LinkedIn principal author Doug Winnie describes what to look for when you build a research plan. Using a matrix of quantitative, qualitative, internal and external data, you can create a well-balanced research plan for your project and use what you learn to formulate new questions and continue getting better insights.
- [Narrator] A well balanced research plan has two axis. One looks at data as being quantitative versus qualitative. The second looks at data coming from internal or external sources. Let's look at an example to help illustrate how these four quadrants can work to build a research plan. Let's say we have an app that allows you to chat with your family members in a private group. You're trying to find out what to do in the next major version of the app. You have been working on small improvements but your app isn't performing very well in the app store.
If we use our grid we can look at sources that are quantitative internal data, quantitative external data, qualitative internal data and qualitative external data. Inside of our current app there are anonymous analytic tools that report what sections of an app people are using, which features are used, and how long people use the app. This quantitative internal data might indicate that sections of your app aren't used much at all. Or after they try a certain feature they then immediately go to the help link in the app.
While these don't tell you definitively what people are doing, it certainly gives you questions that you can postulate and try to answer with more data. This is an excellent example of quantitative internal data. Okay, we know that we don't have a lot of users for our app, so there are tons of potential customers in our audience. We can decide to gather quantitative external data through a survey asking parents and children what they would want in a family chat app. The results of that survey could indicate that parents and their children often use different platforms.
Or that children want emoji support. A survey is a great example of quantitative external data. When you are going outside of your app or company to get information. But sometimes we want to know specifics that require us to sit down and have a conversation with someone. Customer interviews or meetings are a great way to do that and they are considered internal qualitative sources of data. Then there are industry or analyst reports that can serve as qualitative information about your external audience.
It is qualitative because it is sharing trends and indicators that isn't quantitative data that you would get in a survey. These reports could talk about app and platform statistics from members of families. Perhaps the report indicates that the age when children get new phones has gone down dramatically in the last few years. That could be interesting to see if that information might lead to a new feature for your app. Each quadrant in the graph can create new questions that you can verify and ask in another quadrant.
A survey might report a trend that you can verify in a customer interview. Which might then be verified in a industry report or through internal analytics data. As you can see, using the quantitative, qualitative, internal, external breakdown is a good starting point to make sure that you are balancing your data research plan.
- Types of products and industries
- Leading through influence
- Understanding your team
- Using an agile or waterfall development cycle
- Managing your product life cycle
- Researching your market, customers, and ideas
- Planning the product
- Building the product
- Releasing the product
- Refining the product
- Understanding when it's time to retire the product