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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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.

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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.

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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.

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Key product management quotes from Zero to One

There are a few books that I consider must-read for anyone working on products. Today I’ll be talking about my favourite ideas from Peter Thiel’s iconic book Zero to One: Notes on Startups, or How to Build the Future

Here’s what the scholar Nassim Taleb has to say about Zero to One: “When a risk taker writes a book, read it. In the case of Peter Thiel, read it twice. Or, to be safe, three times. This is a classic.”

Taleb’s remark on Zero to One surprised me. And here is why.

At this point, I was already familiar with a few books written by Nassim Taleb. I had also read (for a few times) Thiel’s class notes that later became the manuscript for Zero to One. I really liked the harmony and sequence of thoughts in each of these books, but at the same time for me the perspectives on life of the authors seemed rather opposite.

One way or another, Zero to One is beyond amazing. I enjoy re-reading it every once in a while, and each time I find something new. And this is exactly what sets aside great books from good ones.

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.

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How to forecast key product metrics through cohort analysis

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.

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.

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Why every team member should know the key product metrics

When I worked at Facebook, the Workplace analytics team had a cool tradition: The team’s weekly meetings always started with a small data quiz.

The winner of the previous week’s competition would prepare a question about the product’s key metrics. For example, “what was last month’s MAU?” or “how many new users joined last week?” or “what proportion of the new companies reach 10 users?” or “what was last month’s revenue?” The question had one requirement: Its answer had to be found on the team’s dashboard.

The participants were to write down the answer without getting help from computers, which meant we could only use our memory to do so. The person whose answer was closest to the correct number got +1 point in the chart, and the person who was the farthest lost 1 point. Every six months, a winner was chosen and the game started again.

I participated in five seasons and won three of them. In one of the final rounds, I was tied with another analyst. The team arranged the final round, where we had to answer five questions in a blitz quiz. I managed to score the winning point and won the mug that you see in the photo below.

I told this story not because I wanted to brag about winning the quiz (well, this too, to be honest). In almost every quiz, the respondents’ guesses on metrics were distributed across a wide range, which I found surprising.

Why? Well, first of all, it was the analysts who played the game. They were the people who worked with data most of their time and should have been good at navigating it. Second, these analysts were working at Facebook, a company that has a very advanced and strong data culture. At Facebook, each team has clear goals, dashboards are available to all the company’s employees, and all meetings start with progress updates on key metrics. How could these people be so wrong in answering questions about the product they were working on?

If you decide to play this game with your company’s employees, you will most likely be as surprised as I was. It will turn out that most people have very vague ideas about the key metrics of your product and business. And some people will have no idea at all.

In this essay, we will discuss why it is important for team members to remember at least approximate values ​​of the key product metrics, why this usually doesn’t happen, and how to get there.

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 “Why every team member should know the key product metrics”