Dennis Hecht, Chief Intelligence Officer, Rooster Strategic Solutions

“A classic is something that everybody wants to have read, and nobody wants to read.” – Mark Twain

For many companies, an analytic story resembles Mark Twain’s definition of a ‘classic.’ We all know that it’s important and that it can help improve sales performance, but few are willing or have the tools necessary to effectively understand or use what the data is telling them. Unfortunately, much of the blame falls on our inability to define the story. We might do a great job pulling diamonds from the data mine, but if we can’t communicate the results clearly and concisely to company executives and non-marketing folks, it’s not worth much.

In previous installments of this series, we explained how to create a Customer Data Platform (CDP), how to turn that data into shoppers, ways to identify where prospects are in the purchase lifecycle, and how to create predictive equations from your available data. Not it’s time to turn to storytelling.

Why your data story may be a nightmare. I presented to a client several years ago who admitted halfway during the meeting “I don’t really know what any of this means.” At first, I was stymied. My multi-slide power-point was very detailed, chock-full of advanced metrics and KPI’s and the latest in data harvesting and analysis techniques. But the marketer in question got lost in the details and didn’t understand the story I was trying to tell. At least he was honest. Many audiences would likely keep feigning interest while they checked e-mail.

And this was a presentation to a marketer with decades of experience in e-mail, programmatic, and other data-fueled tactics. Imagine how confusing this presentation would have been to non-marketing folks?

We must learn to tell a better data story, creating a comfortable space with easy-to-understand access points for business, technical, and non-marketing people to come in and see how the data can work for them. Fortunately, there are some easy fixes. In fact, there are only three components of a good data story.

Start with the why. Before you begin putting together a presentation, revisit the goals from the campaign. What were you trying to achieve? This will help you decide what to measure as well as what to highlight in big, flashing letters on the first slide. Bringing it back to the goal helps encourage the audience to focus on what really matters and helps prevent “paralysis analysis.” The purpose of any campaign should be tied to one goal with one to five KPIs that determine success. Lead with those.

Put your KPIs in context. It’s worth restating that the KPIs should be established before the campaign is launched, and that there should only be a handful; three to five KPIs is as many as you should try to explain. And the key word here is ‘explain.’ It’s not enough to point out that we got 15-percent opens, 5-percent clicks, and 1-percent web page registration. Is this good? How did it compare to previous efforts, or to an industry average? If this was an acquisition effort, how many of the responders were first-time visitors? And most important, did the result meet or exceed our goals? If it’s a good KPI, you should be able to explain what’s happening in the background through monitored trends and triggers that inform why we did or did not achieve that KPI.

Don’t forget the “now what.” What have we learned? What actions can we take and measure that will help us understand if the trend is moving in the right direction? The key is democratizing the data, boiling down insights and making them actionable to everybody in the company, no matter how data-literate they may be. Good storytelling resonates with the business users and enables them to make decisions.

Some data people are guilty of thinking of data as a protected asset that can’t be understood by – or shared with – the general public, or that it’s simply their job to present findings, as opposed to recommending strategy. These are both wrong. If you’re telling a data story, you should be spending more time on understanding the goal and the components than on drawing and crafting a presentation. If you do this, telling the story should be easy because you’ve already thought out the whole thing. And because you’re intimately aware of both the campaign strategies and the overall metrics, you’re in the best position to recommend ideas or tweaks that could improve future performance. Even if your ideas aren’t implemented, you’ve proven that your team is more valuable than one that simply spits out results.

Bonus tip: Create a one-slide synopsis. I fully understand how difficult this is. But if you can boil down your presentation into a single slide or an “elevator speech,” it can be attached in an all-company e-mail or posted on the breakroom wall. Not only will this ensure that everyone in the company understands what you’re doing – which brings even more credibility to your team – but it increases the likelihood that others who might not ever see a full presentation will be encouraged to and questions and develop strategies based on the work you’ve done.

If you want to refine – or overhaul – your data science storytelling, or if you’re not even sure where to begin, I’d love to start a conversation.