When I’m asked to describe what we do at Talisman Insights, my response (after saying “surveys and focus groups”) is a variation of the following: “we offer data-driven insights to fuel organizations’ growth.” If I have more time to dive deeper into our process, I will also talk about how we help develop data-informed strategy. It’s easy to think that I’m talking about the same thing, except that I’m not.

“Data-driven” and “data-informed” are more like cousins than they are siblings. They have a common origin (i.e., data of one sort or another), but they have slightly different journeys. To call “data-driven” deductive logic and “data-informed” inductive logic oversimplifies the relationship even though it moves us in a broadly valid direction.

Instead, I characterize them in the following way:

  • Data-driven: stays close to the data gathered with insights driven both by deductive and inductive logic. That is, we assess what we know based on the data (deductive logic) and how that applies beyond our sample (inductive logic). 
  • Data-informed: takes more scientifically-gathered data as one element in a broader context that includes operational capacity, experience in the category, knowledge of customers, investor reaction, competitor reactions, and potentially many other factors.

A data-driven action hews more to what you know from the data itself (or other basic research like Prospect Theory) and less on what else you know. A data-informed action draws from additional context relevant to the actions or decisions, using them in combination with the data.

A common occurrence where you can see the dichotomy between data-driven and data-informed is in segmentation research. We will conduct a segmentation study, use statistical analyses to find actionable segments, and then write about those segments. We almost always include a verbal description of each segment prior to the deep-dive into their results. The end result is often something like:

Meet our Terrific Technologists. These younger men tend to live urban settings and have jobs across a wide variety of industries in marketing or IT. They believe that having the latest technological toys brings them fulfillment and happiness. While they don’t enjoy shopping per se, they will spend all day in a Fry’s, a Best Buy, or store that caters to their particular interest. Video games are a great way for them to relax along with watching the latest shows on their favorite streaming services.

The above description sticks closely to our theoretical segmentation. You can probably guess the skews and over-indexing demographics and psychographics from the write-up (A25-34, male, lives in urban area, employed full time in marketing or IT; early adopter of technology, hates shopping in every category except technology; plays video games to relax, etc.).

This kind of write-up works well for the clinical delivery of the segmentation solution. It gives your team an inkling of how to craft brand strategy and products that are more apt to please this segment than not. On its own, it works. But. It lacks pop; it’s clinical; it feels inhuman.

The next step in making a segmentation actionable is the creation of the persona. A persona steps away from using only the data in the study to craft a write-up. It asks the additional question of: who do we know that is a Terrific Technologist? We’ve had clients that actually name a persona from the name of their actual customer who they believe best embodies the segment.

A persona based on our fictional Terrific Technologist might look something like this:

Meet Thad. He absolutely has to have the latest technology in hands the day before it comes out. People come to Thad to ask him about what he thinks about a new piece of technology or to help them figure why “damned computer isn’t doing what it’s supposed to do.” Thad isn’t an IT professional but he could be if he wanted. That’s how much he knows. Right now, Thad is thinking about where his career is going to next. He’s debating about taking a new role or some time off to go after his MBA…or maybe doing both. He’s a work hard-play hard guy. Even though video games are one of his favorite ways to relax, Thad is more than happy to round up his friends to play a pick-up game of basketball. Shopping in a store is not Thad’s thing. He uses multiple resources to shop online before he goes into the store to pick up what he’s truly after.

“Thad” in our persona above would be based on the data and then multiple people who feel like a “Terrific Technologist.” These people could be your customers, your friends, your family members, or even your co-workers. The point is that you use the data as a starting point and then bring in other bits and pieces of information that carry almost equal weight.

I’ve mentioned more than one that our credibility as researchers hinges on reliable data and the use of our knowledge about psychology, economics, and sociology (among other fields) to help our stakeholders and clients achieve their business goals. As researchers, the data drives our development of insights and thoughts around their implications for a business. This builds our credibility because we can point to solid evidence and clear logic to support our position. However, we are more than researchers.

As marketing insights professionals, we support a business, which means we need to think about externalities beyond the data. I had such an event appear early in my career as a corporate-side researcher. We had worked with external agencies to develop and test several creative expressions of one of our brand’s positionings. The winning creative expression stood out along all the key metrics, outperforming the others by far. There was one big problem: it would be, by far, the most expensive of the three we tested to execute at the level envisioned in the test materials. Budgetary concerns overrode the data. We used the data in the research to help hone the creative expression our leadership chose to incorporate some of the elements of the winning expression to the extent possible within the budgets. That is the essence of data-informed decision making.

As researchers and business professionals, your insights and implications are data-driven with your ultimate goal as helping your clients, stakeholders, and leadership make data-informed decisions.


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