Category posts
Growth channels
Editorial
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
- 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.
- 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.

Editorial
In a series of essays for GoPractice blog, Zakhar Stashevsky, CMO at Letyshops, will explore how to influence growth through effective channel management. Letyshops is a cashback service that allows users to return part of the money they spend on online shopping.
In the first essay, Zakhar discusses why you pay careful attention to the details of attribution model and incrementality when calculating the unit economics (ROI) of your ad campaigns.
Calculating unit economics is easy when you have perfect tracking and attribution for your ad campaigns. But in the real world, perfect tracking and attribution is virtually impossible.
Without understanding the features of the attribution methods used, the specifics of the channels, and the problem of traffic incrementality, unit economy calculation can lead to one of two errors:
- The team underestimates the channel and does not take a full advantage of it
- The team overestimates the channel (this is called incrementality) and loses money
Many teams make these mistakes. We made them ourselves when we were scaling Letyshops. Thanks to this experience, I can now share my thoughts on how to identify and avoid these errors.
→ 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.
Oleg Ya
In late 2013, Fab, a fast-growing e-commerce startup that had raised $330 million in funding, realized that it had a serious problem: Its business model wasn’t working. The company started on a downward sloped that began with laying off many employees (including its co-founder). In 2015, the company, which had been valued at more than $1bn, was acquired by PCH Innovations for a mere $15 million.
What went wrong? Well, like all commercial failures, Fab’s story is complicated and unique. But one recurring theme can be seen in the demise of this once-promising startup: over-reliance on paid marketing.
In this essay, I’ll talk about a growth model based on paid marketing. I will discuss the limitations and the hidden risks of these models, and the consequences of ignoring these risks. I will also discuss how to make the growth model based on paid marketing sustainable and secure.
Let’s assume you’ve reached the product/market fit, which means your product generates value for a specific market segment. You’ve also found the advertising channels where LTV (Lifetime Value) of the acquired users exceeds CAC (Customer Acquisition Cost)—you have created effective channels for delivering the product to your target audience.
That’s great news because few products get this far. The vast majority get eliminated from the race at earlier stages. But it’s not yet time to sit back and relax. There are new dangers ahead, especially if the product’s growth becomes highly dependent on paid user acquisition channels.
→ 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.

Oleg Ya
Classical economics is built on the assumption that people act rationally, which means their decisions are aimed at maximizing their benefits. This statement (which is a base for the classic economics theory), is a bit doubtful, partly because people usually don’t have all the necessary information to make the best decision in a given situation. But even within the framework of the available information, people tend to make irrational decisions.
In this post I will show you some interesting experiments that highlight relevant characteristics and patterns in humans’ decision making processes. Most of these experiments are about the way people behave when deciding about purchasing something, so you can easily apply them to your business or everyday life.
I have to say, I really like the picture below as it perfectly portrays the main idea of this article. Interestingly, squares A and B have the same color in this picture. (You can check it yourself using Photoshop or some other photo-editing tool if you don’t believe me.)
→ 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.

Oleg Ya
A couple of years ago, I wanted to turn my theoretical app store optimization (ASO) knowledge into a working skill.
So I decided to develop a mobile game. My goal was to validate the hypothesis that in the super-competitive mobile gaming market, you can launch a product that will grow into something large solely through organic traffic.
Let me say right away that I did validated this hypothesis: A mobile game we created over the weekend ended up amassing over 2 million downloads, and received over 30,000 new users per day at its peak, all through organic traffic.
But the path to success looked nothing like the original plan.
Here is the story of how the project evolved and how one small change increased the number of downloads by 200%.
→ 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.

Other content series
that you might find useful
- Addressing user pain points vs solving user problems better
- Product manager skills: evolution of a PM role and its transformation
- Product metrics, growth metrics, and added value metrics
- Customer retention levers: task frequency and added value
- How to measure the added value of a product
- Should a product be 10 times better to achieve product/market fit?
- Product/market fit can be weak or strong and can change over time
- Two types of product work: creating value and delivering value
- What is the difference between growth product manager, marketing manager, and core PM
- When user activation matters and you should focus on it
- User activation is one of the key levers for product growth
- The dos and don’ts of measuring user activation
- How “aha moment” and the path to it change depending on the use case
- How to find “aha moment”: a qualitative plus quantitative approach
- How to determine the conditions necessary for the “aha moment”
- Time to value: an important lever for user activation growth
- How time to value and product complexity shape user activation
- Product-level building blocks for designing activation
- When and why to add people to the user activation process
- Session analysis: an important tool for designing activation
- CJM: from first encounter to the “aha moment”
- Designing activation in reverse: value first, acquisition channels last
- User activation starts long before sign-up
- Value windows: finding when users are ready to benefit from your product
- Why objective vs. perceived product value matters for activation
- Testing user activation fit for diverse use cases
- When to invest in optimizing user onboarding and activation
- Optimize user activation by reducing friction and strengthening motivation
- Reducing friction, strengthening user motivation: onboarding scenarios and solutions
- How to improve user activation by obtaining and leveraging additional user data
- Tax/benefit framework for analyzing user activation
- How well do you articulate value during user activation? Check with the value communication framework
- How product teams get the “aha moment” wrong
- Slack vs Teams vs Workplace: the intriguing dynamics of the work messenger market
- How the “Slack vs Microsoft Teams” race evolves as the world switches to remote work
- How Revolut Trading was built. The importance of industry expertise and the balance of conservative and new approaches
- The values and principles of Wise. Key ideas from the Breakout Growth Podcast by Sean Ellis
- How to calculate customer Lifetime Value. The do’s and don’ts of LTV calculation
- Guide to ARPU: formula, calculation example, LTV vs ARPU
- How to calculate unit economics for your business
- Experiments where you make your product worse – the most underrated product manager tool
- Why your A/B tests take longer than they should
- Peeking problem – the fatal mistake in A/B testing and experimentation
- Mistakes in A/B testing: guide to failing the right way
- Designing product experiments: template and examples
- To reduce your product’s churn rate, first find out why users stay
- What is product/market fit and how to measure PMF
- How engagement metrics can be misleading
- How to forecast key product metrics through cohort analysis
- Cohort analysis. Product metrics vs growth metrics
- Correlation and causation: how to tell the difference and why it matters for products
- How product habits are formed and what dopamine has to do with it
- Hook Model: encouraging a product habit to improve retention
- Not every product is habit-forming, but all products can have loyal users
- How to design and run JTBD research interviews: guide and templates
- Is product management the right choice for you? This is your checklist
- Common mistakes made by junior product managers and how to overcome them
- Product sense demystified. The importance behind the buzzword
- Using data for strategic decisions
- The downsides of a data-driven culture
- Moving from a startup to an enterprise as a product manager
- Using data to understand competitive and market dynamics
- Data-driven, data-informed, and data-inspired product decisions. What are the differences and when should you use each one?
- Pros and cons of a data-driven culture
- Quantitative vs qualitative data: what is the difference and when should you use one instead of the other
- Losing sight of real users and their needs behind the metrics. How can product teams avoid this?
- How to move from engineering to product management?
- How to establish effective collaboration between product managers and data analysts
- Metrics to focus on before and after product/market fit. How to better understand your product at different stages?
- How can PMs encourage more teammates to use data?
- Data cherry-picking to support your hypothesis. What is it? Why is it bad?
- Data mistakes to know and avoid as a product manager
- Key data skills for product managers: experienced PMs sharing their thoughts
- How to move from marketing to product management?
- How to increase the effectiveness of your product analysts
- Why every team member should know the key product metrics
- How to move from marketing to product management?
- Key data skills for product managers: experienced PMs sharing their thoughts
- Product manager skills: evolution of a PM role and its transformation
- What is the difference between growth product manager, marketing manager, and core PM
- How to move from engineering to product management?
- Product growth, reinvented: what growth hacking is (and isn’t)
- Moving from a startup to an enterprise as a product manager
- Product manager interview: real questions plus guide for employers and candidates
- Rolling retention, Day N retention, and the many facets of the retention metric
- Long-term retention—the foundation of sustainable product growth
- Retention: how to understand, calculate, and improve it
- Errors in calculating ROI and unit economics. Impact of attribution models and incrementality on the ROI calculation of marketing channels
- Traffic attribution models. Why attribution models need to change along with growth channels, product, business objective and external environment