How to move from Marketing to Product Management?

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.

So we decided to talk to a few people who successfully made this transition and answer the following questions:

  • Why did they decide to switch from marketing to product?
  • How long did the transition take, from the moment they decided they wanted to switch to the moment they got the PM job?
  • What was the hardest part of making the switch?
  • Which skills did they find useful?
  • Which skills did they already have and which ones did they have to learn?
  • How did they learn the skills they lacked?
  • What was the journey like once they made the switch to a product role? Did their marketing background help further down the road?
  • What would be their advice to someone from marketing who wants to switch to a PM position?

Note that we are taking a broad look at this transition, where you can approach the product through a wide spectrum of roles between marketing and product. For example, you can get a step closer from marketing to product by taking on a Product Marketing role. Another way marketing people made their way to product management was to start by becoming a product evangelist.

Ready to make a switch? Start with this free Growth Skills Assessment Test.
You can also jump straight into action and see what Product Manager’s job is like with Simulator by GoPractice!

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Cohort analysis. Product metrics vs growth metrics

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?

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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Traffic attribution models: Why attribution models need to change along with growth channels, product, business objective and external environment

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.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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Getting rid of IDFA, GAID and Cookies. The imminent future of ad systems

GoPractice spoke with Andrey Novoselsky, the head of advertising at VK (the biggest social network in Russia) from 2014 to 2021.

Andrey told us:

  • How the advertising systems work and why it is important to maintain the balance between three parties: users, advertisers, and the ad platform.
  • How UAC-like campaigns will affect the interests of these parties;
  • What will be the future of advertising without IDFA, GAID, and cookies; and what marketers and advertisers should prepare for in the future.

For the convenience of our readers, we are presenting our conversation with Andrey in Q&A format.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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The values and principles of Wise. Key ideas from the Breakout Growth Podcast by Sean Ellis

GoPractice is launching a new format: snapshots of noteworthy product and business podcast episodes.

In the first issue, we have prepared a retelling of the Breakout Growth Podcast episode, in which Sean Ellis and former PayPal marketing director Matt Lerner interview Nilan Peiris, VP of Growth at the fintech company Wise. In the beginning of July 2021 it had a valuation close to $11 billion and opted for a direct listing.

For the convenience of our readers, we have opted to provide a summary of the key points of the conversation instead of a full or partial transcript of the interview. You can listen to the full episode on your favorite podcast platform – Apple Podcasts, Spotify, Castbox.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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Errors in calculating ROI and unit economics. Impact of attribution models and incrementality on the ROI calculation of marketing channels

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.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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How Revolut Trading was built. The importance of industry expertise and the balance of conservative and new approaches.

GoPractice spoke with Dmitry Vasin, the Product Owner of the Revolut Trading from 2018 to 2020. Revolut is a leader in digital banking. At the beginning of 2021, it was the largest digital bank in Europe with over 15 million users and ~$5.5 billion valuation. During this period, Dmitry helped develop and launch the product.

Dmitry told us about the specifics of creating and developing a product in a strictly regulated market:

  • Why it is important to involve people with solid industry knowledge in product development
  • Why and how to combine a conservative approach with the desire to innovate
  • How regulation affects the work of a product manager.

In the conversation, Dmitry discussed the mistakes his team made at different levels—technical, legal, product—and the lessons he drew to avoid such mistakes in the future.

For the convenience of the reader, we present the material in Q&A format. We also divided the conversation into two chapters and a brief conclusion.

The first chapter gives a general idea of ​​the trading product from Revolut: what is its value, how it was launched, what it achieved in the market. The second chapter discusses the details of working in a strictly regulated market. In the summary, Dmitry provides some recommendations and reflects on what he would have done differently if he were to start from scratch.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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Ultimate Guide to a Data Analyst Role: Skills and Requirements

This post is written by Eugene Kozlov who was head of analytics of Yandex.Taxi – the leading ride hailing service worth several billion dollars. In this article, Eugene demystifies analytics roles at companies by breaking them down into different levels and management roles. Hopefully, with this guide, you’ll be better positioned to evaluate your position as analyst and those of analysts you will be hiring and managing at your company.

In eight years of work in analytics, I have interviewed and hired hundreds of people and have a good idea of the ins and outs of the analyst market.
The key knowledge here is that this market practically doesn’t exist. In 2019, I hired 34 analysts for my team, 23 of whom (68%) were interns or juniors. I would have been happy to hire someone more experienced, but people of such level didn’t exist, so I had to hire people with potential and help them grow.

In comparison, we hired 23% junior team members (five people out of 22) for data engineering teams, so the market is there. Data engineering is common and well developed in banks, telecom, and retail, which means that there are more ready-made specialists in the market.

This essay serves two purposes.

First is to clarify the terms in which we think about the levels of analysts. This will reduce the existing entropy in the market, where an arbitrary set of expectations and skills can be hidden behind a job opening or an analyst’s CV, ranging from project management and systems analysis to automation of routine business operations. In this market environment such prefixes as junior/senior/leading carry no information at all.

The second purpose is to provide a clear roadmap for growth and development as a data analyst or a person who has to do the work of a data analyst but has a different title to make it more applicable to everyone. At Yandex.Taxi, we are forced to build a growth career ladder for our employees, because otherwise we won’t be able to cope with the demand. The very formalization of analysts’ levels described in this essay is a consequence of this approach. However, not everyone works in large companies, and not everyone has access to a strong mentor. So this essay aims to help such people take a look at their growth points and work on them.

 If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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How to calculate Customer Lifetime Value. The do’s and don’ts of LTV calculation

LTV (Lifetime Value) is an important metric for decision-making in both marketing and product management. But measuring LTV is a bit tricky and you can easily make mistakes when calculating it. Moreover, even articles that have found their way to the first first page of Google search results contain mistakes when it comes to calculating LTV.

In this essay, I will discuss how to (not) calculate LTV, and how to avoid these common mistakes:

  • Calculating LTV based on revenue instead of contribution margin.
  • Calculating LTV by using users’ Lifetime which is calculated as 1/churn or in any other way.
  • Calculating LTV based on the average number of user purchases.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.

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Mistakes in A/B Testing: Guide to Failing the Right Way

Failing fast and often will help you learn from your mistakes sooner rather than later. This is an advice you hear often from successful product managers. But what you hear less often is that not every failure is a successful learning experience.

A product’s success is largely dependent on coming up with a hypothesis and designing the right tests. Without those elements, you might draw the wrong conclusions and steer your project in the wrong direction.

In his guest post for the GoPractice blog, Ethan Garr, VP of product at TelTech.co, shares some hard-earned experience in product testing. Through concrete case studies, Ethan shows us how to avoid key pitfalls when designing tests for hypotheses.

If you want to learn how data can help you build and grow products, try Simulator by GoPractice!
To find out where your product, data and growth skills stand, try the free Growth Skills Assessment Test.


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