Katkin is Europe’s first fresh-cooked cat food and health company, and one of the UK’s most rapidly growing direct-to-consumer brands. Their digital product team aims to put customers at the heart of everything they do, and has always relied on customer feedback to evolve their products. 

Challenge: Traditional research methods lead to long time-to-insight

As Katkin scaled, they’ve been on the lookout for AI-enabled research tools that offer reliable insights without the downsides of traditional approaches, which they found include:

1. Long lead-times for projects including user interviews: Traditional user interviews involve time-consuming tasks like scheduling interviews, managing no-shows and long lead-times to book in the right participants, which can be challenging for lean teams that want to move fast.
2. Long time-to-insight: Each traditional user interview, where the researcher interviews the user in-person or via video call, can take weeks to arrange and a further 2 hours each to conduct and analyze, making it hard to build a continuous research process with a lean team. Together, this lead to the typical lead time for completing a research project involving any user interviews was more than two weeks.‍
3. Out-of-context data: Traditional moderated user interviews and usability tests often involve prompting participants to imagine a hypothetical scenario or recalling a past experience, instead of the research being conducted in-the-moment as users navigate through the user journey that’s being researched. This often raises doubts about the accuracy of the data that’s collected.

That’s why the digital product team at KatKin turned to Wondering, to quickly gain insights that could help them elevate their users’ experiences further through AI-powered user research methods like AI-led user interviews.

The Solution: 4x+ shorter time to insight with AI-led user interviews deployed in-product

When Matilde Betti Canuti, Senior Product Designer, and her team first came across Wondering, they were delighted at the efficiency in which they could run user research with the help of Wondering’s AI-led user interviews. 

"With traditional user interviews it would take weeks to set up and complete interviews, but with Wondering, I can launch an AI-led study on a Friday evening and have results by Monday morning," Matilde explains. "We completed a full research project in just 3 days with detailed interviews with more than 30 users, which would have taken 2 weeks to complete previously." 

“As the AI is trained to ask intelligent interview-style follow-up questions, the depth and richness of the insights we get are not compromised compared to a traditional user interview. The AI-led interviews are able to stay on topic, remain professional and even express empathy throughout the interaction with the user, which is important when speaking to people about subjects close to their hearts, such as their pets.” Mathilde continued.

"Shortening the time it takes us to get insights also has helped stakeholder engagement with research, because it enables them to quickly get an understanding of user needs" Matilde noted.

“What we also like about Wondering,” Matilde added, “was the wide range of users we can speak to. We have access to Wondering’s global panel of 150,000 participants for discovery research to identify problems and validate concepts; we can also use their in-product studies to interview customers as they use our products, helping us get feedback on new designs and get insights about the product journeys they go through.”

“Wondering helps us gain valuable insights from potential and existing customers," Matilde concluded.

Take the guesswork out of your product development with AI-powered user interviews and analysis

By deploying AI-powered user research studies to collect targeted feedback from your customers at scale, you can:

1. Validate new ideas and test your designs: Test your designs before you start building and quickly validate new ideas and concepts
2. Optimize your product journeys: Uncover pain points and areas for improvement in your core product journeys
3. Prioritize your product roadmap: Make informed decisions on which features to build next based on a deeper understanding of your users

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