October 10, 2018
The State (and Future) of Digital Marketplaces
Not all marketing segmentation strategies are created equal. Specifically, I’m speaking about demographic segmentation and behavioral segmentation.
When marketers discuss “segmentation,” they’re more often than not referring to demographic segmentation. Demographic segmentation is a hallmark of marketing campaign strategy, honing in on characteristics such as age, gender, location, income level, and marital status to define demographic profiles of target buyers. For instance: “25-40 year old females who live in a large metropolitan area.”
Modern marketers, however, are using new technology to transition their marketing approach to segmenting by consumer behaviors, pointing to things like preferences, interests, opinions, and routines. These are psychographics, creating a basis for behavioral segmentation.
Demographics tell marketers who typically buys a particular product or service based on certain attributes, whereas psychographics reveal who is more likely motivated to buy.
Demographics show who typically buys a product, whereas psychographics show who is more likely motivated to buy.Click to tweet
So, demographics indicate “25-40 year old females who live in a large metropolitan area,” whereas psychographics show that “those who take public transportation to commute to and from work are more likely to buy a certain product or service.” There is overlap between the two groups, but not all of the latter are part of the former.
From a marketing perspective, demographics define what buyers commonly need, but behaviors point directly to preferences, defining what buyers want. As a marketer, knowing a consumer’s desires is much more powerful than just knowing their physical demographics.
For instance, if a recent college graduate earns $40,000 in an entry-level position (which puts them at a certain income level), inferring from their behaviors that they would like to earn $100,000 in five years will play a more significant role in their purchase decisions (e.g. searching for and buying online courses to boost job skill development).
Just communicating with the person based on their current situation would be a weak demographic-based approach. On the other hand, continuously marketing to their aspiration to be a senior manager would produce better marketing results, making the person a loyal customer for the duration of the 5 years and even beyond.
Restorando, a leading restaurant booking platform in Latin America, used by over 4.5 million people every year, has reaped bottom line impact by leveraging Kahuna to approach its marketing messaging using behavioral segmentation—and taking this a step further by ensuring that it’s learning and understanding consumer behaviors in real time, as they happen, and making adjustments to its messaging accordingly.
Behavioral segmentation has enabled Restorando to accurately target only those who would be interested in a certain offer or dining experience based on their previous dining habits, thereby alleviating the brand from having to guess what their customers’ dining preferences would be based on their demographics.
So, instead of just assuming that someone would like a popular restaurant in their city because others in their age/gender demographic do, Restorando is able to see whether that person actually ever booked there and/or whether that person has booked at restaurants with similar cuisines, thus showing a higher probability of them being intrigued by and reserving a table at the restaurant if Restorando were to recommend it to them.
Specifically, for its Valentine’s Day campaign last year, Restorando segmented its audience based on anyone who had booked a table for two in the past three months, narrowing in on those who would be more likely to see value in a Valentine’s Day offer.
Before Kahuna, Restorando would have sent the same promotion, but would have segmented based on a demographic like age (assuming that “25-40 year olds are more likely to have a significant other and dine out for the holiday than not”). Or even worse, Restorando would have sent the promotional message to its entire audience. Either way, Restorando would have created numerous unwanted messages among its vast user base.
With Kahuna, however, Restorando is able to segment in a way that makes sense to the campaign, thus increasing the chance that those targeted are interested in taking advantage of the offer, and the same time, refraining from over-messaging (and annoying) those who are more likely not to take advantage of the offer.
Has real-time behavioral segmentation created measurable impact? Yes. Leveraging Kahuna’s behavioral segmentation feature has resulted in an increased email open rate from 5% to 15% as well as a 42% lift in goal conversions over campaigns that do not use behavioral segmentation. Kahuna has helped Restorando hone in on its consumers’ behaviors and inferred preferences at the individual level, leading to more relevant, contextual, and personalized offers and messages. How’s that for behavioral segmentation?
But this isn’t all. Take this opportunity to dive deeper into Restorando’s case study and learn how else Kahuna helped the e-commerce site achieve stronger marketing metrics. Simply click the button below!
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