Many marketers already have the tools they need to realize the benefits of personalization, but most insist on using the same tired techniques when examining “average” consumer behavior. The myth persists that consumers within the same demographic will behave in identical ways.
For example, the “average 32-year-old female” evokes a snapshot image of a married mother of two in a middle-income household of four.
Picture this: Anna is 32. She is single, earns $90,000 a year, and owns a condominium in a trendy urban neighborhood.
Her grocery trips tend to be frequent and quick, and Anna spends little time meandering through the aisles. Instead, she moves deliberately, straight from fresh produce, to cheese and wine, to frozen meals, stopping for a few items in the personal-care section as she checks off her list.
Now another picture: Julia is 32. She and her husband are both employed and pool an annual income of about $150,000. Their schedules are packed with extracurricular activities for their four children. Julia grocery shops just a few times a month and makes an occasion of it. She scours aisles for new convenient but healthy products, browses the magazine aisle, and, as a treat, grabs a latte at the Starbucks kiosk.
In reality, the commonly held view of a 32-year-old female can be wildly different from how actual people in that demographic engage with a brand. Not only do Anna and Julia’s lifestyles diverge from that of the “average” 32-year-old woman, but their behavior is much different.
Consumer research often reveals a frustrating disconnect between perception and reality — what consumers claim to want and what they actually purchase.
For example, while some indicate that email is their preferred way to receive offers and coupons, many shoppers actually demonstrate a higher use of direct mail. Similarly, while some consumers tell us that they never purchase chocolate, their in-store transactions tell a different story.
Behavioral data is a powerful tool for deciphering that disconnect and more intimately understanding the consumer—what they actually want vs. what they say they want.
How will you use behavioral data to segment and target your customers?