Reflection is key to wisdom; knowledge is important but only the first step. The second step is understanding what you learned and applying it. Without the two, you can’t gain wisdom. If you aren’t learning from what you know, then you’re being inefficient. And assuming you read my previous blog, you know how I feel about inefficiency.
As a researcher, one of my favourite exercises is a meta-analysis. I love to see how individual questions and studies can be pasted together to answer new questions. Once you have built an insight repository and have your entire organization humming and entering all their data into it, what do you do next? How do you ensure you are getting the most value out of having that system? One of the most valuable actions I have seen our clients take is using the tool for their meta-analysis.
I have seen our clients use our software in many interesting ways. Here are some of the most powerful things I have seen accomplished as part of meta-analysis projects:
Insight and Benefit Library: using the library function, researchers are given access to quickly scan and filter insights and benefits that performed well. Then, they can analyze common factors that may be contributing to this, which ultimately aids them. Imagine doing a giant optimizer test with all the concepts you have ever tested all at once. When researchers understand what works and what doesn’t for certain categories, they can write more successful concepts in the future.
Need Gap Analysis: searching across all consumer categories, a single researcher can quickly pull all the need gap studies that have been conducted in the last year. Even with slightly different suppliers and methodologies, the team could dig into common need gaps across categories and create messaging that could be reapplied throughout multiple brands. This saved in non-working media costs and allowed mega brands to be more impactful.
Brand Equity Analysis: by searching all the research around brand equity (similar to the need gap analysis), researchers were able to understand as a company where their brands were struggling the most. By understanding the problem areas, they could focus their messaging and advertising to lift all brands simultaneously. This is especially useful in Pharma where brand equity is measured in non-standard ways and benchmarking can be difficult.
Of course, big data can be daunting, especially when it is unstructured market research data. With the right questions and just a few days of focused meta-analysis, you will be surprised to find what your team already knows.
Any other tips on gaining wisdom from your data? We’d love to swap ideas.
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