Recently, I had a revealing conversation with someone who is a true expert in B2C marketing. Interestingly, the story she shared with me came out of her experience as a consumer, not a marketer.
She’s a Mustang driver, a loyal customer for years. I could tell how much she loves to drive Mustangs, as her enthusiasm heated up our conversation in a cold New York City café. She often visits the carmaker’s website to check out the new Mustang models, even taking the time to configure her dream Mustang. She regularly visits the Mustang Facebook page, too, and has, of course, liked it. With all this activity, you’d think the carmaker might have picked up the hint about her interest level, right? Instead, Ford sent her campaign promotions for a mid-price SUV suitable for families. Why? Our expert in B2C marketing fell into a particular demographic segment—married mother of a certain age and income.
Why is this story so meaningful? I see it as a parable about customer relevance and marketing analytics. Our Mustang loyalist sent unmistakable signals about her preferences. Her configuration of the car at Ford’s website offered further clues about what might prompt her to buy. But the marketing team at Ford failed to see the signals clearly evident in this data and sent her an irrelevant offer.
Consumers leave behind them a trail of data “breadcrumbs” with every online click or in-store interaction, which clearly shows what they want, how they want it, and when they are most likely to buy—which is why it’s up to you, the marketer, to use those breadcrumbs to synchronize your selling cycle with the consumer’s buying cycle, delivering the relevance today’s consumers demand. Our Mustang driver is a case in point.
Customer segmentation, of course, is fundamental to achieving relevance. In fact, it has been part of the marketing “bible” for many decades. You might actually see Henry Ford’s production of the first affordable car for the middle class as an exercise in segmentation long before it became a marketing watchword. The Model-T fulfilled Ford’s vow to build a car “so low in price that no man making a good salary will be unable to own one…” (My Life and Work, Henry Ford, 1922)
Given the rapid proliferation of marketing channels and digital media, today’s marketers have even more options available to them to develop buyer personas for their products. We are in an era of micro-segmentation and micro-targeting based on the wealth of customer data generated across the complex customer journey with its broad array of touchpoints. Marketers have access to data on consumer search and engagement patterns, demographics, social and interest graphs, campaign responses, and even geo-location data. This data becomes a listening post that yields tremendous insight into buyers when combined with the power of analytics. Yet the volume and diversity of this data makes this a daunting task for most.
Demographic segmentation told marketers that our Mustang enthusiast should statistically have been interested in an SUV. But that’s still not the data that reveals what, when, and how she will buy. Behavioral data often trumps demographic data in predicting purchase patterns. What’s needed is to discover what motivates a consumer to buy, including product qualities, offers, or other factors that cause specific individuals to respond. To fully understand consumers and achieve relevance, marketers need to connect, integrate, and analyze data generated in a variety of ways—demographic and psychographic characteristics, but also behavioral, social, and geo-location information.
The data produced as buyers visit stores and browse across websites, social platforms, and mobile media, for example, make it possible to identify consumers that are more likely to buy than others. These types of purchase predictors can be further segmented by geographic location and seasonal elements to create profiles of potentially high-value consumers which can then be matched to offers relevant to them.
Achieving relevance also involves another critical element: timing. Technologists like to talk about the real-time speed of analytics platforms. And it’s true that analytics today make it possible to track marketing campaigns in real time, allowing rapid course corrections that were impossible just a few years ago. Marketers, however, also need to focus on “right time” marketing. That’s how you synchronize your selling cycles with the consumer buying cycle.
“Right time” is about understanding when a buyer is likely to make a purchase decision. Behavioral, social, and mobile data are much more time-oriented and can provide predictors to enable the marketer to send the right offers at the right time, in contrast to demographic segmentation, which provides no clues at all to timing.
It’s your choice as a marketer. Customers today have heightened knowledge of brands. Irrelevant offers are blindingly obvious to them. You need to deliver the right message to the right person at the right time in the right channels—or watch them defect to other brands. The data breadcrumb trail tells you how to achieve the relevance your buyers demand—and deserve.