Your Customers Don’t Care if You Use Qual or Quant Data

As research and product leaders, we tend to obsess over how the sausage is made. This makes sense as innovation is our craft and mixed method research and cross-functional collaboration are our tools. But it’s important to remember that customers frequently don’t care how we arrived at our decisions. They just want their problem solved.

The distinctions we make between qualitative and quantitative research for example are professional constructs. They’re not even that clear cut – you can easily turn qualitative data into quantitative data. Convert text responses into discrete sentiments and, VOILA!

That’s not to say that we shouldn’t conduct mixed method research. Of course we should! But we shouldn’t begin our inquiries with tools. Instead, begin with the decision that needs to be made. Here’s how most conversations about data unfortunately go:

Person X: “I need a dashboard with this data.”

Person Y: “Let’s say the data says X, what are you going to do with it?”

Person X: “I don’t know.” 

We’re drowning in data and constantly collecting more and yet we don’t even begin with a framework for the decisions we’re going to make! Instead, we need to focus on creating value for the customer and then work backward. Once they do that, teams often realize they’re looking for the wrong thing, asking the wrong questions, using the wrong research and tools.

Start With the Decision and Work Backward

We all want to build better products and services. But once we get to the details, pen to paper, things start falling apart. Look at a question as simple as, “Who uses this feature?” Do you want to know, literally, the names of the users? Do you want to know what percentage of our users use it? Do you want an absolute total of users? Users who have ever used it? Used it recently? Used it frequently? 

Think of how many nuances are baked into such a simple question. There are 400 different ways it could be answered, and 399 of them won’t actually help anyone make any decisions. 

Instead, begin with the decision that needs to be made and create a mock dataset necessary to inform that decision. Based on the mock dataset, consider what types of research are needed to generate it. Research tools are just that – tools!

Decisions Matter More Than Tools

Growing up, if I struck out in a game of baseball, I’d often blame the baseball bat. “It’s the archer, not the bow,” my dad would say. Similarly, mixed method research is important, but the people and decisions we prioritize are infinitely more important.

I have seen both successful companies that over-index on quantitative data as well as those that over-index on qualitative data. What they all have in common is an obsession with the customer, not the tools and methods for research. Then there are companies that do high-quality mixed method research and drown in data, but ultimately fail to inform any important or high-impact decisions. 

As long as you’re prioritizing the right decisions and trying to reduce bias by understanding the market from multiple perspectives, you’ll satisfy customers with great products and services. Don’t get lost in the jargon.

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