10.12.2023 Keith Errington
Whether you have chosen to take a quantitative or qualitative approach, a fundamental part of B2B research is collecting data. But what do you do once you have this data? How do you interpret it to create something meaningful for your audience?
In this post we discuss how to make sense of your data and the power of finding the story within it.
Data analysis
So, you’ve run your online survey and have your data export. You have finished all your interviews and collated all your notes, transcripts, and outputs from any tasks. The next step is to analyse all this data.
Data analysis is the process of organising, structuring and attaching meaning to data. To do this effectively, the data needs to be analysed in the context of your overall research aim; what you set out to learn from the research. Your goal is to draw out insights from the data that help you get to the crux of your overarching research question.
Start by going through each of your research objectives and gathering the data that helps you to meet them. For example, if one of your objectives is ‘to explore customer social media behaviour’, pick out the data that will help you understand this; the sites they are using, how often they use them, and what they use them for. This data needs to be examined and tied together to build a clear image of their behaviour.
The importance of storytelling
While analysis helps you to organise and attach meaning to your data, what you really want to emerge from the data is a story.
Stories are powerful for engaging people. They help us to make sense of complex data, and they are memorable. Think about a movie or book you read recently. It’s more than likely that you won’t remember every scene or chapter, but you’ll be able to summarise the overall story.
In marketing, we use storytelling all the time to connect with audiences and to build a vivid picture of brands, products and services. It’s the same with research; it’s all about storytelling.
Tom Davenport, author and co-founder of the International Institute of Analytics explains the value of finding the narrative in data:
“Stories have always been effective tools to transmit human experience; those that involve data and analysis are just relatively recent versions of them. Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation – all the things that make data meaningful and analytics more relevant and interesting.”
The ability to create a compelling and memorable story with your data is key to engaging your audience and creating an impact; whether the audience is your target market or stakeholders in your business. A story will, quite simply, bring the research to life.
Finding the story
The secret to storytelling in research is to focus on the bigger picture. To look at answers to each of the questions you asked in isolation would be a mistake. Without wider context, the data will not build a story. You need to dig deep to expose patterns and relationships and weave each strand of insight together to create a narrative that will help you to understand this bigger picture.
For example, a manufacturer wanting to win new business might conduct a survey to better understand their target market. They might find that 50% currently don’t outsource, that 70% of them would consider it, and that 60% are fearful of handing over responsibility to a third party. These findings help identify the story and illustrate the big picture; in this case that there is a large potential market who aren’t already outsourcing and who are open to it, but that fear of handing over the reins is a big barrier. The detail of the story might then include insight on awareness and perceptions of outsourcing providers.
Like in any story, you should have a beginning, middle and end. The start of a story is about scene setting. Here you can give context to your research story; what are the insights that your audience needs to understand for the story to have meaning? The middle of the story is your key findings, and the end your conclusion; linking the findings back to your overall research question.
It's important to consider that your data might surprise you. Your hypotheses might be proved wrong, or the findings might not be what you want to hear. But it’s crucial that you build the story around the data; let the data lead you to the story.
Don't attempt to reverse-engineer the data to fit a story you want to tell, or a story that fits with your hypotheses. This can take your research from potentially useful, to dangerous. Your story needs to be devoid of bias, and should only make claims that your data can support. It is data that will make your story credible.
A good piece of research creates impact, and finding the story within the data is crucial to this. A story will help you make a point to your audience; to provide direction or help them understand something. Research that tells a compelling story is more likely to be listened to and remembered than simply a lifeless reporting of facts.
