Insight #017:Are You Making Assumptions About Your Customers Based On Demographics?

It’s an old trick in the book – segment data based on demographics to target newer customers.

But Customer Success Manager Kerri Linderboom cautions brands from falling too far down this “easy route” of segmentation if they want to see lasting loyalty conversions.

In her work for TASK Groups Plexure division, Kerri specializes in using the complex digital environment to manage customer lifecycles. In simple terms, she’s our go-to for understanding customers through data and bringing them into a brand’s ecosystem.?

Kerri says that one of the most common mistakes she sees is how brands typically approach segmentation. “People often come into using data and then exclusively look at customer demographics – age, gender, location, etc.,” she explains. “They’ll start to categorize customers into personas which leads to generalizations. Based just on the surface level information, they’ll start to group people as busy young mom or budgeting student.”??

While there is a place for personas in marketing, the problem with this strategy is that it leads to gross assumptions about what customers want, and how they should behave.

Here’s an example…

Imagine a 60-year-old grandmother who really embraces technology. She keeps up with digital trends, loves to shop online, and prefers self-checkout to her 40-year-old son. If we relied on existing demographic models to capture her into a brand, there’s no way we’d be targeting her loyalty effectively; we would have assumed based on her age that she wouldn’t be interested in a digital campaign. Meanwhile, we’re putting all this effort into her son to engage, while he can’t even remember the password to his own email address.

“Demographics offer us great reporting, but they don’t actually uncover behavioral trends – and that’s what will actually help you personalize to customers.”

Instead of relying solely on personas, segmentation should include a magnitude of different facets. The prerogative should be to build a profile based on how the customer makes decisions and what drives them to engage. It’s less about who a customer is and more about how a customer behaves. By segmenting based on behavior versus demographics, you’ll still end up with manageable personalization sets that will spur better engagement results.

“Brands have a tendency to think that they need to know all aspects of their customers to interact with them at all. In reality, this never works. It takes time to understand your customers deeply; you need to gather data points over time to start accurately piecing together their behavioral trends.”

Where demographics can be useful

“Demographic intel helps us picture and relate to customers, but it doesn’t necessarily help us target them. You can use demographics – but tread carefully.”

Kerri says to think about it as a value exchange. Customers will be happy to provide you with their data and their preferences because they expect brands to give them better experiences in return. "If a customer indicates they are a vegetarian, be smart enough not to serve them beef content.”

Collecting data will help you predict and determine the preferences that will have an impact on your brand’s interactions with consumers. However, there is only so much personal information customers are willing to share, especially when you are failing to leverage the data you already have.

“This is where trends become important. Is this customer buying a large amount of food, or do they go for single meal orders? Do they go out on Wednesdays? What time? Those purchasing behaviors will help you unlock those more meaningful data points.”?

Kerri notes that behavioral data segmentation can take a while – often a year after a customer enters your brand’s ecosystem. Having patience – and letting data drive the process – is crucial.

When it comes to data segmentation, avoid the temptation to rely too much on demographics. The beauty of a data-driven approach to customer targeting is that it eliminates the human fallacy of making stereotypical assumptions.

Behavioral data places emphasis on what customers actually want – beyond just the profile they represent. Ultimately, that will help brands deliver better messaging and value to customers, resulting in higher loyalty rates long term.