Storytelling and market research: 8 tips on how to find the story in your data
Posted by Ray Poynter, 4 August 2021
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 eight tips for market researchers on how to find compelling stories from your data.
1 Use frameworks
Some people seem to have a natural ability to find the story in the data without having a ‘system’. However, in many cases these savants struggle to explain how they do it and explain their method. The alternative to intuition is a framework, and a framework is something that everybody can utilise.
Frameworks can make finding the story more efficient and effective. A framework is a systematic model of how data should be organised, analysed 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, organising and utilising 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, the right ingredients, in the right proportions, at the right temperature for the right length of time are requirements. But great baking is more than chemistry; it is also creativity and skill – the same is true for finding the story in the data.
2 Start by Defining the Question
If you don’t really understand the questions, you are unlikely to recognise the answer, even if you find it. To understand the question you need to understand more than just the brief, you need to understand what the underlying business question is, what is already known, what is believed, and what actions are going to follow from the answer.
3 Find the big picture
Too often, when people look at data, they dive straight into the weeds, looking for interest differences and 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? If we were researching a utility market, we might find that 70% of the market used just three suppliers, that 80% of customers have not changed supplier during the last five years, and that 90% 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 sorts of information would illustrate that the big picture was that the market is 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.
4 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 age 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, something I call robust simplification. This is 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-organising or re-framing it. For example, you might say something like the following: “Most millennials who express a clear preference prefer X to Y.” Another way to make the story stronger is to be more specific, if you find X is liked more by young people, more by men, and more by people in New York – have a look at young, men in New York, you may find that this is the group who really like it, and who are driving the other scores.
5 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 organisation’s business problem and provides either a solution or a direction. As an insight professional, 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 organisation 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. All the other information can be sent separately, or included in an appendix.
6 Link the 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 organisation’s pre-existing learning or beliefs.
It is also important to consider the things that are currently “not believed” by your audience. Say, for instance, that you’re working with an organisation 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. Craft your story in a way that does not need the audience to change their beliefs at the start of the story.
7 Facilitate action
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, and that mission is to create stories and communicate information that will allow an organisation to make better decisions. The stories we create from the data need to be useful. Our stories are based on evidence, they should focus on business problems that need answers, and they should promote outcomes.
8 Use analogies and memes
We want the story to be memorable, engaging, and to be capable of robust simplification. Analogies and memes are a great model for doing this. Here are a couple of examples.
You do some research in the use of AI solutions to generate insights. The research shows that many companies talk about piloting ideas and running proof of concept, but very few are using it in any everyday, core processes. So, to emphasise the numbers you might share something like this:
“Artificial Intelligence in insights is like high school sex, everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
Or you are conducting customer B2B research for a components supplier. You find that the main threat to the business comes from recently hired execs in the service team who are unappreciative of the long history of support the company has offered and who are often drawn towards the supplier who can deliver it fastest. To emphasise the point made by the interviews and data, you might adapt a popular meme, such as the one below.
Conclusion
Turning data into an interesting, useful story is the first step to gaining business insight. The tips here will help you identify what’s compelling in your data in a way that helps move business decisions forward.