A couple of months ago, I spoke at a conference in Las Vegas. Immediately before my talk, two advertising execs, one a professed quant geek and the other a “creative,” spoke about how their agencies rely less on hunches these days and more on quantitative data to drive emotional relevance between their clients and consumers. “We can identify human emotions in massive rivers of data,” the ad men said. When I pressed them for an example during the Q&A session, they described how they had recently mined millions of clickstreams, search queries, video views, website clicks, and the like for a major mortgage lender. All in, the technology investment behind their analysis must have stacked well into six figures. And their big emotional insight? When people start shopping around for a mortgage, that’s all they can focus on until they’ve gotten it all sorted out.
I could hardly believe my ears! Any skilled ethnographer could have discovered that same insight — and then some — through a day of in-home customer visits and $150 in taxi receipts.
Customer experience professionals can now glean customer insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. It’s amazing and wonderful, yes. But here’s the danger: Companies that become mesmerized by big data put themselves at risk of spending enormous amounts of time and money amassing new data sources — and in the process, forgetting that research methods like observation and one-on-one interviews even exist. This has the potential to create a large, and exceedingly expensive, blind spot.
Don’t get me wrong. I’m not a big data hater. However, to create a complete picture of who your customers are and what they really need, you need a combination of quantitative and qualitative research methods.
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