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case study — learning ecosystem design
They didn't need more training. They needed us to ask better questions.
A B2B SaaS company brought me in to fix their onboarding. High completion. High satisfaction. Customers still leaving. What we found beneath the surface changed everything about how we designed the solution.
What we achieved
The numbers looked fine. Something was wrong.
I was brought in to fix the training. Before touching a single piece of content, I spent three weeks asking a different question: what do we actually know about why customers leave?
91% completion. 4.2/5 satisfaction. 68% churn. That's not a content problem — that's a mystery. And I was very, very curious.
Learning is working
91% module completion. 4.2/5 confidence score. Every learning metric pointed up.
Customers are leaving
68% churn within 18 months. Feature adoption dropping month on month. Support tickets rising.
High completion. High confidence. High churn.
The training data and the business data were telling completely different stories. That disconnect became the central question of the entire project.
Curiosity led to an uncomfortable truth.
I interviewed Product (n=4) and Customer Success (n=6) separately, asking the same question: "In your own words, why do customers leave?" I didn't share the answers between teams. The divergence was striking.
Both teams were working hard. Neither was wrong exactly. They'd just never been in the same room — and I started thinking that gap might be the problem itself.
Recommended: the "Product team said vs Customer Success said" two-column visual
Suggested size: 760 × 380px
The confidence trap
Users scored 4.2/5 confidence — but had only ever practised in clean demo environments. When they hit their own messy real-world data in production, they froze. They didn't ask for help. They quietly stopped using the feature. The decision to churn was made weeks before any renewal conversation.
The translation gap
CS heard "it's too complicated" and logged it as product feedback. Product simplified the UI. But customers meant: "I don't know which of the 12 ways to do this fits my use case." A decision-support problem dressed as a UI complaint — invisible because no one was translating between teams.
Before we design anything, we need to be in the same room.
No eLearning was going to fix a problem two teams couldn't agree on. I recommended a full-day facilitated workshop before a single piece of content was scoped.
I had to pitch this internally. "You want to run a day-long session before building anything?" Yes. Designing without alignment isn't faster — it's just confidently wrong.
Three things came out of the room that nobody expected:
The friction tax
- An 8-minute task was taking 40+ mins — customers were depleted before exploring anything else.
- Churn wasn't frustration. It was exhaustion.
Nobody knew how to have the hard conversation
- CS Managers were avoiding capability conversations — they feared it would sound like blame.
- No one had ever taught them how to name a knowledge gap kindly.
A shared tagging taxonomy
- Shared tags across CS and Product's existing ticketing system — same words, same definitions.
- No new meetings. No new tools. Just a shared language.
It was never just a training problem. Here's what it actually was.
The workshop, interviews, and usage data together produced one clear picture. This was four problems layered on top of each other — and only one was addressable with learning content.
Not a course. An ecosystem.
The eLearning was about 20% of the solution. The rest was systems, process, and one very human conversation that nobody had planned for.
Once we had the journey map, I kept asking: where does a standalone eLearning actually show up in this picture? The honest answer: only at the very start. The rest of the journey was completely unaddressed.
Contextual in-app guidance
Tooltips and decision prompts at the exact moments users freeze in production. Triggered by behaviour, not a timer.
90-second decision job aids
"Which path is right for my data?" — short guides for the 3 most common decision points. In the product, not the LMS.
Real-data scenario practice
Rebuilt around anonymised customer data profiles — not clean demo environments. Branching, Storyline 360.
Manager conversation guide
Signals to help managers spot the Confidence Trap early — what to watch for at weeks 2, 4, and 8.
Shared tagging taxonomy
Same words, same definitions across CS and Product's ticketing system. No new meetings. Just a shared language.
The difficult conversation workshop
A short facilitated session for CS Managers: how to name a knowledge gap without blame. Not a course. A conversation that changed the renewal dynamic.
Three versions. Three theories about what would work.
The scenario module went through three distinct iterations, each tested with a cohort of 8–12 newly onboarded customers before scaling. Each failure taught me something the data alone couldn't.
V1 made things worse. I'm putting that up front. Bad data from a test is still good data — it told me exactly what the real problem was.
More content, better structure
Extended the module from 20 to 45 minutes. Added two new workflow sections and a knowledge check every 10 minutes.
Result: completion dropped 91% → 74%. "Overwhelmed before I'd even opened the product." Feature adoption at day 30: 34%. Unchanged from baseline.
↺ Pivoted — more content made things worse → the length wasn't the problem. the context was.Branching scenario with demo data
Built a branching scenario in Articulate Storyline. Three diverging paths based on data type. Users loved the format.
Result: engagement up significantly. But users still froze when they hit their own data in production. The demo data was too clean. Adoption at day 30: 41%.
◎ Partial success — right format, wrong data → the scenario worked. the unreality didn't.Real-data profiles + contextual in-app prompts
Worked with Product to create anonymised profiles from actual customer data types. Combined with contextual prompts at the exact in-product decision points. The scenario and the product now spoke the same language.
Result: feature adoption at day 30 jumped to 67%. Users navigated independently when they hit problems. Support tickets down 34%.
✓ Validated — shipped to full cohort → realistic context changed everything.What I'd do differently
Run the workshop in week one
The misalignment between Product and CS was obvious from the first interview. I'd propose it on day one, not after three weeks of discovery.
Instrument V1 from the start
Completion rates told me it wasn't working. xAPI click-path data would have told me where and why — much faster.
Involve CS in content review
Their customer language would have made the V2 scenario feel more real from the first draft.
Build the feedback loop first
The shared tagging taxonomy was the most structurally important change. It should have been day one, not the final deliverable.
Take this prototype for a spin.
The V2 branching scenario is built and available — the iteration that proved the format before I got the data right. It gives you a feel for the decision architecture, the branching logic, and how I structure scenario-based learning for a technically complex product.