KRY + Dot
KRY is one of Europe's leading digital healthcare platforms, operating telemedicine and physical healthcare facilities across Sweden, France, the UK, and Norway.
€800K/yr
revenue opportunity identified from a single open-ended question
10 min
to identify, analyze, and size the opportunity
20+
hypotheses tested simultaneously across dimensions
“We could have cut the funnel by age, by gender, by cohort, by dozens of dimensions. We just never had the time to do it systematically. Now we can.”
Claire Bertrand
Data Insights Lead, KRY
The Problem
KRY is one of Europe's leading digital healthcare platforms. Beyond telemedicine, KRY operates physical hospitals and healthcare facilities in Sweden, offering a broad range of in-person consultations alongside its digital services across Sweden, France, the UK, and Norway. Thousands of consultations daily, across four markets, through a symptom-driven booking funnel.
Like most companies that have reached a strong product maturity, KRY needs to find less obvious opportunities for growth. Some of them consist in fine-tuning the growth engine, improving conversion at specific funnel steps, for specific patient segments, in specific markets.
The challenge: fine-tuning requires exhaustive exploration across every combination of dimensions. And that requires time no team has.
- The Combinatorial Problem
- Break the funnel down by age, gender, specialty, market, booking type, time of day. The number of combinations to test grows exponentially. Data teams can only focus on the most obvious hypotheses.
- The Resource Constraint
- KRY had more data points than ever, but a smaller, leaner team. Testing a single hypothesis manually took 2 to 4 hours. Systematically testing all of them was simply not feasible.
- The Realistic Alternative
- Not hiring more analysts. Not paying a consulting firm. The realistic alternative was that these growth opportunities stayed hidden. Permanently.
Evolution
From Gut Feeling to Systematic Exploration
- Phase 1: Early Stage
- Gut Feeling
Growth from conviction. New specialties, new markets. Qualitative feedback from clinicians and ops teams. Obvious opportunities. - Phase 2: Structured
- KPI Dashboards
Revenue funnel mapped. Clear KPIs. But stuck in the box, only testing high-level hypotheses. Too many combinations, too little time. - Phase 3: With Dot
- Systematic AI
Every hypothesis tested. Every dimension explored. 20+ analyses run in parallel. Unknown unknowns surfaced and sized in minutes.
The Discovery
Claire connected Dot to KRY's existing Looker semantic layer, synced the metric glossary and business term definitions, and provided the revenue engine framework her team had built.
Then she asked one open-ended question: "Where are the growth opportunities for France?"
Dot analyzed a full year of data. It tested roughly 20 different hypotheses across multiple dimensions (age, specialty, funnel step, market, booking type) and surfaced three recommendations, each sized in euros.
KRY Booking Funnel
Findings
Dot tested ~20 hypotheses and surfaced the three with the highest impact. Only the signal, not the noise.
Psychiatry & Psychology Cancellation Rates
Cancellation rates for mental health specialties were ~2x higher than for GPs. Claire suspected clinicians were pre-booking follow-ups, inflating cancellation numbers. Dot verified: it analyzed rebooking flows and timing, confirming the elevated rate was structural, not a leak. Useful to know, not urgent to fix.
Gynecology Growth Trajectory
Dot highlighted unusually strong growth in gynecology consultations. While not directly actionable, it raised important questions: what drove this growth? Partnerships, influencer marketing, app positioning? Understanding what worked could be replicated across specialties.
The 40+ Patient Drop-Off: A Missing Symptom Worth €800K/Year
Patients aged 40+ were converting at a rate 7 percentage points lower (~10% relative) than younger patients at one specific step: viewing the symptom list. They arrived at the booking flow but didn't find what they needed.
Dot dug deeper: among 40+ patients who selected "other health inquiries" (the catch-all option), more than 40% were diagnosed with musculoskeletal (MSK) pain. The symptom was simply absent from the list.
The tip of the iceberg: patients who selected "other" despite not finding their symptom. The hidden part: all the patients who simply dropped off without clicking anything.
"On the symptom step, something's happening with how patients perceive what we can help them with. We could have done this analysis before. But we could also cut the funnel by gender, by cohort, by so many things. We just weren't doing it systematically."
Claire Bertrand, Data Insights Lead, KRY
Why It Worked
- Shared Language
- The revenue framework and metric glossary created a shared foundation. Dot didn't produce generic statistical findings. It reasoned using KRY's own growth model and business terminology.
- Transparent & Auditable
- Every recommendation backed by simple, inspectable data. Not a black box. Just good ideas based on clear datasets. Quickly convincing that there's no people-pleaser bias or number-tweaking.
- Business Semantics
- Dot understood that age isn't just a variable. It represents different healthcare needs. The semantic layer of the business made recommendations relevant and credible, not just statistically significant.
- Democratized Analysis
- Previously, exploring a hypothesis required Looker or SQL proficiency. Now anyone (ops, clinicians, PMs) can test an idea. The data team's formalized context becomes a shared asset, not trapped expertise.
The Core Shift
Before Dot, KRY's data team could test the most obvious hypotheses. With Dot, they can test every hypothesis, across every funnel step, every dimension, every segment, in the time it used to take to explore one. The problem was never the data or the framework. It was always a resource problem. Dot solved it.
"It's so great to think that someone with a different gut feeling, who had no tools to dig into that idea before, can now come up with their own business case. There's no barrier of how good you are at Looker. That's bringing more democracy to the process."
Claire Bertrand, Data Insights Lead, KRY
The Setup
KRY already had clean, structured data in their warehouse and a well-defined analytical framework. The setup was about connecting Dot to what already existed, not building anything new.
- 01.
- Connect the Semantic LayerDot connected to KRY's LookML, the semantic layer behind Looker that already held the funnel data and consultation events.
- 02.
- Define Data SourcesSelected which Looker explorers would serve as data sources for the analysis.
- 03.
- Sync Context & DocumentationPulled metric definitions, business term glossary, and explorer documentation. All the context behind doing analytically sound work.
- 04.
- Provide the Analytical FrameworkFed Dot the revenue engine methodology, the team's analytical playbook for understanding what drives growth.
- 05.
- Ask the QuestionOne open-ended prompt: "Where are the growth opportunities for France?" Dot handled the rest.
The Numbers
- €800,000/year
- Revenue opportunity identified from a single open-ended question
- ~10 min
- To identify, analyze, and size the opportunity
- 20+
- Hypotheses tested in parallel before surfacing the top 3
- 1-2 days
- Total setup time to go from zero to systematic exploration
- 100x faster
- Hypothesis testing compared to manual analysis
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