Category posts
Product analytics: tools and hints

Most of us have learned to think about products in terms of user churn at specific steps in the funnel. We keep asking ourselves “Why do users leave?” and then we try to find and fix the reasons for this. We assume that solving those issues and removing friction will improve the key product metrics.

Funnel optimization surely is a good approach to improve key product metrics. However, it doesn’t work in all situations. And when it does work, it usually only brings incremental improvements, not fundamental changes.

Today, we’re going to talk about a different approach when examining your product. This approach can boost your product, and sometimes it can take your product to a completely new direction.

I suggest posing the question “Why do users stay?” before “Why do users leave?”

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Product/market fit is an important concept when working on a new product. All entrepreneurs and product managers are committed to it. But if you ask what the term means, very few will be able to give a clear answer. Even fewer will have an understanding of how we can measure product/market fit using metrics.

Without a clear definition, even the most useful concepts will be of little help when making decisions. In this post, we will discuss some of the most common product/market fit definitions and their advantages and disadvantages, as well as tell you about PMFsurvey.com (Product / Market fit survey by Sean Ellis) developed in collaboration with GoPractice, which is designed to give you an objective metric of how close are you to Product / Market fit.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

It is common to evaluate your product performance and the impact of changes you make by using engagement metrics with active audience in the denominator. Examples of these metrics include the time spent per active user, the occurrence of certain actions (messages sent, levels played, chapters read, etc) per active user, or ratio metrics (what percent of active users perform a specific action) .

In most situations, these engagement metrics will be helpful. But in some cases, they can be misleading. And it is important to understand why and when this can happen, and what you can do about it.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Forecasting the dynamics of revenue, audience, and other key metrics is an important process for any product that is in its growth phase. Having a good forecast helps to prioritize projects at the planning stage, and then helps to keep track of how quickly you are growing against the forecast, allowing you to spot problems as early as possible.

The very process of creating a forecasting model allows you to synchronize the team in terms of understanding the product’s growth model. It also provides a tool for assessing the impact of working on different areas of the model.

Today we will talk about building audience and revenue forecasts for your product using cohort analysis. We will also find out the pitfalls and difficulties of this process.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Cohort analysis is a highly effective product and marketing analytics tool. Unfortunately, few people know about it, and those who do rarely use it.

This essay will discuss the following:

• What is the essence of cohort analysis?
• What is the difference between growth metrics and product metrics?
• Why do attempts to build product analytics based on growth metrics fail?
• How to use cohort analysis in marketing and product analytics
• Which product metrics should you monitor and why?

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Product managers or analysts will often say something like: “My analysis of the data shows that users who do action X are more likely to buy the premium version or become successful.” Based on this insight, they decide to invest time and effort into making more users do X.

But this is a classic example of assuming causation where there is only correlation. Maybe one thing really does cause the other—or maybe it’s just another case of two metrics happening to grow at the same time.

We have all heard the time-old caution: “Correlation does not imply causation.” It sounds obvious, almost too obvious. Yet time and again, even very experienced people, whether by accident or design, treat them as the same thing.

Here we will look at why it’s so easy to gloss over the difference between correlation and causation, how to prove causality, and why it’s important to keep all this in mind when working on a product.

User habits can have a big impact on a product’s long-term success. We will kick off our discussion of habits with how people develop them and the ways in which popular products nudge users to keep coming back.

Regular use of a product can become a habit. In the previous article we zoomed in on dopamine’s role in this process. This time we will look at a way of thinking about habit formation called the Hook Model, proposed by Nir Eyal.

We began this series with how product habits get made and why the neurotransmitter dopamine is so important for this process. Then we looked at the Hook Model, which Nir Eyal created to explain habit formation. Here in our concluding article, we will look at which products by definition cannot be habit-forming, what user habits can be confused with, and alternative ways to encourage users to interact with a product more often.

For a thoughtful approach to product growth and development, it’s critical to know the tasks and contexts that create the jobs-to-be-done (JTBDs) handled by your product.