Ask most people what they are looking for in insight reporting and they will tell you that it’s storytelling.
Telling a compelling story is an important skill that market researchers today need to master, but doing so presents a couple of significant challenges. First, finding the story in the data is not always easy. Secondly, market research pros need to move beyond the story to create insight.
This post addresses the first issue. Here are seven tips for market researchers on how to find compelling stories from your data.
Some market research pros seem to have a great ability to find the story in the data, but in many cases they struggle to explain how they do it and explain their method.
Frameworks can make finding the story more efficient and effective. A framework is a systematic model of how data should be organized, analyzed and linked to the business question and the wider context.
David Smith and Jonathan Fletcher’s The Art and Science of Interpreting Market Research Evidence provides a useful overview on the use of frameworks for analysis. The book highlights the need to identify what is already known and what actions are intended to follow from the project. It also suggests specific processes for checking, organizing and utilizing data, for example assessing what is available, the best way to check the validity of different information sources, and the use of triangulation to increase the reliability of conclusions and advice. As the title suggests, the process of finding the story for insight is a combination of art and science. The science part relates to understanding the parameters of the information, but the insight still requires the researcher to draw upon their own creativity.
In many ways the process is like baking; baking is basically chemistry, requiring the right ingredients in the right proportions at the right temperature for the right length of time. But great baking is more than chemistry; it is also creativity and skill—the same is true for finding the story in the data.
Start with the big picture.
Too often, when people look at data, they go straight to variances. For example, they may look at differences between users and non-users, young versus old, north versus south, and so on. These differences often produce nuggets of information, but without a wider context, they will not produce a story.
In most cases the bedrock of the story will come from an understanding of the bigger picture. For example, how many brands dominate the market? What is the purchase cycle for the sector? What are the main strengths and weaknesses of the sector? For example if we were researching a utility market we might find that 70 percent of the market used just three suppliers, that 80 percent of customers have not changed supplier during the last five years, and that 90 percent of people do not know the prices being offered by other suppliers. Answers to these questions can help identify the story behind the data. These sort of information would illustrate the big picture, which suggests that the market was very static and although people might be worried about prices, most people do not behave as active shoppers (for example by contrasting prices and changing supplier). The detail of the story might then explore why most people were unengaged, which customers were behaving like active shoppers and what events tended to create active customers.
Find a strong story.
Your story should be memorable and facilitate action. Avoid stories that are too subtle or too convoluted.
For example, consider this story: “Twenty percent of millennials prefer X to Y, compared with 15 percent among other groups.” The key problem with this story is that it is not true for 80 percent of millennials; liking X is NOT a characteristic of most millennials.
A better approach is to look for stories that mean the same thing when they’re simplified. That’s because when a story gets to other parts of the business, it tends to become simpler. The simple form of “most millennials prefer X” is “millennials prefer X,” which is a usable simplification. The simplification of “20 percent of millennials prefer X” is often “millennials prefer X,” which is usually not a usable simplification.
If there are no strong messages in your data, try re-organizing or re-framing it. For example, you might say something like the following: “most millennials who express a clear preference prefer X to Y,” or “people in their 20s prefer X to Y.”
- Stick to one key story backed by a few points.
A market research story should not contain multiple plots. Look for the key story that addresses the organization’s business problem and provides either a solution or a direction. As an insight pro, it’s your job to reduce multiple plots into a single, cohesive story.
Most audiences are busy and need to grasp the key elements of the story. Equally importantly, different people within the organization need to share an understanding of what the key points are. Therefore, I recommend limiting your number of key points to three—ideally supporting each key point with two clear pieces of evidence. Identifying no more than three points helps save your audience time and helps to highlight your specific findings.
- Link your story to your company’s big concerns.
Stories are more memorable and powerful if they build on what your audience already knows and already accepts. Identify your audience’s assumptions and their current belief structures. Link your story to the organization’s pre-existing learning or beliefs.
Equally important is to consider the things that are currently “not believed” (or not said) by your audience. Say for instance that you’re working with an organization that does not believe that they’re inferior to competitors. If you pin your story to something that relates to the competitor being better on some features, you may well be right, but getting your audience to accept your story will be a challenge.
People writing a novel or a play only need to produce an engaging and interesting story. That’s not true for market research professionals. You have a specific mission, which is to create stories and communicate information that will allow an organization to make better decisions. The stories we create from the data need to be useful. Our stories are based on evidence—and they should focus on business problems that need answers.
Build on what your audience already knows.
Your story should feel like it is consistent with what they already know. It should build on assumptions that your audience already holds.
For example, consider smartphones and Japan. Most people are aware that Japan is a technologically advanced and rich country, so the assumption is that it would be like South Korea and Singapore and be a heavy adopter of smartphones. However, compared to rich Western countries, it has a relatively low smartphone adoption rate. So, the best way to convey this story is to build on what the audience already knows. Here’s how I would do it:
- Japan is a technologically advanced country
- Even before the advent of the modern smartphone (i.e. the iPhone), it had phones that were smart and the adoption of mobile phones was around 95 percent.
- This meant that services and users evolved a set of preferences quite different from the rest of the world. Just like the way Darwin reported on different evolutionary routes for creatures in the Galapagos Islands.
- When smartphones came on the scene, the Japanese consumer was faced with the prospect of losing many of their established services and options in order to adopt the global trend.
- Many Japanese customers decided (at least for the time being) to stick with local options, which are now known by many people as “galake” (Galapagos phones).
In this example, instead of simply asking the audience to accept a surprising fact about Japan, I used their knowledge about Japan being an early adopter of technology and the mechanics of Darwin to show that Japan is currently in a different place to other, leading, Western countries.
Turning data into an interesting, useful story is the first step to gaining business insight. The tips I provided here will help you identify what’s compelling in your data in a way that helps move business decisions forward.