This post gives an overview of essential mobile marketing KPIs that would be considered for a performance dashboard. It also outlines a general framework of how these KPIs relate to each other.
Conversion funnel is an important part of any performance dashboard. The funnel shows how the user travels through different stages and interacts with the ad and then with the product. A simple conversion funnel for a mobile marketing display campaign advertising for an app would be: Impressions-Clicks-Installs-First Interaction, with percentage share between them.
Return KPIs can be measured as monetary values: revenue per user, lifetime user value, etc. However, non-monetary KPIs (number of installs, daily active users, daily active paying users, average session length, etc.) are also important, especially in the cases where monetization is detached from the download (free apps or image campaigns).
Cost KPIs will largely depend on your payment model for the advertising. Possible ways are:
Cost per mille (per thousand impressions)
Cost per click
Cost per install (also possible: cost per lead, cost per download)
Cost per engagement
Cost per revenue or revenue share models
ROI is calculated as a percentage share of return on what has been invested in advertisement, for a product that does not monetize immediately, the ROI is calculated per time period (daily, weekly) in user cohorts.
The framework at the bottom of the picture shows how the KIPs can be adapted to achieve a significant level of preciseness and data consistency.
Timeframe includes both how the KPIs are aggregated (daily, weekly, monthly) and what time periods they cover (past, current or future/trend data).
Segmentation is essential for getting the working data. Making the segments too broad will impact the data consistency negatively and working with micro-segments is mostly too difficult to implement and does not allow to make solid conclusions based the data sample. As an example, consider segmenting the data by country or region, by traffic channel or by device type.
Sample statistics includes the parameters that analyze the quality of data and how the sample behaves in general. Instead of just aggregating and averaging the data, consider such parameters as median, min. and max. amounts and standard deviation. Besides, look at the size of the segments you work with: which of those have the strongest impact and why? Also, learn to recognize patterns in your timeframe, and how the data fluctuates on a daily, weekly or seasonal basis.
After you have decided on the KPI data you will include for your dashboard and reporting, consider making the data more visual and more structured for the intended users. In one of my next posts, I will cover the issue of data visualization in more detail.