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.

Continue reading “Getting rid of IDFA, GAID and Cookies. The imminent future of ad systems”

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.

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

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.

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

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.

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

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.

Continue reading “Ultimate Guide to a Data Analyst Role: Skills and Requirements”

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.

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

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.


Continue reading “Mistakes in A/B Testing: Guide to Failing the Right Way”

Peeking problem – the fatal mistake in A/B testing and experimentation

You can make many mistakes while designing, running, and analyzing A/B tests, but one of them is outstandingly tricky. Called the “peeking problem,” this mistake is a side effect of checking the results and taking action before the A/B test is over.

An interesting thing about the peeking problem is that even masters of A/B testing (those who have learned to check if the observed difference is statistically significant or not) still make this mistake.

P.S. 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.

Continue reading “Peeking problem – the fatal mistake in A/B testing and experimentation”

Growth expert Sean Ellis joins GoPractice team

Recently, Sean Ellis, entrepreneur and the author of Hacking Growth, joined our team on GoPractice. Sean brings years of invaluable experience in product management to our fast-growing community. But what is even more fascinating is what made him interested in Simulator in the first place and convinced him to help make it an even better experience. Here is the journey that led Sean to GoPractice.

P.S. 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.

Continue reading “Growth expert Sean Ellis joins GoPractice team”

iOS 14 & IDFA & Attribution: A Global Change in the Mobile Advertising Market

The mobile industry is undergoing one of the most fundamental changes of recent years. Apple has decided that early 2021, app developers will no longer have access to IDFA by default.

IDFA is a unique device identifier used for ad attribution, retargeting, alike audiences, analytics and other tasks. After the change, in order to receive the IDFA, an app developer must explicitly request the user’s permission (which is similar to allowing push notifications in an app). According to various estimates, the share of users who will provide access to their IDFA doesn’t exceed 10%.

Apple has provided privacy-friendly alternatives for attribution, but they fail to cover even a small fraction of the tasks that teams working on developing and promoting mobile apps currently have.

This shift means that mobile marketing (estimated at $80 billion), and by extension the mobile industry, are about to change drastically. In this essay, we will discuss in detail what will change, how it will affect the main players in the mobile advertising market such as developers, ad systems, attribution service providers, and advertisers.

P.S. 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.

Continue reading “iOS 14 & IDFA & Attribution: A Global Change in the Mobile Advertising Market”