You think ads in mobile apps are annoying, don’t you?
I believe that the popular backlash against digital advertising stems from poor targeting practices. Bad targeting usually means bad advertising; good targeting means matching people with messages that are actually relevant to them. In fact, Adblock Plus found in a survey that 75% of its users were willing to see responsibly-targeted, unobtrusive ads.
That’s why it’s high time mobile apps take advantage of data to make their ads more relevant.
Most app developers are gathering mountains of data about users’ interests that could be combined with “interest graph” data from Facebook, Twitter and other interlinked social networks to target ads based on what a user actually likes. By doing this, app developers could dramatically boost engagement with their ads and charge a premium for their audiences… not to mention make mobile ads a little less bothersome.
To demonstrate this concept in action, I’ve looked at four popular apps that are not already targeting based on interest data, with a few ideas on how they can get started. Some may disagree with my suggestions for changing their most beloved apps, but I believe that ad targeting done well will enhance user experience, not detract from it.
Pandora users have become accustomed to irrelevant ads; as one reviewer said, “the app was great, but the ads were ‘completely useless’.” Most of Pandora’s in-app advertising revolves around intermittent audio ads based on a user’s location, but few ads take into account a user’s tastes or preferences. Obviously, just because two users live the same town doesn’t mean they’re interested in the same thing… so why doesn’t Pandora move away from geo-targeting to much more nuanced interest graph targeting?
For Pandora, this means analyzing first-party data (playlist seeds and music ratings) and combining it with third-party data (other interest graph data, such as what a user likes and who they follow). By integrating a user’s thumbs-up and thumbs-down ratings with interests identified from her Facebook and Twitter data, Pandora could easily present ads for products and services she would find relevant.
Many ads on Flixster promote current movies playing at nearby cinemas, or special offers from advertisers such as LivingSocial. Users get interstitial screen takeovers based on location, and lots of ads for Flixster features and content. Other ads seem completely untargeted, such as LivingSocial pop-ups and Rotten Tomatoes banners.
Flixster has a ton of valuable data about its users (location, types of movies they like, movies they’ve seen and rated etc.) that they could be using to target ads – and they aren’t even doing this simple type of targeting. The app could also go above and beyond targeting based on its own data to include third-party interest graph data from Facebook and Twitter. Combining Flixster’s own interest data with information like which celebrities its users follow on major social platforms could help advertisers reach people who might be interested in TV premieres featuring their favorite movie stars.
Yelp is one of the most popular local apps around today. Millions of users launch Yelp every day to look up reviews for restaurants, stores, attractions and more. And guess what? Yelp doesn’t serve mobile ads at all! To be fair, Yelp’s monetization strategy focuses mostly on getting local businesses to pony up for “enhanced” listings, but the world’s most popular local reviews app could be doing so much more to monetize its mobile traffic. On the Yelp website, users see relevant promoted events and businesses on the right side of the screen, as well as some targeted advertising – but on the mobile app, nothing.
The thing is, Yelp knows what type of restaurants, shops, events, and attractions you frequent, as well as your location, places you’ve reviewed and more. For its mobile app, Yelp could combine a user’s location, searches, and review history to deliver highly relevant ads. What’s more, Yelp could integrate third-party interest graph data to identify users as “moms” or “sports fans” to create even more relevant ad experiences.
OkCupid is another mobile app that serves no ads. This is a company that uses sophisticated data mining to make sophisticated dating matches. It asks users dozens of questions to create their unique profiles, and has data on age, interests, location, preferences for a date, opinions, chats, messages etc. Yet OKCupid does not use this data to serve targeted ads on mobile. I’m sure the daters on OkCupid would be an outstanding audience for ads for relevant local restaurants, bars, comedy clubs, theatres, and other outings, not to mention ads for clothes, makeup, accessories, and other must-have items for actively-dating singles.
OkCupid could start by mining the rich dataset it has on all the singles using its service to create targeted ads based on location, interests and age. It could also combine this information with third-party interest graph data to attract a wider range of advertisers, beyond those that fit the “dater” profile. For example, they could help auto brands find in-market auto shoppers, or help airlines find frequent travelers.
These advertisers could even gear their creative towards this audience to make the ads relevant to the app experience, with ad copy such as “Don’t take her out in your old clunker. Check out the new Jeep!”