5 Examples: Transform Customer Research to Actionable Insights with Journey AI
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See how Journey AI turns 5 customer research sources into customer journeys in these real-life scenarios
All businesses want to get inside the heads and hearts of their customers. But dealing with information coming from different conversations, interviews, and surveys at different times can be confusing and messy. Still, there are risks to not properly analyzing customer data: if you overlook the details of what your customers are telling you, you may be leaving revenue and growth opportunities on the table.
We built Journey AI to make the research process less painful and time consuming. AI can and should be used as a research assistant for the customer experience, without replacing the customer-facing teams that facilitate relationships. AI can help with speed, efficiency, and accuracy of data analysis, while also breaking down customer needs from an unbiased point of view. With Journey AI, you can gather tons of customer insights at once, and map them onto actionable journeys in just a few minutes.
But how does it actually fit into your day-to-day workflow, and can it work with all of the different sources of customer data? In this article, we’ll walk through how to use Journey AI to create journeys from:
Interview transcripts
Sticky notes and whiteboard sessions
Research reports
Support tickets
Knowledge base articles
For each source of customer research, we'll show you a journey generated from that input. We’ll give an example of a real journey created from that data. We’ll show what the journey looks like in TheyDo, and how it extracts insights to drive customer-centric decision-making.
Thanks to your new AI assistant, you can start making sense of all of your research right away. Just follow these examples.
Use Case 1: Synthesize customer interviews
For this example, we used the transcript of a recording from Lenny’s Podcast titled, “The UX Research reckoning is here.” In it, Lenny Rachitsky interviews Judd Antin, former UX Research Director at Airbnb and Meta. They discuss how research departments have been affected by layoffs in tech, and what researchers can do to prove the financial impact of their work to executives.
(We chose this interview on purpose – give it a listen if you want to learn more about the future of UX research.)
We input the transcript into Journey AI, and voilà, a complete customer journey appeared: The Researchers’ Impact Journey.
The AI-generated journey covers the new responsibility of the UX researcher: to make an impact on the business, in addition to learning about the customer and the market.
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View the public journey in TheyDo.
The steps and phases of the journey follow the steps and phases of a UX researcher’s job (according to Judd) and the pains, gains, and observations that arise along the way. Direct quotes from the source interviews are nested within the insights, and a key quote is highlighted for each.
Overall, the journey can be used to pinpoint exactly when and where UX researchers can add value in their roles, so they can improve their career prospects as well as contribute to business growth.
How to create a journey using Journey AI
To do this with one of your own customer interviews, follow the steps below (or watch a video tutorial):
Select “Map with Journey AI” from the main dashboard.
Upload your interview transcript.
Add some context to the journey by identifying the source type (“Interview” in this case), creating a name for the interview file, and selecting the relevant persona.
Click “Create a journey.”
Review the insights generated by Journey AI and decide whether to accept, reject, or modify them.
Once the journey has been created, you can add as many research files as you’d like to the same journey, and it will update accordingly.
Use Case 2: Collect takeaways from sticky notes
Ah, sticky notes. Those wonderful, colorful little squares that contain bite-size brilliance, only to be thrown away after a week or month stuck to a wall.
For this use case, we borrowed insights from another episode of Lenny’s Podcast, this one a compilation episode called “Failure,” in which several guests discuss their major career failures, what they learned from them, and how they led to their current success.
We collected their insights into digital sticky notes using Mural, and then clustered them by speaker and theme.
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Imagine that these guests were all customers sharing their experiences with you, and you were recording and clustering their soundbites. How do those clusters become journeys?
Without AI, you’d probably pick one cluster to focus on, based on incomplete data – a hunch, a top-down request, your own bias towards certain problems or opportunities.
With Journey AI, you can make sure that customer needs come first — based on their words alone.
The end result is a journey based on the job of learning how to create wisdom from failure.
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View the public journey in TheyDo for “Failure.”
To do this yourself, you can simply drag and select your text or sticky notes within Mural, Miro, Figjam or other digital whiteboard tools. After that you can follow steps 3-5 outlined above.
Use Case 3: Analyze research reports
For this example, we’ll show you how Journey AI can work with complex data from (pretty much) any source. We pulled a McKinsey research report about tying customer experience initiatives to business value. Relevant!
When we input the text of the report into Journey AI, it generated a detailed journey based on the job of creating value-driven customer experiences.
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View the journey created from the report.
Because the report is comprehensive, Journey AI creates a detailed customer journey. Each journey step contains qualitative information about how well that step is functioning. This way, it’s easy to see where improvements can be made.
To do this yourself: Simply copy and paste your research report’s findings into Journey AI. Then, you can follow steps 3-5 outlined above.
Use Case 4: Solve support conversations
Customer support teams are often in triage mode — the volume of support requests, comments, and questions makes it difficult to glean strategic insights.
Journey AI can help sift through an overwhelming number of tickets to find the feedback that will be useful for making customer experience improvements.
In this example, we input various TheyDo support conversations where customers were experiencing product usage and account issues.
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View the journey in TheyDo.
Journey AI took all the messages and sorted them into pains, needs, and gains, also creating an overview that shows exactly when, why, and how the user experience could be enhanced.
To do this yourself: download your tickets from your customer support tool (Zendesk, Intercom, etc. usually will have this capability). Copy and paste the data into Journey AI. Then, follow steps 3-5 as outlined above.
Use Case 5: Transform a series of knowledge base articles
Knowledge bases contain a wealth of customer knowledge — but, despite the authors’ best efforts, they often lie quiet and forgotten, like a library gathering dust. Nobody wants to sift through several different articles to find one small piece of information. It’s boring and no one has time. They need a shortcut.
Journey AI puts your knowledge base to work by combining the insights of several articles into one journey.
In this example, we uploaded two articles from the TheyDo knowledge base into Journey AI, “How to set up a customer journey in 4 easy steps” and “Micro and Macro journeys.” The result was a journey that merged the two topics into a map about journey mapping.
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View the public journey.
In this case, the journey not only synthesizes existing knowledge, but creates new knowledge based on the work you’ve done in the past.
To do this yourself: Copy and paste the text of a knowledge base article into Journey AI. Then, follow steps 3-5 outlined above.
Don’t just speed up your research with Journey AI — improve on it
Journey AI is a reliable research partner because it sticks to the primary sources, and the principles you set out for it. It produces consistent outcomes, with consistent levels of accuracy and quality. When you add Journey AI into the mix, you can depend on it to turn around work (especially the boring stuff) quickly. In other words, given a variety of sources, Journey AI will always deliver usable results on time.
Each piece of research is valuable because it shows you many different perspectives. In fact, adding several research reports on the same topic can show you many different opportunities within in a single journey. Additionally, the more data you add, the more you will start to see patterns and similarities in the insights. You can save time by not solving the same problem over and over again. By analyzing all of these journeys, where they converge and diverge, you can get a more complete picture of the overall customer experience.
There are tons of different ways to support your customer research with Journey AI. These examples are just a starting point. Which one will you try first?