8.21.17
Data-informed Versus Data-driven Strategy
Carolyn Lodge
In today’s business climate, data is essential. From the importance of data on which to base business decisions to the data that ultimately helps us test and optimize campaigns to the data that eventually shows a return on investment—we know data drives business. It’s not always cut and dry, which makes the interpretation of data and context even more important. The way we approach data is best when we remember that we are in the business of influencing human behavior. Rather than being data-driven, we focus on being data-informed.
Data-informed means you’re looking at data as part of the analysis with full recognition that sometimes it won’t tell the whole story. The need to rely on data is imperative, but we don’t look at data without remembering that the motivations behind consumer behavior are our biggest challenge. You can’t tell the whole story of how a human behaves, reacts, or is motivated by using data alone.
Sometimes data-informed means the data is going to set us on our track. It may lead us down a path versus being the end result. Data informs the direction, but it’s not the only thing that informs the strategy.
Testing recommendations
When we form recommendations, we analyze data across industry, category, and product to analyze the current situation. That data gives the baseline of what is happening within the segments in which we’re working. We review consumer behavior data—how people are making decisions and consuming media. Then we look at psychographics—how attitudes and aspirations in the context of current events affect the way decisions are made. We examine how these things weave together, gathering all the information needed to develop our strategy.
In a perfect world, we have the budget for surveying, brand audits, and other methods to test our recommendations. Depending on the project, that testing can be accomplished with a variety of methods, including full, large-scale brand audits, focus groups, or online surveys. In a scenario where the budget does not allow, we will not let our recommendations sit in an echo chamber of the individuals who first thought of them.
Instead, we ask more questions. Part of our process is to dig into data, but we also internally question our own strategy and make sure it’s not just our first assumption. We key back into data to see if the research supports our conclusions. Since we’re in the business of influencing human behavior, the whole idea is that data cannot tell the whole story of how humans behave.
We will never completely diverge from what data tells us, but each situation depends on the kind of data presented and the source of the data. If we come across research or data that we strongly disagree with, we do more research to understand a piece of information behind the data in an effort to learn more of the story.
The data may say one thing, but we know that within any dataset there are always limitations. You have to consider your sample size and the way in which questions were asked to interpret the results. Even if you’re using a highly-respected resource, the human element is always a variable.
Assessing data integrity
For a recent client, we were provided a dashboard of data on their audience and performance. We used that data to identify the trends within their business. If we hadn’t taken the time to dig into the integrity of the data, we would have made far different conclusions and misguided the client.
When those situations happen, we dig deeper. We don’t take data as the straight answer without asking additional questions. We look to other sources for verification. If we diverge from the original data, it’s through researching more sources to confirm or disprove one study’s conclusions.
Not all data is created equal. You can’t always trust data at its surface level, and you can’t treat all sources equally. Not only are we focused on finding data to inform our recommendations, but we’re also finding the appropriate data.
In our process, we talk through ideas and voice our assumptions, asking, “What do you think about this?” Then we look for things to confirm or disprove our assumptions, keeping ourselves in check. You can always find some data to support your conclusion, but that doesn’t mean that conclusion is tested or validated.
Balancing intuitions with numbers
Having a “good feeling” is never enough to sell a strategy to a client. The art of business is understanding how humans make decisions. In a survey or another situation where we’re gathering data, participants may respond with what they think you want them to answer rather than their own, honest answers. Just like a personality test, they may not answer from a purely self-aware mindset. Even further, focus groups are often more of a study on group dynamics than on the particular material being tested.
Ultimately, data is information. But we’re not just looking for information; we’re looking for insight. If you’re data-informed, you are using your gut to an extent in every situation. Following your gut means using the information to find the insight—informing the situation based on the data you have. Otherwise, you’re using an approach that is purely by numbers or by the book. From a demographic standpoint, that might be okay. But there are pitfalls in every area of marketing
For example, we study media ratings using Nielsen Diaries. Nielsen offers the best tool available, but it’s not a perfect tool. We can’t rely on that data as our only source to make our decisions. There are anomalies in every book and variables that must be considered. In some markets, we have so few diaries that making wholesale decisions based on that data is dangerous. Instead, we use that data and leverage our own knowledge and experience to form the best recommendations.
Learning from experience
I know I’m not a person that deals well in grey area; I work better when things are black and white. One of my biggest takeaways in this business is learning that marketing involves a fair amount of grey area, which reiterates the importance of treating this business as both an art and a science.
Using data to inform recommendations makes me more comfortable, knowing they’re rooted in that information. This business thrives on budgets and analytical recommendations, but it also finds life in the creative aspect, which is pure heart. It’s where those two things converge that campaigns are really striking.
The data component is absolutely necessary for efficiency. If you don’t care about efficiency—you just want something funny or heart-warming—then you can do that without worrying about strategically reaching your target. But to be effective, you have to be strategic. Data helps us be responsible with our clients’ money and ultimately drives the efficiencies that help them reach their business goals.
The spirit of our philosophy and approach is truly in collaboration. If we think of data as one of our collaborators, it becomes an integral piece in finding the insight that leads to more results.