Three Key Metrics to Measure User Intent On Mobile Cost Per Install Campaigns

You work for an agency that is on the cutting edge of mobile advertising, and you are consistently on top of all of the latest trends in mobile marketing. You know affiliate tracking systems like Kochava, Mobile App Tracking, and AdX like the back of your hand,  and could probably run Facebook mobile newsfeed ads in your sleep.

Client-wise, you tend to work with free-to-play mobile games that understand how to optimize ad spend for sustained and consistent value. You aim to find traffic sources that continuously yield high LTVs for your clients, and in the process, brand you as a media-buying innovator.

If this sounds like you, then you are most likely gauging user intent by calculating user lifetime value and measuring viral pull. Below are three additional tactics you can leverage to optimize cost-per-install campaigns, and identify those campaigns that drive users with high levels of intent.

1. Session Optimization: Sessions are one of the most straightforward indicators of user intent on the direct-response ad side, and thus an essential variable to optimize around. At first, your traffic sources will probably display discrepancies in session quantity and session time for new users, so it is wise to wait until you have reached at least 1,000 impressions before drawing conclusions from your data.



Facebook ads are particularly useful in this regard, since you can trace installs back to the specific ads that drive them. This allows you to optimize around session data at an ad level, and will provide you with an extra layer of insight to utilize when evaluating your campaigns.

2.  Event Optimization: A/B testing your traffic sources around specific events that take place within an app is a highly effective way to identify outliers.

A good starting metric to optimize around is the number of times an app is opened, which you can track through analytics systems such as Flurry and or Mobile App Tracking. It is a good practice to optimize both ad creative and traffic source around frequency of app openings.

App openings are just the first step -- during a recent dating app install campaign we were a part of, we optimized around the action of users adding photos to their profiles. There was a major discrepancy at the traffic source level in terms of the cohorts that chose to add photos and those that did not. Further examination allowed us identify the individual traffic source and specific ad that was generating the largest number of added photos per user signup. We then optimized our campaigns around this engagement variable.

3. User Attribution and Virality: An essential byproduct of a positive user experience within a mobile app is the sharing and social engagement it initiates after a user's session ends. In essence, this figure is your K Factor, or the number of additional users you will gain for each new user. Each traffic source's unique K Factor can be determined by pulling data on the amount of shares per session per user that it generates. These numbers will provide valuable insight into the viral reach of the specific audience cohort you are looking to build, and allow you to focus your resources on channels that will provide long-term viral growth.

The three strategies above are simple but effective ways to differentiate traffic sources by level of user intent. Implementing them will put you in a better position to ensure the success of your current and future mobile campaigns.
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