data-storytelling – skill Dữ liệu & Khoa học cho agent AI

Data Storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

Do not use this skill when

• The task is unrelated to data storytelling
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• Use the frameworks and examples below; verify calculations against the supplied source data.

Use this skill when

• Presenting analytics to executives
• Creating quarterly business reviews
• Building investor presentations
• Writing data-driven reports
• Communicating insights to non-technical audiences
• Making recommendations based on data

Core Concepts

1. Story Structure

Setup → Conflict → Resolution

Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations

2. Narrative Arc

1. Hook: Grab attention with surprising insight
2. Context: Establish the baseline
3. Rising Action: Build through data points
4. Climax: The key insight
5. Resolution: Recommendations
6. Call to Action: Next steps

3. Three Pillars

| Pillar | Purpose | Components |

| ------------- | -------- | -------------------------------- |

| Data | Evidence | Numbers, trends, comparisons |

| Narrative | Meaning | Context, causation, implications |

| Visuals | Clarity | Charts, diagrams, highlights |

Story Frameworks

The numeric stories below are synthetic teaching examples, not observed customer outcomes or benchmarks. Replace their figures with verified input, show assumptions, and distinguish association from causation. Recalculate totals before recommending spending or forecasting impact.

Framework 1: The Problem-Solution Story

# Customer Churn Analysis

## The Hook

"We're losing $2.4M annually to preventable churn."

## The Context

- Current churn rate: 8.5% (industry average: 5%)
- Average customer lifetime value: $4,800
- 500 customers churned last quarter

## The Problem

Analysis of churned customers reveals a pattern:

- 73% churned within first 90 days
- Common factor: < 3 support interactions
- Low feature adoption in first month

## The Insight

[Show engagement curve visualization]
Customers who don't engage in the first 14 days
are 4x more likely to churn.

## The Solution

1. Implement 14-day onboarding sequence
2. Proactive outreach at day 7
3. Feature adoption tracking

## Expected Impact

- Reduce early churn by 40%
- Save $960K annually
- Payback period: 3 months

## Call to Action

Approve $50K budget for onboarding automation.

Framework 2: The Trend Story

# Q4 Performance Analysis

## Where We Started

Q3 ended with $1.2M MRR, 15% below target.
Team morale was low after missed goals.

## What Changed

[Timeline visualization]

- Oct: Launched self-serve pricing
- Nov: Reduced friction in signup
- Dec: Added customer success calls

## The Transformation

[Before/after comparison chart]
| Metric | Q3 | Q4 | Change |
|----------------|--------|--------|--------|
| Trial → Paid | 8% | 15% | +87% |
| Time to Value | 14 days| 5 days | -64% |
| Expansion Rate | 2% | 8% | +300% |

## Key Insight

Self-serve + high-touch creates compound growth.
Customers who self-serve AND get a success call
have 3x higher expansion rate.

## Going Forward

Double down on hybrid model.
Target: $1.8M MRR by Q2.

Framework 3: The Comparison Story

# Market Opportunity Analysis

## The Question

Should we expand into EMEA or APAC first?

## The Comparison

[Side-by-side market analysis]

### EMEA

- Market size: $4.2B
- Growth rate: 8%
- Competition: High
- Regulatory: Complex (GDPR)
- Language: Multiple

### APAC

- Market size: $3.8B
- Growth rate: 15%
- Competition: Moderate
- Regulatory: Varied
- Language: Multiple

## The Analysis

[Weighted scoring matrix visualization]

| Factor      | Weight | EMEA Score | APAC Score |
| ----------- | ------ | ---------- | ---------- |
| Market Size | 25%    | 5          | 4          |
| Growth      | 30%    | 3          | 5          |
| Competition | 20%    | 2          | 4          |
| Ease        | 25%    | 2          | 3          |
| **Total**   |        | **3.05**   | **4.05**   |

## The Recommendation

APAC first. Higher growth, less competition.
Start with Singapore hub (English, business-friendly).
Enter EMEA in Year 2 with localization ready.

## Risk Mitigation

- Timezone coverage: Hire 24/7 support
- Cultural fit: Local partnerships
- Payment: Multi-currency from day 1

Visualization Techniques

Technique 1: Progressive Reveal

Start simple, add layers:

Slide 1: "Revenue is growing" [single line chart]
Slide 2: "But growth is slowing" [add growth rate overlay]
Slide 3: "Driven by one segment" [add segment breakdown]
Slide 4: "Which is saturating" [add market share]
Slide 5: "We need new segments" [add opportunity zones]

Technique 2: Contrast and Compare

Before/After:
┌─────────────────┬─────────────────┐
│ BEFORE │ AFTER │
│ │ │
│ Process: 5 days│ Process: 1 day │
│ Errors: 15% │ Errors: 2% │
│ Cost: $50/unit │ Cost: $20/unit │
└─────────────────┴─────────────────┘

This/That (emphasize difference):
┌─────────────────────────────────────┐
│ CUSTOMER A vs B │
│ ┌──────────┐ ┌──────────┐ │
│ │ ████████ │ │ ██ │ │
│ │ $45,000 │ │ $8,000 │ │
│ │ LTV │ │ LTV │ │
│ └──────────┘ └──────────┘ │
│ Onboarded No onboarding │
└─────────────────────────────────────┘

Technique 3: Annotation and Highlight

import matplotlib.pyplot as plt
import pandas as pd

fig, ax = plt.subplots(figsize=(12, 6))

# Plot the main data
ax.plot(dates, revenue, linewidth=2, color='#2E86AB')

# Add annotation for key events
ax.annotate(
    'Product Launch\n+32% spike',
    xy=(launch_date, launch_revenue),
    xytext=(launch_date, launch_revenue * 1.2),
    fontsize=10,
    arrowprops=dict(arrowstyle='->', color='#E63946'),
    color='#E63946'
)

# Highlight a region
ax.axvspan(growth_start, growth_end, alpha=0.2, color='green',
           label='Growth Period')

# Add threshold line
ax.axhline(y=target, color='gray', linestyle='--',
           label=f'Target: ${target:,.0f}')

ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')
ax.legend()

Presentation Templates

Template 1: Executive Summary Slide

┌─────────────────────────────────────────────────────────────┐
│  KEY INSIGHT                                                │
│  ══════════════════════════════════════════════════════════│
│                                                             │
│  "Customers who complete onboarding in week 1              │
│   have 3x higher lifetime value"                           │
│                                                             │
├──────────────────────┬──────────────────────────────────────┤
│                      │                                      │
│  THE DATA            │  THE IMPLICATION                     │
│                      │                                      │
│  Week 1 completers:  │  ✓ Prioritize onboarding UX         │
│  • LTV: $4,500       │  ✓ Add day-1 success milestones     │
│  • Retention: 85%    │  ✓ Proactive week-1 outreach        │
│  • NPS: 72           │                                      │
│                      │  Investment: $75K                    │
│  Others:             │  Expected ROI: 8x                    │
│  • LTV: $1,500       │                                      │
│  • Retention: 45%    │                                      │
│  • NPS: 34           │                                      │
│                      │                                      │
└──────────────────────┴──────────────────────────────────────┘

Template 2: Data Story Flow

Slide 1: THE HEADLINE
"We can grow 40% faster by fixing onboarding"

Slide 2: THE CONTEXT
Current state metrics
Industry benchmarks
Gap analysis

Slide 3: THE DISCOVERY
What the data revealed
Surprising finding
Pattern identification

Slide 4: THE DEEP DIVE
Root cause analysis
Segment breakdowns
Statistical significance

Slide 5: THE RECOMMENDATION
Proposed actions
Resource requirements
Timeline

Slide 6: THE IMPACT
Expected outcomes
ROI calculation
Risk assessment

Slide 7: THE ASK
Specific request
Decision needed
Next steps

Template 3: One-Page Dashboard Story

# Monthly Business Review: January 2024

## THE HEADLINE

Revenue up 15% but CAC increasing faster than LTV

## KEY METRICS AT A GLANCE

┌────────┬────────┬────────┬────────┐
│ MRR │ NRR │ CAC │ LTV │
│ $125K │ 108% │ $450 │ $2,200 │
│ ▲15% │ ▲3% │ ▲22% │ ▲8% │
└────────┴────────┴────────┴────────┘

## WHAT'S WORKING

✓ Enterprise segment growing 25% MoM
✓ Referral program driving 30% of new logos
✓ Support satisfaction at all-time high (94%)

## WH

… (nội dung đầy đủ có trong gói tải về)