But that is not the only reason to use stories. Time and time again in our experience, stories have been more than an afterthought; they have actually enabled a more rigorous analysis of data in the first place. Stories allow the analysts to construct a set of hypotheses and provide a map for investigating the data.
We recently worked with a department store retailer and a team of analysts looking for creative insights into customer loyalty. We started with a storyline, a narrative hypothesis, according to which a customer experiences different journeys through the department store over time and rewards the retailer with a certain level of loyalty.
How will these journeys unfold? Does the customer start in cosmetics and then move into clothing? Does she go from the second floor to the first floor to buy a handbag to match a new outfit? Does she have shopping days where she takes a lunch break in the restaurant before continuing her shopping? Do less loyal customers make different journeys from more loyal ones?
In other words, we were interested not just in what customers were buying, but in the mechanics of how they make their purchases and how this may make them loyal. After the analysis, the true story of a customer’s path to loyalty is in fact revealed.
Where do these stories come from? In our experience, they can come either from the experience of an expert in the sector or brand, as was the case in the previous example, or from qualitative research using observation or in-depth interviews.
We recently advised a telco client in developing the “jobs to be done” for a range of new products and services. We interviewed consumers and heard their own stories of how they go about using their mobile devices throughout the day. The general narrative hypothesis we drew from listening to these stories is that consumers cobble together mobile solutions to suit their lifestyles.
One consumer revealed that he actually owns two SIM cards for the same smartphone and told us in what context he changes from one to the other.
Another customer told us about the parental control and other relevant apps and browsing that she has discovered and collected and which facilitate her lifestyle as a mother.
What we are seeing here is a multi-usage context (characterized by two SIM cards) and a mobile-mommy context, each of which calls for a distinct analysis and possibly different products/services to be developed subsequently.
In other words, we found that the customers’ homemade solutions could be used to identify what kind of data to gather and what kind of analysis to perform.
The analyses will, in turn, enrich the initial stories and lead to deeper insights. What is important here is that the storyline, told before the analyses, enables an authentic human element to surface that would be more difficult to extract from the data alone
In order for a story to truly enable analytics, the story development process needs to be rigorous. We use the framework of Grounded Theory to ensure that the data and overarching storyline inform each other and are coherent with each other. The idea is for the analyst to navigate back and forth between the data and the developing story to ensure a good balance between the creative narrative and the analytics that reveal the facts and details of the story.
The enabling storyline should not be too restrictive: it needs to support the development of the plot and characters as they emerge from the analysis, but without bias. Conversely, the storyline can suggest specific questions to be asked of the data for a more in-depth analysis.
In a world that’s flooded with data it becomes harder to use the data; there’s too much of it to make sense of unless you come to the data with an insight or hypothesis to test. Building stories provides a good framework in which to do that.
Authors: Judy Bayer & Marie Taillard
