Metrics TIPS

GET THE LATEST TIPS EACH WEEK

Get product resources, conference updates, along with access to an exclusive Slack channel by subscribing below.

What are some decisions that the Facebook “growth team” made to rapidly grow the number of Facebook users?

Andy Johns describes the general types of decisions that the growth team at Facebook used to help Facebook reach it’s first 500 million users. He describes decisions around tactics, decisions around strategy, decisions around hiring, and decisions around priorities and culture. (via @ibringtraffic)

Growth Decisions Based on Tracked Positive & Negative Experiences

If existing user’s experiences are positive, you want them to tell others. If their experiences are negative you want them to tell you. Dicky Singh explains how you can use “the difference between positive and negative experiences to make multiple decisions, including whether to ask for an app-store rating, ask for a review in an e-commerce product, recommend user update to a newer version of the app, redirect to support, or recommend installing other apps.” (via @dickeysingh)

Become Data-Driven: 9 Data-ish Resources for Product, Growth, and Marketing Professionals

You want to grow your product and you’re convinced that data-driven decisions can help you do that. But if you’d like to learn more about data driving your decisions, where can you look? Buckley Barlow suggests nine resources “that can help you get started in data-driven decision making. You need to start with a foundational knowledge, garnered from the best experts in data.” (via @BuckleyBarlow)

How Data-Driven Decisions Set You Up for Growth

You may think you know what your audience wants so that you can grow your product. If you don’t have data to confirm your hypothesis, you’re not going to know for sure. When you have data to inform your decisions, you can improve your decision making and increase the likelihood that you’ll experience the growth you seek. Elizabeth Chung takes a look at “the importance of testing your hypotheses and how a data-driven culture” can help your product grow. Elizabeth suggested to support a data-driven mindset, you may want to encourage brainstorms, create a schedule of A/B tests, and ask “why?” (via @elizchung)

Product Analytics: A Comprehensive Guide to Using Data for Better Product Decisions

If you want to grow your product, you need to get new customers and retain the customers that you already have. In order to do that, you need to be able to answer a series of questions about your customers. Ruben Ugarte explains how product analytics can help you answer those questions and make decisions that impact the growth of your product. (via @ugarteruben)

Why You are Probably Interviewing the Wrong People (and How to Fix it)

According to Teresa Torres, “quantitative methods live in the realm of statistics. Qualitative methods do not.” She used both methods during customer interviews but saw the latter as a way to “describe the participant’s experience”, and the former as a way to “predict the attributes or behavior of our audience”.

Qualitative vs Quantitative Feedback?

Truly data-driven product development calls for both. Each form of data has strengths and weaknesses in different circumstances. Cliff Gilley asserts that you must know the audience you are analyzing and, considering the vast amount of data that could be collected, avoid analysis paralysis.

When Do You Use Quantitative vs. Qualitative Research?

To decide which kind of data to work with, Ron Yang suggests that you ask yourself, “would my problem best be solved by feelings or facts?“. Qualitative research helps you to find subjective themes in data collected from interviews and focus groups. Whereas quantitative data helps you find objective data through methods like A/B testing, surveys and analytic tools.

Qualitative vs. Quantitative

Simply put, “we call data ‘quantitative’ if it is in numerical form and ‘qualitative’ if it is not”. So follows, by Saul McLeod, an overview of each type of data and methods used to obtain them.

Effective quantitative research requires good experiment design

In-product analytics, error mining, and customer support data are great to understand an existing product. When you’re working on a new product you need a different source of information to guide your decisions. You need to build experiments, and you need to make sure those experiments are designed appropriately.  Teresa Torres shares why you should run experiments to test specific assumptions instead of ideas and how to go about doing that.