Over 300 million people listen to Spotify to get their music fix. Why? Because Spotify listens to over 300 million people.
It’s amazing, right? In a world where most marketers have to literally bribe customers to fill out a survey, Spotify customers are freely sharing with Spotify what they like and don’t like about the service. And they’re doing the same with your brand too, only you may not be listening.
The purpose of this blog isn’t to make you feel bad, or to present Spotify as some kind of marketing wunderkind. Rather, it’s to make a point that most marketers are listening to the wrong signals. Consider, for example, how you target offers to your customers. Do you target customers based on demographic data (e.g., gender, income, age), or do you target them based on the behavioral signals that are hiding in plain sight–in your customer data?
The difference between segmenting customers by demographics versus behavior is striking. To illustrate that point, look at the two playlists below. The playlist on the top is a selection of songs I haven’t heard based on the songs that I actually listen to. The playlist on the bottom is based on basic demographic data: popular music in the country where I live and what kind of music people in my age group listen to, etc.
Which playlist do you think is most likely to keep me engaged as a listener? Exactly, the one on the top.
Demographic segmentation vs. behavioral segmentation
Demographic segments and behavioral segments are after the same thing: personalized marketing. But where demographic segmentation is based on what I think I know about a certain type of customer, behavioral segmentation is based on what customers are actually interested in as demonstrated by their actions. The trouble with assumptions, other than being flat-out wrong an alarming percentage of the time, is that they tend to stay the same from year to year–but consumer behavior can change dramatically from year to year. Just look at the recent pandemic. How many people thought they would be shopping online for groceries twelve months ago or paying top dollar for toilet paper?
As much as marketers would like to achieve segments of one, it’s just not practical when you have millions of customers. What marketers really need are better segments based on real behavior rather than static stereotypes. “Married men with incomes above $100,000” isn’t a meaningful segment because it’s not telling you anything about a customer’s intent and interests. “People who like electronics and social causes” does tell me something that I can use to start a meaningful conversation with that customer and guide them along an intelligent journey with my brand.
Journeys are increasingly where marketing is headed. For example, I’ve recently begun buying pants from Bonobos. Now, there’s nothing in my DNA or demographic data that screams out “Bonobos pants.” By understanding my interests, however, Bonobos was able to take me on a journey, from introducing me to the brand, to telling me a story about the company’s ethical practices and high-quality production techniques, to eventually selling me a pair of pants, and another pair, etc. But, much like my pants, that was a journey that was tailored to me as an individual.
The future of segmentation
So, if building a segment of one is impractical, how do marketers guide customers along their own individual journey? Through personalized offers and content recommendations. With behavioral data and the right tools, marketers can automate content recommendations and product offers to keep individuals engaged and interested. At Lytics, we call this a recommendation engine. Recommendation engines can support and even replace segmentation by presenting real-time, personalized offers and experiences to your customers based on what you know about those customers’ interests and preferences. And, as Spotify has shown, if you’re tuned into what your customers really want, they’ll stay tuned into your brand.
Find out how you can personalize marketing experiences for your customers in Lytics’ guide to personalization at scale.
