Category posts
Data and analytics

Data is essential for every product. These essays will help you improve your skills in analytics and avoid data mistakes.
Author:
Editorial
Using data to understand competitive and market dynamics
Using data to understand competitive and market dynamics

Product managers know that understanding their market and competition inside and out is vital to the success of their products. Comprehensive market knowledge tells you what problems your customers are trying to solve, what they want, and ultimately what new features or products to build. Knowing the competitive landscape helps to set your business apart, allows you to create a strategy to deal with new competitive developments, and helps to arm your sales team to win against the competition. 

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Author:
Editorial
Data-driven, data-informed, and data-inspired product decisions. What are the differences and when should you use each one?
Data-driven, data-informed, and data-inspired product decisions. What are the differences and when should you use each one?

When an organization says they are data-driven, they typically mean that they base decisions on data. But there can be vast differences with how data is used to make these decisions. Is data only being used to validate straightforward decisions? Are multiple sources of data combined with other factors to determine priorities like the features to be worked on next quarter? Or is an exploration of data being used to spark innovation and determine new strategy? Each situation requires different skills, tools, and ways of working with data to be successful.

This is why the concepts of data-informed and data-inspired are being added to the data-driven discussion; they allow for a more nuanced definition of how data is actually used in an organization. Data-informed and data-inspired decisions consider depending not only on data for clear-cut decisions, but on using data in conjunction with other important influences and to invent something new.

Some may argue that adding the terms data-informed and data-inspired to the data-driven discussion adds complexity and muddles the discussion around data. While that may be true in some cases, really understanding how to correctly use data based on a particular need is critical to creating products that customers love. In the end, the terminology isn’t as important as making sure you’re getting the most out of data. 

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Author:
Editorial
Quantitative vs qualitative data: what is the difference and when should you use one instead of the other
Quantitative vs qualitative data: what is the difference and when should you use one instead of the other

Product managers use data at the heart of their decision making. There are two types of data that they rely on: quantitative and qualitative. Quantitative data refers to numerical data that can be measured; examples include number of clicks, number of users, and monthly recurring revenue. Qualitative data cannot be counted, but instead describes traits or features such as ease of use, user likes and dislikes, and motivations behind user actions. 

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Author:
Editorial
How can PMs encourage more teammates to use data?
How can PMs encourage more teammates to use data?

Working with data helps companies across the board to unlock their potential and become more productive and better at making decisions. However, making people in the team and company rely on data involves a lot of work. Product managers must often set a strategy, reinvent processes, and change organizational behavior. 

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Author:
Editorial
Data cherry-picking to support your hypothesis. What is it? Why is it bad? 
Data cherry-picking to support your hypothesis. What is it? Why is it bad? 

Data is an essential part of the work of every product manager. It helps to form and validate hypotheses, provide more insights about user behavior, and make better decisions and track product changes.

But the misuse of data can be harmful. One important example is selecting only data that confirms a particular hypothesis and ignores relevant contradictory evidence.

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Author:
Editorial
Data mistakes to know and avoid as a product manager
Data mistakes to know and avoid as a product manager

Data is a key part of product management. We gain our intuition by looking at data. We come up with hypotheses based on our observations of data. We test and validate these hypotheses using data. And we make key product decisions and monitor and track changes in data. In a nutshell, data helps us go from ideas to facts to decisions.

However, working with data is also wrought with pitfalls that every product manager should avoid. Data can sometimes be misleading or incomplete. It might not tell the whole truth and lead you in the wrong direction. And it might amplify your own erroneous assumptions.

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Author:
Editorial
Designing product experiments: template and examples

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 former student 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.

Designing product experiments: template and examples
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Author:
Oleg Ya
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.

Test your product management and data skills with this free Growth Skills Assessment Test.

Learn data-driven product management in Simulator by GoPractice.

Mistakes in A/B testing: guide to failing the right way
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Author:
Oleg Ya
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.

Test your product management and data skills with this free Growth Skills Assessment Test.

Learn data-driven product management in Simulator by GoPractice.

Peeking problem – the fatal mistake in A/B testing and experimentation
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Author:
Oleg Ya
Why your A/B tests take longer than they should

When conducting experiments, teams usually include all the active users in their tests, or sometimes they tend to add all the new users who join the app during this test. So when calculating the metrics for different test groups, all the data from the moment the A/B test kicks off is taken into account.

Today I’ll talk about how you can reduce the time required to get the signal on the change you are testing in a product. You can do that by changing the process of adding users to the A/B test, and in this essay I will show you how you can do it.

Test your product management and data skills with this free Growth Skills Assessment Test.

Learn data-driven product management in Simulator by GoPractice.

Why your A/B tests take longer than they should
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