How Labster prioritizes which user problems to solve next
“Labster, a science-learning platform, needed to decide which customisation problems to solve for its educators, with limited resources and plenty of conflicting requests. UX Researcher Tudor Cristian Bogdan gathered problem statements from internal workshops, then ran a Customer Problem Stack Ranking survey on OpinionX with 1,000 users to rank them by importance and split the results by customer segment.”
The results overturned the team's assumptions: the problem they expected to rank first came 17th, an unexpected opportunity rose to the top, and the highest-priority problems changed depending on the customer's use case. The lesson Tudor took away: the loudest customers aren't always the most representative, and knowing which problems matter most beats knowing they exist.
Background
Labster is an award-winning science learning platform that gives educators a catalogue of immersive virtual lab simulations, proven to take students from disengaged to inspired and prepared. At the time of this research, the team of over 350 employees had raised $147 million to date and served millions of students across 3,000+ educational institutions around the world.
Challenge
The more tailored Labster is to each educator's use case, the more immersive the experience for their students. But every new educator who signs up brings their own context that the platform has to adapt to.
At the time, the only way educators could tailor Labster was through the interactive quizzes at the end of each lab simulation. With limited resources, Labster's Product Management team had to prioritise which user problems were worth solving, working out which customisation opportunities would have the most impact on the most educators. The challenge was where to start.
Process
Tudor Cristian Bogdan, a UX Researcher at Labster, took on the question.
He started with two internal workshops: one with Account Managers to gather customisation problems from existing customers, and one with Sales Executives to gather the problems new users raised. Those workshops gave Tudor a bunch of pain points and feature suggestions that became his list of customer problem statements.
Before jumping into active research, Tudor identified the three questions he needed to answer:
Which problems are customers most keen to solve?
How often do customers experience or consider these problems?
How does a customer's use case influence the problems they're most keen to solve?
Based on those objectives, Tudor built a "Customer Problem Stack Ranking" survey on OpinionX and shared it with 1,000 Labster users. It showed users his problem statements as a series of head-to-head pairs, and their votes set the relative importance of each one. Alongside the ranking, Tudor added questions about problem frequency and use case, so he could later split the ranked results to compare the priorities of different customer segments.
Results
Tudor's survey results revealed three surprising insights:
The problem they expected to rank highest finished 17th!
An opportunity the team hadn't expected rose to the top for customers.
The highest-ranked problems varied a lot by the customer's main use case, which let Tudor focus on what mattered most to Labster's best-fit customers.
The results proved to Tudor how important it is to prioritise user problems by importance, not just confirm they exist:
“We already knew what our customers’ common problems were, but we didn’t know which ones were most important to them. We had never asked them to compare the importance of these problems before. Having a set of problems prioritized by our users was the game changer that OpinionX enabled.”
Tudor's research went on to inform multiple roadmap projects for the product team and changed how he now thinks about customer feedback and feature requests:
“The survey results helped me demonstrate to the broader team that the loudest voices are not always the most representative customers — just because you’ve got a very loud customer complaining about something does not mean they represent your broader customer base.”
How to prioritise user problems with real data
Company growth is about a lot more than just identifying and solving customer problems. You've got to know (1) which customers are your most important segment and (2) which problems those customers are most urgently trying to solve.
“As a rapidly scaling company, almost anything you do will move the needle, but this research made me realize that solving certain problems moves the needle a lot more than others.”
If Tudor had just trusted that customer feedback represents what the whole customer base cares about, he'd have pushed Labster toward the wrong priorities. He could have run a bunch of user interviews to find patterns instead, but at a scaling startup like Labster, you don't always have time for that. OpinionX let Tudor quickly draw on Labster's existing knowledge of customer problems and turn it into real data, to find the problems most worth solving first.
Create your own customer problem stack ranking survey like Tudor's with OpinionX, the advanced market research survey platform that thousands of product teams use to rank and segment their customers' top pains, preferences, and priorities.
Frequently asked questions
What is Customer Problem Stack Ranking? Customer Problem Stack Ranking asks your users to rank their own problems by importance, usually as a series of head-to-head pairs, so the results show which problems matter most, not just which ones exist. Labster used it to turn a long list of feature requests into a prioritised list.
How did Labster decide which problems to solve next? Tudor gathered problem statements from two internal workshops, then ran a Customer Problem Stack Ranking survey on OpinionX with 1,000 users. He also asked about problem frequency and use case, so he could split the ranked results by customer segment.
Why shouldn't product teams just listen to their loudest customers? Because the loudest customers aren't always the most representative. In Labster's case, the problem the team expected to rank first finished 17th, and priorities varied a lot by customer segment.
What did segmenting the results reveal? The highest-priority problems changed depending on the customer's main use case. That let Labster focus on the problems that mattered most to its best-fit customers, not an average that fit no one.