data-storytelling

name	data-storytelling
description	Transform raw data into persuasive business narratives using Setup→Conflict→Resolution framework. Templates for problem-solution, trend analysis, and comparison stories. Use when presenting data to stakeholders or building data-driven reports.
group	domain
triggers	["данные в презентацию","бизнес отчёт","data storytelling","нарратив из данных","инсайты для стейкхолдеров"]
output	structured data narrative with headline, context, insight, recommendation, and call-to-action
calls	[]

Data Storytelling

Transform raw data into persuasive narratives that drive business decisions.

When to Activate

• Preparing data for stakeholders / executives
• Building reports or slide decks from analytics
• Writing data-driven recommendations
• Turning dashboards into actionable insights

Three Pillars

Pillar	Role	Without it...
Data	Evidence — numbers and trends	Opinions, not facts
Narrative	Meaning — context and implications	Numbers without story
Visuals	Clarity — charts and highlights	Complex tables nobody reads

Core Framework: Setup → Conflict → Resolution

Setup:      Context — where are we? What do we track?
Conflict:   Problem or opportunity — what changed? What's at risk?
Resolution: Recommendation — what do we do about it?

Story Templates

1. Problem–Solution

HEADLINE: We're Losing $2.4M to Preventable Churn

SETUP:     Monthly churn has held at ~2.1% for 8 months.
           120 customers cancel monthly. LTV = $20K avg.

CONFLICT:  Analysis shows 68% of churners share a pattern:
           — No action in first 14 days after signup
           — Never used core Feature X
           Exit surveys: "Didn't understand the value"

RESOLUTION: Onboarding intervention for at-risk users (day 7 trigger):
           — Projected churn reduction: 35% (based on cohort A/B data)
           — Revenue impact: +$840K/year
           — Implementation cost: ~80 eng hours

CALL TO ACTION: Approve Q2 onboarding sprint. Review cohort data →

2. Trend Analysis

HEADLINE: Mobile Revenue Up 340% — Desktop Still Declining

SETUP:     18 months ago, mobile = 12% of revenue.
           Today: mobile = 41%, desktop = 59%.

CONFLICT:  Mobile conversion rate (2.8%) still lags desktop (4.1%).
           If mobile matched desktop conversion → +$1.1M ARR.

RESOLUTION: Mobile checkout redesign (UX audit shows 3 friction points).
           Q3 sprint → target mobile CVR 3.5% by EOY.

3. Comparison (Option Evaluation)

HEADLINE: Option B Delivers 2.3× ROI vs. Option A

METRIC        OPTION A    OPTION B    WEIGHT
Cost          $120K       $85K        30%
Time to value 6 months    3 months    25%
Scalability   Medium      High        25%
Risk          Low         Medium      20%

WEIGHTED SCORE: A = 62pts / B = 78pts

RECOMMENDATION: Proceed with Option B, mitigate risk via staged rollout.

Presentation Arc

1. HEADLINE       — Specific + business impact (not "Q3 Report")
2. CONTEXT        — Why this matters, scope, timeframe
3. DISCOVERY      — What the data shows (key findings, 2–3 max)
4. ANALYSIS       — Why it happened (root cause, contributing factors)
5. RECOMMENDATION — What to do (specific, actionable)
6. IMPACT         — Expected outcome, metrics to track
7. CALL TO ACTION — Clear next step, owner, deadline

Headline Formula

"[Metric] + [Impact] + [Context]"

Bad headline	Good headline
"Sales Report Q3"	"Q3 Sales Up 23% — Enterprise Segment Drove 80% of Growth"
"Churn Analysis"	"Churn Costs $2.4M/Year — 3-Week Fix Can Recover $840K"
"User Engagement"	"DAU/MAU Dropped to 18% — Feature X Adoption is the Leading Indicator"

Visualization Principles

Progressive Reveal

Build complexity across slides — don't dump all data at once:

Slide 1: Headline metric only
Slide 2: Add trend line
Slide 3: Add cohort breakdown
Slide 4: Root cause annotation
Slide 5: Recommendation + projected impact

Annotation Over Decoration

❌ Pretty chart with no callouts
✅ Chart with annotation at inflection point:
   "← Launched Feature X here. Retention +12pp in 30 days."

Before/After Contrast

Show the gap visually — don't leave audience to calculate it:

BEFORE: Conversion 2.1% → AFTER intervention: 3.4%
Impact: +$840K ARR (highlighted as large number, not hidden in table)

Key Principles

1. Start with the "so what" — lead with insight, not methodology
2. Curate ruthlessly — 3 key findings beat 12 mediocre ones
3. Quantify the impact — "$840K" > "significant revenue opportunity"
4. End with a decision — every presentation needs a clear ask
5. Use accessible language — no jargon for executive audiences