BY LISA BRINK – DIRECTOR, CUSTOMER STRATEGY AND INSIGHTS
There’s quite a bit of conversation going on today about how big data might just lead to the end of traditional primary research. But I challenge us to think differently. Behavioral data and traditional primary research are two powerful functions that can complement each other quite nicely, and bolster insights exponentially. It doesn’t have to be an “us-versus-them” mentality or merely a complacent cohabitation. In fact, they can be quite transformative when working together.
Most organizations today aren’t set up to fully take advantage of these two functions working together. Behavioral data and primary research tend to have separate learning plans and objectives, and to complicate the situation, they can live in different parts of the organization. Setting aside organizational structure implications, these two areas can learn to partner without org changes and reap the benefits from joint problem solving.
One benefit of using behavioral data and research together is that you get to explore and understand the “what” and the “why” behind a particular situation or phenomenon. Behavioral data tells you what people are doing—you don’t need to rely on them to remember—through general interactions with the brand (e.g. shopping, purchasing, customer service calls, loyalty program participation, etc.). Research can help with the “why” behind behaviors and prove or disprove hypotheses. Together they can tell the whole story, separately there may be gaps or incorrect conclusions drawn.
An example of this is as follows. A few years ago, I read a great article about a project that was using technology and data to detect cholera outbreaks in Rwanda. Nathan Eagle, now an American technology executive and adjunct professor at Harvard University, set out to study how technology could be used to detect and maybe even predict cholera outbreaks. Using data acquired from mobile phones, he began studying people’s commuting patterns between and within villages. He began to see a pattern in his data: rather abruptly, people’s movement would just cease. He hypothesized that some sort of disease or illness had broken out in a village and subsequently halted typical commuting behaviors.
Working with the Rwanda Ministry of Health, he compared actual flu/cholera outbreaks to what he was seeing in his data. The changes in commuting patterns did, in fact, correlate to the outbreaks. But after some time, he discovered that his model wasn’t actually predicting a cholera outbreak—it was instead predicting when a village was experiencing a flood—which subsequently provided the perfect environment for an epidemic. He determined that his initial approach had missed an important element: he had not paired the mobile phone data with any form of on-the-ground research, such as administering surveys to people who could actually provide input on what was happening. He then modified his approach and developed tools via a mobile application to collect self-reported information from people on the ground as soon as he saw changes in commuting patterns. Without collecting the self-reported data (primary research) and connecting those insights to his mobile phone data set (behavioral data), he would have continued to misinterpret the cause of the commuting pattern changes and possibly made inaccurate outbreak predictions. He now regularly incorporates this insight when making predictions.
This is a great example, albeit a more global one, of how behavioral data and primary research should work together. Let’s think about the everyday application of this in generating consumer and strategic insights.
Consider a problem that you’re trying to solve in your organization. Imagine someone comes to you and requests your help in determining why a loyalty program isn’t working as well as expected. Your immediate reaction is to go out and talk to loyalty members—to conduct focus groups. It’s easy. All you need to do is pull a sample of customers that are in the program, ask a few qualifying questions, and bam, you’ve got butts in seats for the focus group.
While it’s certainly not a bad idea to go out and talk to your customers about your loyalty program, think about the valuable insights you might garner by first embarking on a thorough analytical study of how the program is being utilized today. In studying the behavioral data, you’ll get a sense for how customers are interacting with your brand and with your loyalty program. You’ll know who was using it and how often, and also understand their shopping and purchasing behavior. This will help you identify who you want in the focus groups. For instance, you might determine that you want to talk to your loyalists (extreme users of the program) and get a sense for why they use it and how it could be improved. Then, juxtapose that with talking to another group of members who are infrequent users of the loyalty program, yet are frequent purchasers of goods / services from your company, to understand how they could love the brand, but not enjoy the benefits of the program. Doing this will help you make sense of what’s working well and what potential barriers exist to using it. You might find it could be as simple as an awareness problem or a misunderstanding of how the program works. This insight can lead to relatively minor tweaks in your strategy, rather than wholesale changes to the program.
Starting with the behavioral analysis gives you insight into what your customers are doing with the program. That knowledge allows you to be more strategic and efficient with your primary research and focus on more meaningful questions to help uncover the “why” behind their actions, motivation, and needs. This winning combination leads to stronger engagement and growth strategies.
In the end, it’s not about choosing between behavioral analytics or primary research, or saying that one is better than the other. The truth is that both are far more effective when applied together to solve the challenges we face in marketing today. I don’t know about you, but I’m excited about the possibility of venturing into a brave new world where research and behavioral data are happily entwined.
A Brave New World
BY LISA BRINK – DIRECTOR, CUSTOMER STRATEGY AND INSIGHTS
There’s quite a bit of conversation going on today about how big data might just lead to the end of traditional primary research. But I challenge us to think differently. Behavioral data and traditional primary research are two powerful functions that can complement each other quite nicely, and bolster insights exponentially. It doesn’t have to be an “us-versus-them” mentality or merely a complacent cohabitation. In fact, they can be quite transformative when working together.
Most organizations today aren’t set up to fully take advantage of these two functions working together. Behavioral data and primary research tend to have separate learning plans and objectives, and to complicate the situation, they can live in different parts of the organization. Setting aside organizational structure implications, these two areas can learn to partner without org changes and reap the benefits from joint problem solving.
One benefit of using behavioral data and research together is that you get to explore and understand the “what” and the “why” behind a particular situation or phenomenon. Behavioral data tells you what people are doing—you don’t need to rely on them to remember—through general interactions with the brand (e.g. shopping, purchasing, customer service calls, loyalty program participation, etc.). Research can help with the “why” behind behaviors and prove or disprove hypotheses. Together they can tell the whole story, separately there may be gaps or incorrect conclusions drawn.
An example of this is as follows. A few years ago, I read a great article about a project that was using technology and data to detect cholera outbreaks in Rwanda. Nathan Eagle, now an American technology executive and adjunct professor at Harvard University, set out to study how technology could be used to detect and maybe even predict cholera outbreaks. Using data acquired from mobile phones, he began studying people’s commuting patterns between and within villages. He began to see a pattern in his data: rather abruptly, people’s movement would just cease. He hypothesized that some sort of disease or illness had broken out in a village and subsequently halted typical commuting behaviors.
Working with the Rwanda Ministry of Health, he compared actual flu/cholera outbreaks to what he was seeing in his data. The changes in commuting patterns did, in fact, correlate to the outbreaks. But after some time, he discovered that his model wasn’t actually predicting a cholera outbreak—it was instead predicting when a village was experiencing a flood—which subsequently provided the perfect environment for an epidemic. He determined that his initial approach had missed an important element: he had not paired the mobile phone data with any form of on-the-ground research, such as administering surveys to people who could actually provide input on what was happening. He then modified his approach and developed tools via a mobile application to collect self-reported information from people on the ground as soon as he saw changes in commuting patterns. Without collecting the self-reported data (primary research) and connecting those insights to his mobile phone data set (behavioral data), he would have continued to misinterpret the cause of the commuting pattern changes and possibly made inaccurate outbreak predictions. He now regularly incorporates this insight when making predictions.
This is a great example, albeit a more global one, of how behavioral data and primary research should work together. Let’s think about the everyday application of this in generating consumer and strategic insights.
Consider a problem that you’re trying to solve in your organization. Imagine someone comes to you and requests your help in determining why a loyalty program isn’t working as well as expected. Your immediate reaction is to go out and talk to loyalty members—to conduct focus groups. It’s easy. All you need to do is pull a sample of customers that are in the program, ask a few qualifying questions, and bam, you’ve got butts in seats for the focus group.
While it’s certainly not a bad idea to go out and talk to your customers about your loyalty program, think about the valuable insights you might garner by first embarking on a thorough analytical study of how the program is being utilized today. In studying the behavioral data, you’ll get a sense for how customers are interacting with your brand and with your loyalty program. You’ll know who was using it and how often, and also understand their shopping and purchasing behavior. This will help you identify who you want in the focus groups. For instance, you might determine that you want to talk to your loyalists (extreme users of the program) and get a sense for why they use it and how it could be improved. Then, juxtapose that with talking to another group of members who are infrequent users of the loyalty program, yet are frequent purchasers of goods / services from your company, to understand how they could love the brand, but not enjoy the benefits of the program. Doing this will help you make sense of what’s working well and what potential barriers exist to using it. You might find it could be as simple as an awareness problem or a misunderstanding of how the program works. This insight can lead to relatively minor tweaks in your strategy, rather than wholesale changes to the program.
Starting with the behavioral analysis gives you insight into what your customers are doing with the program. That knowledge allows you to be more strategic and efficient with your primary research and focus on more meaningful questions to help uncover the “why” behind their actions, motivation, and needs. This winning combination leads to stronger engagement and growth strategies.
In the end, it’s not about choosing between behavioral analytics or primary research, or saying that one is better than the other. The truth is that both are far more effective when applied together to solve the challenges we face in marketing today. I don’t know about you, but I’m excited about the possibility of venturing into a brave new world where research and behavioral data are happily entwined.
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