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In the previous essay, we discussed why it is important for product managers to work on improving the efficiency of their solution to the target problem, and as a result, focus on the metrics of their product’s added value in comparison to alternative solutions.

In this essay, we will use a specific example to demonstrate why product metrics depend on the added value of the product. In particular, we will study the levers of influence on the Retention metric.

→ 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.

In the simulator we introduce two types of metrics: growth metrics and product metrics. These categories help students understand which metrics should be used in which situations.

• Product metrics answer questions about the product itself. They help you to understand how the product converts new users into active users, paying users, profit, orders, support requests, etc.
• Growth metrics answer questions about the business built around the product. These metrics include revenue, number of active users, number of orders or calls to support.

Over the past few years, I have noticed another type of metric: efficiency metrics or added value metrics.

In this essay, we will discuss what these metrics are, why they are needed, and why the product team should focus on them.

→ 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.

Some believe that a product manager’s job is to formulate and prioritize hypotheses, and then turn them into knowledge through A/B tests and research.

Others think that a product manager’s role is to be a user advocate, make features, and improve product metrics.

And then there are those who see the product manager as the person who manages the roadmap, motivates the team, improves the unit economics, optimizes key funnel conversions, and is responsible for the product’s revenue.

In reality, depending on the team, product managers do some or all of the above.

But these are only tools that should help achieve the most important goal, a goal that product managers often forget.

→ 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.

Data is at the heart of product management: from forming and validating hypotheses to designing and running experiments to measuring the impact of product changes and understanding the market dynamics.

To find out more about the different aspects of data skills in product management, we spoke to experienced product managers from different companies and industries. Their comments will give you a good understanding of the following:

• What data skills are important for product managers and why?
• How do companies evaluate the data skills of people they want to hire for a PM position?
• How can you improve your data skills?

→ 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.

Teams that don’t use experiments usually think they know their product, its users, and what they should do to achieve their intended results. In contrast, teams that use experiments acknowledge that they know very little about their product and users. This way of thinking presents the team with a unique opportunity to improve. Our alumni Anton Rifco dived deeper into the topic to tell our readers more 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.

GoPractice opens a series on how to create products that people need – on the basics of product management.

From this article, you will learn how product work differs through the prism of “problems and pains” from work through the prism of “increasing the effectiveness of the solution.”

ARPU and LTV are often used interchangeably. But in reality, they are two fundamentally different metrics. They are calculated in different ways. They answer different questions.

This article will discuss the following:

• How to calculate ARPU
• How ARPU differs from LTV
• How increasing ARPU can harm your business
• How decreasing ARPU can benefit your business
• Why you need to be careful about using ARPU to make product decisions

→ 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.

In this blog post, we will look at professionals who switched from marketing to product management and what they learned on this journey.

Our partner Sean Ellis made a poll on LinkedIn to find out more about people’s experience in switching to product management. From this survey, we learned that most people came to product management from marketing, which turned out to be a great starting point.

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.

In collaboration with GoPractice, Letyshops CMO Zakhar Stashevsky continues to discuss product growth through effective advertising channel management.

Table of contents for this series of essays

1. Errors in calculating ROI and unit economics. Impact of attribution models and incrementality on the ROI calculation of marketing channels. In this column, we discuss why, when calculating the unit economics, it is impossible to ignore the influence of the used attribution models and advertising incrementality.
2. Traffic attribution models. Why attribution models should change along with growth channels, product, business challenge and external environment [you are here]

In this piece, we discuss how to select attribution models to assess the effectiveness of advertising channels based on the specifics of the product, marketing mix, business objective, and environmental conditions. We will also explain why it is necessary to revise and adapt the attribution model in the event of changes in these factors.

From here on, Zakhar tells the story.

In the previous essay, we discussed how the incorrect calculation of the unit economics and ROI can lead to an underestimation or overestimation of the advertising channel and, as a result, erroneous marketing decisions. Excessive scaling or channel shutdown can lead to direct or indirect financial loss, which leads to missed growth opportunities on the market.

Such errors can happen for a variety of reasons. One of the most common is marketing and analytics teams not paying enough attention to the attribution models based on which they make their decisions. They simply use the default attribution model available in the analytics system, or select a model when they start working with a new channel but don’t change the model as the company evolves and the marketing mix, the length of the product sales cycle, and external factors change.

→ 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.