Most insights executives have experienced frustration at some time or another about re-using respondent data from past research studies. The problem goes something like this – a primary research project is conducted with a representative sample of respondents, and key results from the data are visualized in the presentation deck.
Time passes and the underlying SPSS and Excel data is lost or forgotten, so it seems like the only results the research delivered were the big picture visualizations that made it to the final report. Weeks or months later, when a marketer or researcher has a question that might have been covered in the data, the insights team has to go back to the agency that did the original research to request and pay for another round of analysis. (Even worse if they have to commission new research).
At Market Logic, clients are using big data search technology to turn this situation around, with functions that make it possible to slice and dice underlying SPSS data from past research projects. Say you want to find out “what do we know about millennials and recycling?” When you search your insights engine, you still get all the qualitative answers to your question as usual, but you also get a list of any survey questions that mention your search terms. Select a survey question to see all the variables that were applied – gender, age group, attitudes toward other issues, and so on. Now slice and dice the research by simply selecting variables of interest.
Finally, if you want to present sub-sections of the results, like “women aged 25 – 30 years’ attitudes toward recycling”, you can recut the data with a filter and fresh visualization. Done in an instant, with no need to go back to the agency and pay twice, and without having to resort to complex analytics tools.
Of course, this begs the question, how much effort is involved to prepare your data for search and re-use? The good news is that onboarding research data re-use is not rocket science. Researchers or agency staff simply upload data files alongside all the other documents they captured in a research project, so there’s no need for expensive support services.
To help the search engine make sense of the data, uploaders also provide a few details about the data set and its variables, such as table description, column labels, and data types. For SPSS files, most details are automatically extracted and presented for review. In addition, data onboarding templates speed up the process when registering similar data sets again and again, in much the same way as you create a template for a regular online banking payment.
As the saying goes: unless you bring data with you, you’re just another person with an opinion. So why not re-use past research data to inform your decisions with relevant facts and figures?