Case Study: Global Sales Enablement Curriculum

Confidentiality note: this client is referred to generically per standing NDA practice. Every sample artifact below is recreated from scratch with a fictional company, persona, and product names ("Meridian Cloud ERP," "Unify Data Fabric," "ProfitLens Analytics," CFO "Maya Chen") to demonstrate the deliverable format and instructional pattern used—none of it is the client's actual content, product names, or data.

A — Analyze

The problem

A global software vendor sells an interconnected portfolio—multiple product lines that solve different pieces of the same customer problem. New and existing sellers were treating each product as a standalone pitch, leading with feature lists instead of connecting products to a customer’s actual business need. The training had to teach positioning judgment, not just product facts, across dozens of industry verticals with genuinely different buyers and regulatory contexts.

Standing production constraints set by the client: strict source fidelity (no inference beyond approved source material), flagged gaps routed back to subject-matter experts rather than filled in, and formatting that had to survive unmodified inside the client’s LMS.

Sample artifact — Needs Analysis Summary. Recreated with a fictional company; illustrates the analysis format, not the client's actual findings.

Discovery interviews · Sales Enablement · Illustrative example

Needs Analysis Summary (excerpt)

Observed signal	Root cause	Performance gap	Recommended focus
Sellers default to a product demo on the first call	No shared framework for connecting a capability to a stated customer need	Deals stall in early discovery	Teach a needs-first discovery pattern before any product training
Reps treat three product lines as unrelated pitches	Products are taught in isolated, siloed modules	Missed cross-sell opportunities	One persona-driven customer story connecting all three products
New hires ramp slowly on vertical-specific positioning	Generic onboarding with no industry context	Extended time-to-productivity	Industry-vertical scenario library instead of one-size-fits-all training

D — Design

The instructional pattern

Each unit follows a repeatable structure: introduce a single fictional customer persona facing a realistic, evolving business challenge; introduce one product at a time only when the story creates a need for it; then step back and show how all the products fit one connected value story. The same "customer need → relevant capability → business value" scaffold repeats across every product so sellers build a transferable mental model instead of memorizing a new structure per product.

Sample artifact — Objective-to-Assessment Map. Illustrative example of how objectives were mapped before any content was written.

Design phase · Illustrative example

Learning objective	Bloom’s level	Assessment method	Delivery modality
Identify which product answers a stated customer need	Understand / Apply	Scenario-matching check	Self-paced eLearning
Position a capability in customer language, not feature language	Apply / Analyze	Role-play, scored by manager rubric	Live coaching session
Synthesize all three products into one connected value story	Analyze / Evaluate	Situational-judgment exam item	Certification exam

D — Develop

Production at scale

Storyboards were produced as structured Word documents so subject-matter experts and instructional reviewers could work in a familiar format, then converted to learner-facing, LMS-compatible layouts. A three-step discovery framework (listen for a signal, probe the operational impact, connect it to the right capability) is taught once and reinforced with a field-ready reference table sellers can use live on a call—a genuine job aid, not just review content.

Sample artifact — Discovery job aid, recreated with a fictional persona and product names.

Field reference · Illustrative example

Key Terms

Term	What it means
Meridian Cloud ERP	Core finance and operations platform running on standardized, cloud-based best practices.
Unify Data Fabric	Connects data across the platform and third-party systems while preserving business context.
ProfitLens Analytics	Flexible calculation engine for complex profitability and allocation modeling beyond standard ERP.

Customer situation: Maya Chen, CFO at a regional retail bank, needs to modernize a heavily customized ERP environment while her team still reconciles profitability models by hand every month.

What You Hear → Think → Explore

What you hear	Think	Explore
“Upgrades are painful.” “We have too much customization.”	Meridian Cloud ERP	Modernization, standardization, continuous innovation
“Our data is fragmented across systems.”	Unify Data Fabric	Data quality, governance, analytics readiness
“Our spreadsheet models are hard to reconcile.”	ProfitLens Analytics	Calculation complexity, traceability, simulation

Assessment items are written at the application/analysis level—situational judgment scenarios rather than recall questions—matching the audience of experienced sellers who need decision practice, not vocabulary drills.

Sample artifact — Storyline branching scenario, rebuilt for illustration. The client retained the original .story file, so this recreates the slide and trigger logic from memory to show the authoring pattern—not a screenshot of the real file. Persona and product names are fictionalized, consistent with the rest of this case study.

This is the Cadence Framework’s Bridge beat in practice: the discovery framework taught earlier (listen → probe → connect) isn’t just described—it’s tested in a branching call scenario, with the trigger logic below showing exactly how a seller’s word choice routes to different coaching feedback.

GlobalSalesEnablement_Discovery.story — Slide 3.2

3.1

3.2

3.3

3.4

Scenario 3.2 · Bridge Cadence — scenario-based transfer

Discovery Call: Maya Chen, CFO

“We already invested in Meridian last year. I don’t see why we’d need Unify Data Fabric on top of it right now.”

What do you say next?

AWalk through Unify Data Fabric’s feature list to show everything it adds.

BAsk what happens today when her team needs a number that lives in two systems.

Triggers — Slide 3.2

1Show layer “Coaching: Strong Connect” — when the user clicks Button B

2Adjust variable DiscoveryScore += 10 — when the user clicks Button B

3Show layer “Coaching: Weak Connect” — when the user clicks Button A

4Show layer “Try Again” — when the user clicks Button A, if Attempts < 2

5Jump to slide 3.3 “Apply: Full Value Story” — when the timeline ends on layer “Coaching: Strong Connect”

Self-directed prototype · built independently, not part of the client engagement

Extending the pattern: a predictive consequence simulation

The discovery framework above (listen → probe → connect) is tested in the client build with a single graded decision. To push the format further, I built a companion prototype on my own time in Articulate Storyline: a three-decision discovery-call simulation where nothing is scored as simply right or wrong. Instead, each choice accumulates toward a single predicted business outcome, and the closing screen explains that prediction back to the learner in their own words—not a canned "correct/incorrect" message.

The screens below are real screenshots from the working .story file—not a recreation—since this is my own scenario (a fictional "Meridian Client" account) rather than client-owned content.

Storyline slide 1.1: a scenario-briefing screen introducing Derek Osei, a skeptical VP of Supply Chain Operations, followed by the P3 AI Coach asking the learner's name before the call begins. — **Scenario briefing (1.1).** Sets up the stakes before any decision point, then asks for the learner's name—captured into a `LearnerName` text variable used later for personalized coaching feedback.

Storyline slide 1.2 showing a discovery-call decision with two response options, and the Triggers panel showing each hotspot setting a Decision1Choice variable. — **Decision 1 of 3, with the Triggers panel open.** Each response is a hotspot layered over the visible option card; clicking one writes a plain-language paraphrase of that choice into `Decision1Choice`.

Variable architecture

Four variables carry the whole interaction:

Variable	Type	Purpose
`LearnerName`	Text	Captured once on the briefing screen; referenced live (Storyline’s Insert → Reference field, not typed text) in the coach’s dialogue so feedback reads as addressed to that person by name.
`Decision1Choice` / `2` / `3`	Text	Each stores a short paraphrase of whichever option the learner picked, then is referenced back verbatim in a "Your Responses" recap on the closing screen—so the summary reflects the learner’s own path, not a generic report.
`DiscoveryScore`	Number	The scoring engine. The stronger, discovery-first option on each decision adds 10 points; the weaker, pitch-first option adds 0—producing a 0/10/20/30 range across the three decisions.

Branching & scoring logic

The three decision slides don’t brand any single option as "wrong" on-screen—both read as plausible things a seller might say. Underneath, each hotspot fires two triggers in a fixed order: first adjust that decision’s Choice variable and add to DiscoveryScore, then, on the final decision, evaluate the accumulated score to route to one of three outcome endings—Disrupted (0–9), Unchanged (10–19), or Enhanced (20–30)—so the ending reflects the compounding effect of all three choices, not just the last one.

Storyline outcome slide reading Supply Chain Resilience: Enhanced, with a recap of all three decisions, an icon-based supplier-to-client flow diagram, and a personalized coaching message referencing the learner's name. — **Enhanced outcome (1.5).** The "Your Responses" cards and the coach’s closing line both pull live from the Choice and `LearnerName` variables set earlier—nothing on this screen is hand-typed per learner.

Storyline detail worth calling out: adjusting a variable and evaluating it are two separate trigger rows, and Storyline runs a slide’s triggers top-to-bottom—so the score has to be written before the trigger that branches on it, or the branch checks a stale value.

Planned enhancement: an animated network visualization

The flow diagram on the outcome screen (Supplier → Warehouse → Distribution → Client) currently ships as static icons and connector lines, built natively in Storyline. The next iteration replaces that connector art with a Storyline Web Object—a small, self-contained HTML/JS page embedded directly on the slide—built with GSAP to animate a signal pulsing along the line in real time as the prediction resolves. Because a Web Object is just an embedded page, it drops in without touching any of the variable or trigger architecture above.

I — Implement

Rollout

Delivered across the client’s internal enablement platform to a global seller population spanning 21+ industry verticals, in phased waves rather than a single global launch—giving each vertical’s enablement leads time to localize examples before full rollout.

Sample artifact — Rollout plan, illustrative structure.

Wave	Audience	Channel	Support
1	Vertical leads (initial industry)	Live virtual cohort	Manager talking points & FAQ
2	Full seller population, that vertical	Self-paced LMS module	Peer office hours
3	Additional verticals, phased	Train-the-trainer	Regional enablement leads certified to deliver locally

E — Evaluate

Outcome

The approach was recognized internally as the client’s model for scalable, repeatable industry training—used as the pattern for extending sales enablement curriculum into additional verticals. The client’s overall learning ecosystem reached 3.4M active learners in 2025.

Sample artifact — certification assessment item, illustrative example. Shows the situational-judgment format used at certification, not an actual exam question.

Certification assessment · Analysis-level item · Illustrative example

A regional bank CFO says, “We already have Meridian Cloud ERP and a BI tool. Why would we need ProfitLens?” Which response best reflects the product positioning taught in this unit?

A	ProfitLens replaces both existing products for finance.
B	ProfitLens fills the complex-calculation and profitability-modeling role; the BI tool focuses on aggregated, forward-looking planning—the two work together. (Correct)
C	ProfitLens is only needed if the bank lacks a data warehouse.

Tools & methods

Needs analysis Objective-to-assessment mapping Storyboarding (Word) Articulate Storyline (branching & variables) Persona-driven scenario design Scenario-based assessment LMS-compatible formatting Phased rollout planning

Global Sales Enablement Curriculum Across 21+ Industry Verticals