# Case-study skeleton

The ordered template, then a filled mini-example. Keep the whole piece 600–1,200 words.

## Front-matter (carries the legal state)

```yaml
---
customer: "<company name, or anonymized descriptor>"
industry: "<sector>"
region: "<market/geography>"
products: ["<product or module used>"]
consent: signed          # signed | pending | anonymized
approved: 2026-05-28     # date customer signed off on the exact quote
quote_source: "<file or link to interview/email the quote came from>"
---
```

## Ordered template

```text
# <RESULTS HEADLINE — one KPI, the single most important number>
   e.g. "Fraud losses cut to 0.10% in 8 weeks"

> Snapshot
> Industry: <sector>   Region: <market>   Products: <modules>
> KPIs: <metric 1>, <metric 2>, <metric 3>   Timeframe: <window>

## The challenge
<The measurable pain before. 1–2 short paragraphs, ideally with the
 customer's own framing and a baseline number. Make the buyer recognize
 their own problem.>

## The solution
<What was implemented and how — concrete and specific. Name the modules,
 the rollout, the integration. "Migrated 1,200 SKUs in two sprints," not
 "partnered closely.">

## The result
<The proof. Each result as a Context → Achievement → Relevance line, every
 number paired with a baseline and a timeframe.>
- <Context>: <Achievement (lift)> → <Relevance ($ or strategic value)>
- ...

> "<One specific claim tied to a number or a felt change.>"
> — <Full name>, <Title>, <Company>

## What's next
<One decision-stage CTA matched to the reader's stage:
 "See pricing", "Book a 20-min fit call", "Read the implementation guide".>
```

## Filled mini-example (real-shaped numbers)

```text
# Support tickets down 75% in 90 days

> Snapshot
> Industry: B2B SaaS (HR tech)   Region: Iberia   Products: Helpdesk AI, Knowledge Base
> KPIs: tickets 1,200/mo → 300/mo, CSAT 72 → 94, first-response 9h → 40min
> Timeframe: 90 days

## The challenge
By Q1 the support team was fielding 1,200 tickets a month with a 9-hour
first-response time. CSAT had slipped to 72, and two of five agents were
at risk of burnout. "We were drowning in repeat questions," the VP of
Operations said. Every new customer cohort made it worse.

## The solution
We deployed the Helpdesk AI deflection layer on top of a rebuilt Knowledge
Base — 140 articles re-tagged and connected to the in-app widget — across
two sprints. Tier-1 questions were auto-answered; anything unresolved
routed to a human with full context attached.

## The result
- Deflecting repeat tier-1 questions cut monthly tickets from 1,200 to 300
  (−75%) within 90 days → freeing two FTEs to move to onboarding.
- Faster routing dropped first-response time from 9 hours to 40 minutes
  → CSAT climbed from 72 to 94.
- Lower agent load → zero support-team attrition over the following two quarters.

> "We went from drowning in repeat questions to answering the hard ones in
> under an hour. My team stopped dreading Monday mornings."
> — María Soler, VP Operations, NorthPeak HR

## What's next
See how the deflection layer fits your stack — book a 20-minute fit call.
```

Notes on the example: the headline is the result, not the company; the snapshot lets a buyer self-qualify; every result line pairs before → after → window → value; the quote makes one claim tied to the felt change; the CTA matches a mid-funnel reader. Word count sits inside 600–1,200.
