An Engineer’s Guide to App Metrics


Building and shipping a successful product takes more than raw engineering. I have been posting a bit about using Telemetry to learn about how people interact with your application so you can optimize use cases. There are other types of data you should consider too. Being aware of these metrics can help provide a better focus for your work and, hopefully, have a bigger impact on the success of your product.

Active Users

This includes daily active users (DAUs) and monthly active users (MAUs). How many people are actively using the product within a time-span? At Mozilla, we’ve been using these for a long time. From what I’ve read, these metrics seem less important when compared to some of the other metrics, but they do provide a somewhat easy to measure indicator of activity.

These metrics don’t give a good indication of how much people use the product though. I have seen a variation metric called DAU/MAU (daily divided by monthly) and gives something like retention or engagement. DAU/MAU rates of 50% are seen as very good.


This metric focuses on how much people really use the product, typically tracking the duration of session length or time spent using the application. The amount of time people spend in the product is an indication of stickiness. Engagement can also help increase retention. Mozilla collects data on session length now, but we need to start associating metrics like this with some of our experiments to see if certain features improve stickiness and keep people using the application.

We look for differences across various facets like locales and releases, and hopefully soon, across A/B experiments.

Retention / Churn

Based on what I’ve seen, this is the most important category of metrics. There are variations in how these metrics can be defined, but they cover the same goal: Keep users coming back to use your product. Again, looking across facets, like locales, can provide deeper insight.

Rolling Retention: % of new users return in the next day, week, month
Fixed Retention: % of this week’s new users still engaged with the product over successive weeks.
Churn: % of users who leave divided by the number of total users

Most analysis tools, like iTunes Connect and Google Analytics, use Fixed Retention. Mozilla uses Fixed Retention with our internal tools.

I found some nominal guidance (grain of salt required):
1-week churn: 80% bad, 40% good, 20% phenomenal
1-week retention: 25% baseline, 45% good, 65% great

Cost per Install (CPI)

I have also seen this called Customer Acquisition Cost (CAC), but it’s basically the cost (mostly marketing or pay-to-play pre-installs) of getting a person to install a product. I have seen this in two forms: blended – where ‘installs’ are both organic and from campaigns, and paid – where ‘installs’ are only those that come from campaigns. It seems like paid CPI is the better metric.

Lower CPI is better and Mozilla has been using Adjust with various ad networks and marketing campaigns to figure out the right channel and the right messaging to get Firefox the most installs for the lowest cost.

Lifetime Value (LTV)

I’ve seen this defined as the total value of a customer over the life of that customer’s relationship with the company. It helps determine the long-term value of the customer and can help provide a target for reasonable CPI. It’s weird thinking of “customers” and “value” when talking about people who use Firefox, but we do spend money developing and marketing Firefox. We also get revenue, maybe indirectly, from those people.

LTV works hand-in-hand with churn, since the length of the relationship is inversely proportional to the churn. The longer we keep a person using Firefox, the higher the LTV. If CPI is higher than LTV, we are losing money on user acquisition efforts.

Total Addressable Market (TAM)

We use this metric to describe the size of a potential opportunity. Obviously, the bigger the TAM, the better. For example, we feel the TAM (People with kids that use Android tablets) for Family Friendly Browsing is large enough to justify doing the work to ship the feature.

Net Promoter Score (NPS)

We have seen this come up in some surveys and user research. It’s suppose to show how satisfied your customers are with your product. This metric has it’s detractors though. Many people consider it a poor value, but it’s still used quiet a lot.

NPS can be as low as -100 (everybody is a detractor) or as high as +100 (everybody is a promoter). An NPS that is positive (higher than zero) is felt to be good, and an NPS of +50 is excellent.

Go Forth!

If you don’t track any of these metrics for your applications, you should. There are a lot of off-the-shelf tools to help get you started. Level-up your engineering game and make a bigger impact on the success of your application at the same time.

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