign?” feel like taking a bullet. You see a three-week analysis ahead that will eat up all of your team’s time and focus.
If you think your team is stuck in Stage 2—the awkward stage of marketing measurement where everything is manually cobbled together for insight—know this: You’re not alone.
The amount of marketers who believe our organizations have extremely well-developed data tracking and analytics capabilities stands at just 12%, according to the Leap Frog 2014 CMO Digital Benchmark Study. A recent NG Chief Marketing Officer’s Summit Intelligence report cites analytics (web/customer/predictive) and business intelligence/data insights as the top two investment priorities among marketing leaders. This is a clear investment priority.
Moreover, large-company CMOs said that in three years, 14% of their marketing budget will be set aside for analytics—a 75% increase over today’s investment levels:
If you are stuck in Stage 2, the good news is, at least you’ve moved past Stage 1, which means you’re still doing better than most. You understand that looking at data in silos isn’t getting you anywhere. You’ve recognized the need to pull together data from all your execution tools and are trying to unify them.
Understanding Stage 3
To get out of Stage 2, your organization needs to automate the processes and the data cleanup that you’re doing manually now. Systems generate all the reams of marketing data coming at you, so you need systems to help us process, clean, and make sense of it. Doing so is the only way to move your organization to a more real-time view of your data, so your teams can use the data to act and optimize rather than just describe the past.
Once marketers automate the aggregation, normalization, and blending of data sets, the (analytical) sky’s the limit.
Once you hit Stage 3 and have real-time, clean and normalized data from all channels at your fingertips, now you can understand your baselines and benchmark. Now that you have a feel for the daily rhythm of the business, now you can turn campaigns on and off to measure incremental lift over those baselines.
Now you can attribute revenue to the content and offers that generated that lift. You can set goals and targets, track daily or weekly progress toward those goals, and generate ideas for rapid optimization. And now (and only now) do you have a dataset that you can rely on to begin to predict sales based on various marketing investment scenarios.
Marketing measurement is a journey. As long as you know where you are on the journey, then you can visualize and put a plan in place for where you need to go next.