Les 10 ICP que tout responsable produit se doit de connaître


These days, you’d be hard-pressed to find a product manager who doesn’t believe in the importance of collecting and analyzing product data. Being data-driven is no longer an exception or some fringe movement, it’s the rule—the new normal. And the product leaders who can get the best product data and glean the most insights about their customers are the ones who will gain a competitive advantage.

As we discovered in our survey of 200 product managers and executives, companies with the most advanced product analytics programs see annual recurring revenue (ARR) that is, on average, two times higher than those with less developed programs. For modern product teams at product-led companies, the value is clear: Better measurement informs better experiences, and better experiences produce more successful customers. But as a product leader, how do you know which data you should pay attention to? And how do you figure out your product’s key performance indicators (KPIs)?

Whether you’re an aspiring product manager or a seasoned product executive, the metrics outlined in this guide will advance your career. For emerging pros, think of these metrics as the foundation of your product analytics program. For chief product officers, think of this guide as your annual product analytics checkup. If you can tick all 10 of these boxes, you know you’ve got the core data you need to provide continuous value to your customers — and organization.

Editor’s Note: We hope you find the charts and graphs in this resource helpful. The data visualized throughout is mock, and the customer quotes, feature requests, and roadmap items are all hypothetical.

01 Product Stickiness

Do my users keep coming back?

Most companies pay close attention to their ability to add new users and close new customers. Acquisition metrics dominate executive meetings for a good reason: they’re essential for understanding growth. But it’s also important to remember that while companies may buy your product, it’s their people that use it. That’s when the buying journey transitions to the product journey. It’s the stage where your product takes on a more central and powerful role in the brand experience.

As a product leader, it’s your responsibility to build a product that not only attracts new users, but also ensures they continuously re-engage with it.

That’s product stickiness–the infinite loop of user value begetting enterprise value.

If a product is sticky, users don’t just sign up and log in periodically, they live inside the product. It becomes part of their daily professional life.

How to Measure Product Stickiness

To increase product stickiness, you first need to be able to measure it. The good news: There’s a formula for that. Just take the ratio of your daily active users (DAU) to monthly active users (MAU). The result is your “stickiness score,” the percentage of your monthly users who engage with your product daily.

A related stickiness metric you can look at is the ratio of weekly active users (WAU) to MAU. Same concept, different time frame. Generally speaking, with both ratios, the higher the percentage, the stickier the product. However, your ability to increase your score, and understand what changes impact it, is arguably more important than the absolute
score itself.

DAU/MAU Graphic

02 Product Usage

Are my users and customers engaging with the product as expected?

With any product, some features will inevitably emerge as your most popular and most used, while other features will inevitably sink to the bottom (and sometimes not the ones you’d expect). That’s why it’s so crucial to measure product usage. As a product leader, it allows you to diagnose the parts of your product that need the most attention so you can work with your team to make improvements.

For best results, you need to build a plan for each of your features, paying special attention to those with the lowest engagement. In some cases, you may discover that adoption could potentially take off with a little more promotion and awareness. In other cases, a redesign might be in order. And still in other cases, the best course of action may be to sunset a feature altogether.

Ultimately, whatever changes you decide to make (or not make) to your features, it all starts with understanding usage. You need data to steer you in the direction of where the product experience is delivering—and where it’s falling short.

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