Why Data Science and UX Research Teams are Better Together "Product people - Product managers, product designers, UX designers, UX researchers, Business analysts, developers, makers & entrepreneurs 8 February 2018 True Customer Success, Data Science, ProductTank, ProductTank San Francisco, User Testing, Ux Research, Mind the Product Mind the Product Ltd 614 Product Management 2.456
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Why Data Science and UX Research Teams are Better Together

In this talk from ProductTank San Francisco Chris Abad, who’s currently VP of product and design at User Testing, shares insights into how bringing together qualitative user research and quantitative data science teams is crucial for companies because it can help them to see the complete picture and inform critical product decisions. Chris shares real-world examples and lessons learned as User Testing established its Product Insights team, a team which is made up of UX researchers, data scientists and data engineers.

Here are a few quick insights from the talk:

Separate UX and Data Science Teams Miss the big Picture

Teams often lack a holistic, big picture view of the challenges they face. We’ve all been in situations where we have our hown perspective and view of things, and we miss the big picture. As a result we draw incorrect conclusions about our products and pursue with the wrong strategy.

Most companies’ user research and data science teams work separately

How do we avoid getting into such situations where the conclusions drawn are based on a limited perspective? In most companies, user research and data science work separately. But if researchers and data scientists work together they can provide a mix of both depth and reach when it comes to insights about the product and customers.

User Testing’s research and data science teams working together

Chris believes that by putting the two teams together you go beyond just basic usability testing or basic analytics. Tight integration between the two teams means you need to create a process that operates self-service, you also need to have a single objective, multiple loops between the two teams, as well as a single holistic view and insight in the end. A data scientist’s and UX researcher’s skills are shown below:-

Three Main Goals

When User Testing put these two groups together into one team, it set the team three goals to consider:

  1. How might we make ensure that we’re solving problems that are worth solving? Chris says the team informs the company’s strategy and helps it to decide what to build next.
  2. How can we understand if the things that we build are having the impact we would like? Most of the time we’re guessing, says Chris. The combined team helps to keep us honest about our guesses, he says, and helps us to understand where we’re right or wrong.
  3. How might we make product insights self-service across the company? Keeping the insights self-service is the fastest and most cost-effective way to scale and take this practice across the entire company.

Chris says combining the two teams was initially just an experiment, but it’s been more successful than anyone anticipated. User Testing is going through the process of redesigning the set-up of research plans and he uses this as an example of how a combined team can work together effectively and discover otherwise unknown patterns of user behavior. It’s given User Testing far deeper insights into how people use its product than it had before and has also delivered some actionable insights, shown below:-

Chris says that User Testing isn’t the only company finding that bringing these two teams together and he mentions that Spotify and AirBNB are doing this successfully. He advises that you get together UX research and data science teams in a room: “Give them a challenge to solve. They will bring a different perspectives and knowledge that will raise questions and perspective that the other side didn’t anticipate.”

This is a a powerful dynamic that helps avoid getting into situations where the conclusions are drawn based on a limited perspective.

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