One concerning trend we have found with market research vendors and end-clients recently is the tendency to “quantify” qualitative data by creating spreadsheets, tables and graphs.
Clients justify this as an effort to get make the data more consumable and impactful to their internal teams. Research vendors, in turn, claim to be giving their clients what they want. Market research vendors also tout new “innovations” that combine both qual and quant data collection techniques, invariably sacrificing some qual depth and some quant sampling error to create a hybrid solution that is best of none.
There is a better way!
We have learned to live with dichotomies and paradoxes in many other areas of life. Physicists embracing both the wave and particle theories of light is one example. Can we then not live with the paradox of qual and quant data in our customer insights world?
Here are some guidance for companies dealing with this quandary:
Use quantitative techniques to generate, collect and analyze large data sets with small margins of error, then layer that with a small qualitative phase to verify the findings and for open ended exploration to go beyond the comfort zone of the quantitative data in your spreadsheets and charts.
Deep learning or hierarchical learning is an offshoot of machine learning used to mine big data for customer insights. The same approach can be used for qualitative group or individual interviews. Since quantitative data collection has become easier and more cost effective to obtain in recent years, your qualitative agenda or discussion guide can skip a lot of questions (by moving these into the quant phase) and dive deeper into customer needs and motivations. We discussed this technique in an earlier post titled “The 'UnFocus' Focus Group.”
As mentioned above, one reason why companies quantify qual data is to make it more consumable for internal stakeholders who are increasingly becoming more quantitatively driven in an era of big data and more skeptical of data that isn’t quantified. There is an alternative as practiced by Intel who ranked number 5 on last year’s Management Top 250. According to Intel’s Chief Strategy Officer, Aicha Evans, Intel engages technical personnel in early client meetings along with their sales reps to provide the former with first hand exposure to customer challenges and unarticulated needs. Providing live and in-person exposure versus merely sharing qualitative data reports goes a long way towards creating a culture that appreciates the value of qualitative insights.
In-home and office on-site visits are an invaluable tool for companies to further their learning about customer behaviors and unmet needs and for verifying product designers’ assumptions about how customers use their products. As P&G Gillette brand mangers found by watching men shave at home, the average shave involves 100 strokes versus the 30 reported in focus groups!
No connection to the 90’s Meryl Street movie, but marketers and market researchers frequently draw artificial boundaries on the topics of investigation for qualitative research. We are investigating how you use email at work versus how you communicate with others; how you use your microwave to heat food versus how do you solve the problem of feeding yourself and/or your family when crunched for time. At the edge of your question lie unexplored and potentially unexploited opportunities for extensibility of your core product!
Alan Nazarelli is President & CEO of Silicon Valley Research Group, an ardent evangelist for the value of qualitative research in an era of big data and machine learning. To connect with him or arrange for a complimentary briefing please press the Connect button.
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