Product Analytics can help you acquire more users, keep them longer, and increase revenue.

This guide shows you how.

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Averages make very poor benchmarks.

I get it. You want to know what typical acquisition, activation, and referral rates look like. You want this information as a signpost to evaluate your own product’s performance.

But there are two huge problems with comparing your product to the average:

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The team over at Mixpanel wrote a wonderful article on measuring user adoption.

In the article, Mixpanel defines adoption as the process by which new users become acclimated to a product or service and decide to keep using it.

They suggest that user adoption conforms to this formula:

Adoption = Value / Effort

I think this is a solid start. If users see value in your product, they’re likely to use it as long as the effort of doing so isn’t too high. Too much time, too high a price, or too much confusion will sour the experience and decrease adoption.

But I’d like to suggest a few more nuanced measurements that build on this idea.

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If your product’s reporting doesn’t give users actionable data, it might as well not be there.

“What will they do with this information?” is probably the only relevant question when designing a new report.

If the answer starts and stops with read the report, you’re doing it wrong.

Users don’t want data. They want insights. Users don’t need reports. They need answers.

Take a look at your product’s reporting and ask yourself if you could actually improve your performance based solely on the metrics it provides, a testable hypothesis, and some elbow grease.

If not, it’s time to redesign the report.

It’s far easier to lose weight by eating better than by exercising.

Even if you run five miles a day, you’re going to get fat if you subsist solely on cheeseburgers, candy, and soda. Bad inputs undermine hard work.

The same is true when designing products.

Good designers look at popular designs as sources of inspiration for their designs. To a designer, the entire web is one big pattern library.

Indeed, most “formal” pattern libraries include examples from popular sites. What they rarely include, however, is any data analyzing those patterns. Some patterns actively help users, but some are barely usable. This is probably because designers have been confusing pattern libraries with style guides and design systems for at least as long as they’ve existed.

We need more data-driven pattern libraries. The folks at GoodUI have made some great strides in this direction. The web needs more projects like that.