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including whether to ask for an app-store rating, ask for a review in an e-commerce product, recommend user update to a newer version of the app, redirect to support, or recommend installing other apps.”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)
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.
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.