Case Study
A branching scenario that lets account executives practice customer discovery calls before going live with a new product.
The company was launching a new enterprise network solution for mid-market business customers. Over 2,200 account executives needed to understand the product positioning, handle common objections, and conduct effective discovery calls within six weeks of launch.
Previous product launches relied on slide-based webinars and PDF reference guides. Post-launch data showed reps were defaulting to feature-dumping on calls instead of asking discovery questions, leading to low conversion rates and support escalations from mispositioned deals.
"We need reps to practice having real conversations, not just memorize talking points. The last launch training looked good on paper but didn't change how anyone actually sold."
I spent the first week gathering information from multiple sources. I reviewed call recordings from a recent product launch, sat in on three live customer calls, and interviewed six top-performing reps and their managers. I also reviewed the post-launch survey data from the previous training.
The data told a clear story: 92% of reps could pass the product knowledge quiz, but only 34% felt confident handling a live discovery call.
The gap wasn't knowledge — it was application. Reps could list features and pricing tiers, but they struggled with reading customer signals, handling budget objections without discounting, and closing with a concrete next step.
The biggest failure point was the first two minutes. Call recordings showed reps who led with product features had a 60% lower chance of getting a follow-up meeting compared to reps who started with discovery questions.
Based on the assessment, I recommended a branching scenario simulation over traditional eLearning. The training needed to put reps in a realistic conversation, force them to make judgment calls, and show the consequences of those choices — not quiz them on facts they already knew.
I mapped the customer journey of a real mid-market discovery call and identified 5 critical decision points where rep behavior most affected outcomes. At each point, I wrote 3 response options — one best practice, one acceptable but flawed, and one common mistake — all based on patterns from actual call recordings.
Each choice triggers specific coaching feedback explaining why it worked or didn't, tied to the consultative selling principles the team was already familiar with. The final design uses a linear-branching model: learners move through the same 5 scenes in order, but their choices affect their feedback and final score.
The production version was built in Articulate Storyline 360, published as SCORM 1.2, and hosted in the company LMS. I built an HTML prototype first to get rapid feedback from stakeholders before investing in the full Storyline build.
AI tools were used to accelerate scripting. I used AI to generate initial dialogue drafts based on call recording transcripts, which I then edited and validated with SMEs. I also used AI to draft the coaching feedback for each choice, then rewrote it to match the team's voice and selling framework. This cut the scripting phase from an estimated 8 days to 3.
The simulation was deployed two weeks before the product launch. Completion was required for all account executives in the target segment.
Post-launch call reviews showed a noticeable shift in rep behavior — more reps were opening calls with discovery questions instead of feature pitches, and objection handling improved across the board. Two additional simulations were requested for the next quarter's launches.
If I built this again, I'd add adaptive difficulty — reps who score well on the first attempt would get a harder version with a more resistant customer persona. I'd also build in spaced repetition: a shorter follow-up scenario two weeks after launch to reinforce skills before they fade.
The AI-assisted scripting saved significant time, but AI-generated dialogue tends to be too polished. Real customers don't speak in clean paragraphs. In future projects, I'd use the AI drafts as a starting point and then deliberately rough them up to sound more natural.
Experience the branching scenario simulation — 8 minutes, 5 decision points.
Launch Simulation →