01 / Business Problem
Practitioner attribution increased materially.
Broker attribution rose well beyond the estimated worst-case scenario, while growing practitioner frustration was reaching the product team through account managers and CX.
I was asked to understand what was driving the change — what practitioners were experiencing, where the product was falling short, and what was behind the increase in attribution.
Broker attribution spiked above the estimated worst-case scenario
A 45-point gap, with no explanation for it. This was the starting point for the research.
02 / Research Question
Understanding what was driving practitioner behaviour.
The question wasn't simply why attribution had increased. I needed to understand what was happening for practitioners and what was driving the change.
- What had changed for practitioners
- What was driving the increase in attribution
- What practitioners valued about the product
- Where they were experiencing friction or frustration
- Whether different types of practitioners were experiencing the problem differently
What was driving the change in practitioner behaviour, and where the product experience needed to improve.
03 / Research Approach
Three weeks: context research, interviews, and synthesis.
I started by understanding the market, customer segments, and how different practitioners worked. This gave me context for the feedback coming through account managers and CX and helped me form questions to explore with practitioners directly.
The interviews then focused on what had changed for them, how they worked with the product, what they valued, and what might be driving the increase in attribution.
04 / Market & Customer Context
Competitive structure and customer segments.
This wasn't a standalone strategy exercise. I needed to understand who the different types of customers were, how their businesses worked, what they needed from the platform, how they currently paid, and where the new pricing model might create friction for some of them and not others.
Competitive structure
A 2.5-player market: one clear leader, one established follower running a similar model, and a long tail of small listing portals splitting what's left. Illustrative — anonymised.
Customer segments
This gave me hypotheses to test in the interviews.
05 / Customer Interviews
Six interviews across three operator types.
I spoke with three types of operators: high-volume, boutique, and independent. The market analysis suggested their businesses worked differently, and that the pricing change might affect each group differently. Interviewing only one type would have missed that.
In the first interviews, I wanted to understand:
- How practitioners worked and acquired listings
- What they valued about the platform
- What had changed for them
- What was driving their behaviour
I started with their business model, workflow and experience with the platform. A new theme emerged: how listings were marketed and where they appeared affected how practitioners judged the platform's value.
I added questions about marketing, channel coverage and how listings generated business. The same theme came up across these interviews.
The later interviews reinforced the themes I was seeing across different types of practitioners.
What was driving the change in behaviour, and whether the problem differed across practitioner segments.
06 / Research Synthesis
I reviewed each interview as I went, tagging observations and grouping recurring themes. This helped me test emerging findings in later interviews.
Example
Several practitioners said they had to enter the same listing information into multiple channels.
The issue appeared across different types of practitioners.
Additional admin work could make the product harder to adopt.
Which themes were consistent across practitioners and important enough to raise with leadership.
07 / Findings & Recommendations
Three recommendations, each linked to a finding.
This didn't produce a single answer. It produced three recommendations, each tied to something from the market analysis or the interviews.
High-volume operators needed predictable costs to protect their margin at volume. The per-transaction model didn't fit that.
Practitioners weren't judging price on its own. Marketing reach and where listings appeared were part of how they judged value, not just the cost.
The interviews supported what the market analysis suggested: high-volume operators had different needs to boutique and independent operators, and the current model wasn't built around theirs.
The next step was to test these findings against quantitative and commercial data before committing to a pricing change.
Where the business should focus its next pricing iteration, and which segment's needs should shape it.
The research didn't produce a new interface. It gave the team a clearer understanding of the attribution problem, why practitioners were frustrated, and where the business should focus next.