Posted by Alan Nazarelli ● Thu, May 02, 2019 @ 11:02 AM

The Qualitative Edge Part 3: Innovating at the Edge of Customer Experience

In part 3 we explore mining innovation from customer feedback. This post underscores the importance of qualitative, in-depth customer input.

market analyst mining data and insights from a spreadsheet on a laptopThere are two key differences in how quantitative and qualitative data are processed and compiled for consumption, which in turn affect how they are used within organizations:

  1. In quantitative data collection and analysis the innovation resides within the data points on the spreadsheet. The data is neatly laid out in rows, columns, and worksheets and there is a comfort level with consuming and acting on the data. But if you look at the edge, you there is nothing there. 

  2. By contrast, there is a lot at the edge in qualitative data, but you have to know not only where to look but how to look. Let me explain. In our data driven world, we are conditioned to quantify. We look at data and our ingrained habit is to look for patterns - we want to classify, compile, chart!  And some of us do this with qualitative data too, often ignoring details such as sample size. We also want to get best of both worlds by doing a large as possible qualitative effort - often a waste of time and resources. Good qualitative is not contingent on sample size. Done right, it can be very time and cost effective.

So, avoid at all costs the tendency to classify and chart qualitative data. View it as a separate track from your quantitative track and be open to disruptive new breakthrough ideas that emerge even if they can't be quantified at first.

Case Study

An example from our client work from a few years ago will illustrate. A casual games company that was floundering in their crowded market space commissioned us to conduct qualitative research (focus groups and in-depth one-on-one interviews) with casual gamers to elicit ideas for innovation to enable them to break away from increasing commoditization in their space.board game pieces, chess, dominoes, checkers, cards

We completed the research and analyzed the data. In addition to the usual analysis and reporting we exercised our peripheral vision and looked for unarticulated needs and breakthroughs at the edge of our data. We found that two people - out of a total of about 60 people whose input we obtained - expressed an unusual reason for playing casual games. One was recovering from traumatic brain injury (TBI) from a recent automobile accident and the other wanted to maintain their brain agility as they aged. These were not the motivations of the other 58 or so participants. 

We presented our findings to the company's leadership team under a section titled Peripheral Vision. We presented these observations and opined that this was an area of possible breakthrough innovation. The idea was rejected because:

  1. It was based on only two respondents (i.e. it was from the edge)

  2. Both participants were older than their cohort of typical casual gamers and the leadership team surmised that the idea would not scale to their younger counterparts.

The upshot? Another company, Lumosity, launched that very concept and is now a successful business with 60 million paying users worldwide today (average MRR, or monthly recurring revenue, of approx. $10 per user per month!). And there is no telling how acting on our insight from the edge would have helped our casual gaming client break out of their commodity trap and create a new revenue stream.

Mining for Innovation
one market researcher pointing out data on a computer to another

Here are questions to consider before you design your next market research or customer insights initiative:

  1. What strategic insights are at the edge of your spreadsheet? 

  2. Does your customer feedback process enable you to trap these? 

  3. Are you customer insights processes quantitatively driven or do they facilitate customer dialogue?

  4. Do you have a mechanism for trapping your customers' unarticulated needs?

  5. Is your management open to exploring ideas that are not derived from  quantitative data?


Read Part 1 and Part 2 of The Qualitative Edge series on the Silicon Valley Research Group Blog.


Alan Nazarelli is President & CEO of Silicon Valley Research Group and 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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Topics: The Qualitative Edge, Market Research Best Practice