Are Dynamic Audience Filters the Next Step for Segmentation? Urban Outfitters and PlaceIQ Think So
Data is the lifeblood of modern marketing.
As mobile has risen to prominence over the past decade, it’s become possible for marketers to understand and process vast amounts of information about their customers, allowing for smarter targeting, more relevant and engaging messages, and stronger customer/brand relationships. That’s because mobile has made it easy for brands to observe customers’ in-app behavior, and—with customer permission—to understand where their audience members are when they’re engaging with that brand’s app.
But with mobile devices becoming ubiquitous (right now, 72% of U.S. adults have a smartphone and the number of mobile users around the world is only expected to grow) and ever-present (71% of U.S. smartphone owners sleep with or near their smartphones), the lines are increasingly blurring between mobile activity and real-life behavior. A customer who is browsing your brand’s app could be sitting at home, thinking about making a purchase for delivery, or they could be standing in the middle of one of your competitor’s stores, comparison shopping. Knowing how and where members of your brand’s audience spend their days can make the difference between effective mobile-first marketing, and efforts that miss the mark.
Thankfully, there’s a new tool that can help provide much-needed context to location and user segmentation: dynamic audience filters. (Read on to see how Urban Outfitters uses them to boost conversions and increase revenue!)
What are dynamic audience filters?
Dynamic audience filters are pre-set, automatically adjusting segmentation filters that make it possible for marketers to more accurately target customers based on their real-life movements and location information over time, among other data.
How do dynamic audience filters differ from traditional mobile location data?
When a brand uses traditional location data to personalize a campaign to its users, they’re generally concerned with two kinds of location: what country, state, or city a given user is in, or whether that user is near a particular location of interest, such as one of the brand’s brick- and-mortar stores. This kind of location data makes it possible to create campaigns based on city/state/country (for instance, sending one promotional campaign to users in the U.S. and a different version of the campaign to users in the U.K.) or to use mobile messaging to encourage customers to visit a nearby store or event.
Dynamic audience filters are different. Instead of just leveraging where a customer is right now, these filters make it possible to take their location data and pair it with nuanced information about the specific locations they visit over time and context regarding the significance of those visits. That provides a whole new level of granularity, making for smarter, more useful segmentation. With dynamic audience filters, marketers can understand each customer’s holistic journey and what their movements say about their interests and behavior, instead of just looking at a single location.
After all, knowing that an audience is in Williamsburg, Brooklyn, on a Saturday night makes it possible to reach them with messages playing off the fact that they’re in New York, or to get them into your brand’s Williamsburg outlet by offering store-specific discounts, but it doesn’t help you understand WHY that group is in the location they’re in. Knowing that a particular group regularly frequents all-night EDM clubs or goes to gyms for late-night spin classes can help you understand them in a deeper, more meaningful way.
How can dynamic audience filters benefit marketers?
In general, having a better understanding of your users helps marketers create customer messaging flows that are more relevant and valuable to the people they’re engaging with, resulting in more effective outreach.
You can see that in Urban Outfitters’ use of dynamic audience filters. In order to define its promotional messages more effectively, the brand turned to location data provider PlaceIQ, which focuses on providing marketers with nuanced, actionable full-context location information. Urban Outfitters used PlaceIQ’s dynamic audience filters in conjunction with the Appboy platform’s messaging tools to offer promotional push notifications highlighting the company’s party dresses based on customers’ real-world behavior—in this case, sending the messages to a segment of female users who had recently visited bars or nightclubs.
By creating the messages based not on customers’ stated preferences or in-app activity, but on their real-world behavior, Urban Outfitters was able to send more relevant outreach. That, in turn, had a real, tangible impact on the success of the company’s party dress promotion, driving up conversions by 75% and related revenue by 146%, compared to an identical campaign that targeted customers based on their stated preferences and in-app behavior.