How Smart Data Can Smash Stereotypes and Improve Customer Understanding
Every day and in nearly every retail business across the country, marketers make assumptions about who their customers are. They assume they know where their customers live, what they earn and what their gender is. They make these assumptions based on observations of who has walked in their stores, and maybe even who they wish would walk in. Management even makes decisions based on these assumptions.
There’s a word for this: stereotyping.
We aren’t talking about the odious form of stereotyping that leads to discrimination. This stereotyping is a mental categorization that puts customers in pre-conceived boxes based on their behavior. When companies rely on stereotypes, they're quite often wrong. When they make decisions based on those stereotypes, their assumptions hamper their ability to grow and learn.
Take, for example, Harley-Davidson. Most people have a similar mental picture of the company’s customers: blue collar, male, 40 to 50 years old, leather, tattoos, beards. Does that ring a bell? An analysis of Harley-Davidson's customer data showed something different. There were a lot of women and a lot of professionals. In fact, the data showed that many of the most lucrative Harley owners tend to break the biker stereotype. However, Harley’s strong brand meant its shoppers wanted to look the part. When professionals visited the store, they weren’t wearing a suit and tie.
It’s a story we hear all the time. In large companies, the disconnect between the imaginary customer and the real one can be exacerbated by marketing teams that aren’t on the sales floor. While associates may have an idea of whom the company sells to, that information might not trickle up, or be inaccurate.
Stereotypes vs. Segmentation
While stereotypes are bad, not all generalizations are. Marketers make general assumptions to devise market segments all the time. When supported by data this enables marketers to develop clear patterns and market segments, resulting in a type of generalization that can be useful.
The most basic type of segmentation — the idea that people have different needs based on where in the U.S. they live — can increase engagement by a multiple of nine or 10. Think about it: Every year, New York-based retailers send out winter offers featuring wool coats. Every year, people in Miami throw those same offers in the trash.
Simply coming up with relevant offers for customers in southern cities — like Miami — is much more appropriate and effective. However, we shouldn’t assume all consumers in Miami act the same. Geography is a segment. It can fall short, but gains power when combined with other variables. Most companies are sitting on a mountain of data from their point-of-sale (POS) and e-commerce systems that they can effectively use to target consumers.
If one simple data point like geography can increase relevance, imagine what all of the information from a POS system could do. Think about what could happen if an e-commerce database was integrated with the POS, and that information was cross-referenced with a brand’s social media operation.
Research has shown, for example, that the vast majority of consumers — nearly 80 percent — won’t engage with a marketing piece if it isn’t tailored to them. Personalized marketing leads to a win-win for consumers and retailers: shoppers get exactly what they most want, and the store sparks a relationship that can lead to true brand loyalty.
Harley, for example, used integrated data to develop multiple customer profiles, then ascribed attributes and motivations to each. The profiles were built on data that illuminated customer segments, allowing Harley to target women, weekend warriors and the traditional leather-clad biker that most people think about when they think of "hogs."
Don’t Forget That Your Customers Are Unique
As the retail landscape has changed, brands that traditionally sold through big-box channels have sought direct contact with consumers. As they’ve made that transition, however, they’ve hit a wall with regard to who their customers are. The retail stores that sell their products often aren’t willing (and many times unable) to share their customer transaction and loyalty data.
That leaves channel-based brands in the lurch when it comes to establishing customer relationships. Brand managers might know what has sold, and they may have been able to make some assumptions about product development based on sales data, but they’ve learned much more after they decided to sell directly to consumers.
E-commerce strategies are ripe for individual targeting. If a customer buys a company’s mainstay product, that firm knows not to offer the same item again. But over time the analysis of customers can help companies further segment them. For example, hobby and sports brands might be able to identify whether a customer was an amateur user of the product or a professional. Automotive brands can use data about a customer’s driving history to remind them that they're due for an oil change.
Data allows brands to target consumers on an individual basis that's relevant to their lives and their level of experience.
Millennials have shown themselves increasingly willing to share personal data that older generations typically won’t. For marketers, this presents an opportunity to go beyond segmentation and develop marketing that treats people as individuals.
The decline of these so-called privacy barriers represents one of the biggest opportunities for retailers. The only way to take advantage is by continuing to offer relevant products or services to consumers, and treating them as unique individuals.
Smart data is giving us the opportunity to get to know our customers at a level that's almost unprecedented in global business. We can do it without stereotyping them.
Ryan Rose is the vice president of business development at Clutch, a provider of advanced loyalty marketing and customer intelligence solutions.
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