Turning Data into Customer Strategy – Three Key Disciplines
The sheer volume of data available to organizations is revolutionizing the way we think about customers. Businesses have more information than ever before. But many are struggling with how to interpret it and what to do with it.
In our with businesses and government agencies working to be more effective with their customers and stakeholders, we've seen three key disciplines that are essential in leveraging data to create an effective customer strategy: (1) using data scientists, (2) adding the customer context and (3) creating strategy with true innovation.
1) Using Data Scientists to Mine and Interpret Data. Never since the advent of computers has the prevalence of “Garbage In, Garbage Out” been so important to business. In this case, it's garbage data going into the strategic planning process. It seems suddenly everyone with reams of data feels qualified to interpret it. However, pulling data points or trends without academic training is likely to generate spurious results. As much as ever, it is critical to have data scientists on your team (and here I use the term broadly to include academically-trained statisticians, survey researchers, and so on) to ensure your data mean something. Data scientists have critical academic training on such key areas as avoiding sample bias and/or making use of nonstatistical sampling, understanding multivariate analysis, eliminating question bias, interpreting correlation compared to causation, and a host of other issues. This expertise is critical to making sure your data are meaningful in extrapolating the trends, desires, and actions of your customers.
Don't build a bridge without an engineer. Don't try to mine and interpret data without a data scientist.
2) Putting Data into the Customer Context. Once you have meaningful data, they have to be interpreted in the customer context, or you risk completely erroneous results. This was demonstrated to me strongly in a case of government health research.
A health researcher in Africa told me how understanding the "customer" was key in understanding their health surveys for a certain African nation. The organisation was trying to determine the level of understanding in rural communities about how HIV was spread. They asked the survey question, “Would you send your child to school if the teacher was HIV-positive?” To this question, 90 percent of the parents said, “No," thereby “proving” to researchers that despite education campaigns in rural communities, there remained a strong stigma and misunderstanding about how HIV was transmitted. However, bring in cultural context… a local researcher explained that in rural communities in this nation, it was sadly common (or at least rumoured) that male teachers were likely to prey sexually on their students. So this question left researchers uncertain whether parents were worried that HIV would be transmitted sexually or just through casual interactions. To solve this, the researchers changed the question to “Would you send your child to school if the female teacher was HIV-positive” (in the local language, the word “teacher” could never be gender-neutral, and was always masculine or feminine). Culturally, female teachers were not rumoured to prey sexually on their students. This question led to the opposite result -- 90 percent of the parents said “Yes.” In this case “customer” knowledge was essential to understanding the true feelings that the initial data didn’t show.
The same is true for all kinds of data. Interpreting and predicting customer trends in a sterile environment, without professionals who truly understand the customer experience, may easily lead to mistaken interpretation and therefore the wrong strategies. It’s key that your strategy team includes people who really understand the customer.
3) Using Innovation to Create Strategy. And finally, even if you have scientifically relevant data interpreted perfectly for customers, generating a good strategy requires creativity and innovation. The obvious, linear solution is often not the best one. Recall the metaphorical buggy whip designers focused on improving the product while people were moving to automobiles. Sometimes, the right strategic approach is a nonlinear leap from the original problem.
In the 90’s, I was working with a client trying hard to improve the response rate of a marketing brochure. We agreed an approach and a budget, and then called in our creative team to create the improved design. Our creative director’s immediate response was that a brochure was the entirely wrong way to reach our target audience (in this case, the audience was early adopters of new technologies). He suggested a completely different approach, using a new web-based approach, which was less expensive, had broader reach, and was much better targeted at our tech-savvy target audience (the web was fairly new to most of us at the time).
This was a technology solution, and it's important to realise that sometimes the innovative approach does not mean a new technology, but a whole new way of solving a problem. Indeed, people who always equate “innovation” to “the latest technology” are missing the point. Innovation means thinking in new and startling ways (which often become obvious once proposed). We learned our lesson and then always involved our most creative professionals from the onset to help us decide what to do, not just how to do it. If your strategy is lacking, consider whether you've involved those highly creative professionals that can solve customer challenges and make the most of customer trends in more effective ways than the simple linear solution.
So, is your customer strategy generating the results you expected? If not, evaluate your approach to determine if you have overlooked one of these three key disciplines in turning your data into strategy.