I imagine consumers of the 1960s would be horrified if they knew what marketers can do to them now. To think that their thoughts can now light up a monitor screen in the form of brain activation or retailers might know their daughter’s pregnant before they do (et tu, Target?).
50 years later, we have computers, and algorithms which used to take days to run now only take seconds. While analysts of ages past were often slaves to Excel and had to build their own bridges, data analysis nowadays is easy and comes complete with nice intuitive graphic interfaces. This democratization of data has made marketers happy and market researchers somewhat weary.
Why? Because market researchers think it their prerogative to keep tabs on what’s going out to their business partners. When marketers brought home an analytics team, often conveniently embedded in their own function, market research sulked over their loss of control. When the analytics team started brandishing social media listening tools and data mining algorithms, some marketing functions started wondering if market research will go the way of dinosaurs with their surveys and focus groups. This premature tendency to ditch established methods is what one might call the Shiny Object Syndrome in analytics.
In reality however, this war of methods is much less a fight to the death than it is a simple redrawing of boundaries. Some toes may be stepped on, but no one’s head should be rolling. In fact, on a higher level, it is driven by a common urgency to better understand the consumer and the ecosystem he/she lives in. When we polled our members earlier this year, marketers say they want to strive for agility partly through better use of data and market researchers say they’re aiming for more relevance through methodology innovation. It seems reasonable to conclude that the smart market researcher would work with, instead of around or against the analytics team, using the newer methods to sharpen and inform the traditional ones. At the same time, the smart marketer would know to take advantage of what both teams have to offer to get at a more holistic picture of the customer and the marketplace.
From my particular perch – where I do data analysis and also listen to our members talk about their data strategy, there seem to be at least two ways that traditional and new methods complement each other:
- New methods excel at getting the “what;” traditional methods excel at getting the “why.” Freaky as it may be, if a company can collect, buy, link and mine my transactional, browsing and social media data then their knowledge of me is 1000 times better than if I’d sat in a focus group for 3 hours. But to act on that knowledge, one has to know why I did the things I did or even why I did them in a particular order. Why did I for example, looked at one website but bought from another? Was it because of simpler checkout? Lower price? More selection? Detailed reviews? More product views? Or a combination of the above? Understanding my deeper motivation in its granular detail would require some conversation, which you could get through a focus group conversation or through a targeted survey. To drive the point home, this is a bit like digging for gold, your new method is the metal detector, your traditional method is the shovel. You need both to succeed.
- New methods are “data led,” traditional methods and “hypothesis led.” Whether it is writing a survey or conducting focus groups, one usually goes in with a list of things to prove or disprove – be it orange packaging improves perception or factor A drives customer loyalty. Hypotheses may be modified or scratched, but the process always starts with a story, however vague at first. With newer methods like data mining, the process is flipped on its head, you go in being open-minded about all possibilities, scanning the entire terrain to see which one data seem to support, then pressure test it for a credible story. To “think outside the box,” data mining adds unique value by allowing you to start without one. Whichever way you choose, where you start is much less important than finally coming full circle to the reasonable conclusion.
How has new data sources/new methodology worked for you? Success story? Headache? Leave me a comment or email me at email@example.com.
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