The mobile app marketing industry has matured immensely over the past years. As discussed in part 1 and part 2 of this blog series, the industry has shifted focus from ad clicks to installs and post-install events. Now, many app marketers have also been testing new strategies such as cross-promotion and re-engagement. However, as this industry evolves, so does advertising technology. This evolution enables app marketers to leverage advanced technology for new strategies.
Lookalike Audience Targeting
App marketers no longer have to blindly target audiences to acquire users. Lookalike audience targeting is a new strategy that enables app marketers to expand their user base by discovering new users who have similar traits and behaviors as their current high lifetime value (LTV) users.
Lookalike audience model is built by leveraging an app’s user data, such as who are active users and spenders. These data allows app marketers to create different user segments for targeting. For example, active users can be put into active and very active user segments while spenders can be put into low, average, and high spender segments. If the app marketer wants to target only very active users and high spenders, the created segments can be utilized to find audiences who share similar traits and conduct similar behaviors as the selected segments.
The search for lookalike audiences can be accomplished by leveraging a programmatic platform partner’s bidding algorithms and database. For example, once Aarki receives user segments from a client, Aarki utilizes an algorithm to find similar users in a rich database and expand the list of device IDs in each segment. This database enables Aarki to find out more about each device IDs, including the operating system (OS), designated market area (DMA), installed apps, time of app activities, and more. After querying the database, Aarki scores each device ID by how similar they are to each user segment a client defined. Low score means the user is not very similar to a selected user segment and high score means the user is very similar to the selected user segment. Aarki’s bidding algorithms then target users who have high score in real-time bidding (RTB) situations.
This strategy allows app marketers to expand user base with high LTV users and drive stronger return on ad spend (ROAS). It is very cost efficient because it does not target users with low predicted probability of bringing high return to an app, whether it is being an active user or spending money in the app.
Another strategy for expanding your user base with high LTV users is utilizing bespoke segments. This strategy enables app marketers to expand their user base by discovering loyal, high LTV users of apps that are similar to theirs. This strategy is derived from the theory that if a high LTV user is loyal to one app in a certain category, there is a high probability that they will also be loyal to another app in the same category.
Since many users utilize more than one app in each app category, app marketers can observe their high LTV users and identify which other apps in their category those users are also loyal to. App marketers can define what are the criteria of a high LTV user. criteria will differ for each app. A game app’s criteria may be users who have played a game for 30 days and spent $100 so far while a travel app’s criteria may be users who have registered and booked one hotel. Utilizing those criteria, programmatic platform partners such as Aarki can leverage first-party bid stream data to find high LTV users in the same app category.
For example, if a social casino app “Vegas Slots” wants to expand its user base, Aarki can utilize its bid stream data to find loyal users of other apps in the social casino category such as dice game, card game, and other slot game apps. Aarki’s machine learning algorithms can then forecast the predicted probability of each user’s loyalty to “Vegas Slots.” Utilizing these insights and client’s criteria of a high LTV user, Aarki’s bidding algorithms target users with high probability to be loyal to “Vegas Slots.”
Due to the knowledge of each user’s app behaviors and preferences, this strategy increases app marketers’ chances of acquiring loyal, high LTV users who would drive the campaign ROAS and overall app return on investment (ROI). Similar to lookalike audience targeting, this strategy can also be cost effective due to the fact that app marketers will only be targeting users with high return. As a result, app marketers will not be wasting their budget on the wrong media inventory.
As more users utilize mobile devices frequently on a daily basis, app marketers are challenged with heavy competition and high churn. However, app marketers can tap into their data and leverage partner’s capabilities to employ lookalike audience targeting or bespoke segment to combat these new challenges. By optimizing for loyal, high LTV users, app marketers can ensure the success of their apps and drive stronger ROI.