Retailflow Data Analysis

AI-Driven Business Innovation Masterclass

RetailFlow Data Analysis

Using AI to Analyse Store Performance Data

Dataset: retailflow-mock-data.csv

Use this RetailFlow store performance data to practice AI-assisted data analysis during Exercise 2.

About the Dataset

What it contains: - 45 RetailFlow stores across Australia - 14 performance metrics per store - Real patterns and correlations to discover

Metrics included: - Store location and demographics (State, Region, Size, Age) - Financial performance (Revenue, Costs, Profit Margin) - Customer metrics (Count, Average Transaction, Satisfaction) - Digital capability (Online Orders %, AI Chatbot adoption) - Operations (Employee Count, Inventory Turnover)

How to Use This Data

Option 1: Excel with Copilot

  1. Download the CSV file
  2. Open in Excel Online
  3. Use Copilot to analyse

Option 2: Google Sheets with Gemini

  1. Upload CSV to Google Drive
  2. Open in Google Sheets
  3. Use Gemini to analyse

Option 3: ChatGPT / Claude

  1. Upload CSV file directly
  2. Use Code Interpreter (ChatGPT Plus) or Claude’s analysis features

Suggested Analysis Prompts

Discovery: Find Patterns

Analyse this RetailFlow store performance data.

What are the top 3 insights or patterns you see?
Focus on what drives revenue and customer satisfaction.

What AI should find: - Stores with AI chatbots perform better - Older stores underperform newer stores - Online order % strongly correlates with revenue

Comparison: Chatbot Impact

Compare stores WITH AI chatbot vs. WITHOUT.

Show me the difference in:
- Customer satisfaction
- Online orders %
- Revenue

Create a comparison table.

Expected insight: Stores with chatbots have ~15% higher satisfaction, ~50% more online orders, and ~45% higher revenue.

Strategic: Investment Prioritization

Which 10 stores should get the AI chatbot investment next?

Prioritize by:
- Currently low online order %
- Decent revenue base
- Potential for improvement

Create a ranked list with rationale.

What AI should do: Identify stores with good fundamentals but lacking digital capability.

Visualization: Create Charts

Create 3 charts:

1. Bar chart: Average revenue by region
2. Scatter plot: Store age vs. customer satisfaction
3. Column chart: Chatbot impact comparison (with vs. without)
   for satisfaction, online %, and revenue

Make them presentation-ready.

Advanced: Predictive Analysis

Based on stores with chatbots, predict the revenue impact if we
roll out to all stores without chatbots.

Show me:
- Projected revenue increase
- ROI calculation
- Payback period

Strategic: Portfolio Optimization

We have $2M budget. Chatbot deployment costs $525K per store.

Which stores should we prioritize to maximize:
1. Total revenue impact
2. Customer satisfaction improvement
3. Strategic market coverage

Give me 3 different portfolio recommendations and explain the trade-offs.

Key Patterns to Discover

Pattern 1: AI Chatbot Works ⭐

Stores with AI chatbots show measurably better performance across multiple metrics. This validates the chatbot investment proposal in Dragon’s Den.

Pattern 2: Legacy Store Problem

Older stores (15+ years) significantly underperform. They may need modernization or AI investment to catch up.

Pattern 3: Digital Drives Growth

Online order percentage is the strongest predictor of overall store performance. Digital capability matters.

Pattern 4: Regional Gaps

East region (NSW, VIC, QLD) outperforms West and South. This suggests regional strategy differences.

Pattern 5: Size vs. Efficiency

Larger stores generate more revenue but don’t necessarily have better margins or satisfaction. Operational excellence matters more than size.

Questions to Explore

Business Questions:

  • Which stores should get AI investments first?
  • What’s the ROI of chatbot based on actual pilot data?
  • Should we invest in old stores or focus on new ones?
  • How does digital capability affect performance?

Analytical Questions:

  • What’s the correlation between online orders % and revenue?
  • Do larger stores have better margins?
  • Which region has the biggest performance gap?
  • What predicts customer satisfaction best?

Investment Questions:

  • If chatbot increases revenue by $177K/year and costs $525K, what’s the payback?
  • How many stores can we deploy to with $2M budget?
  • Which stores have the best fundamentals for AI investment?

Connection to Dragon’s Den

This dataset supports the AI Customer Service Chatbot proposal ($525K, 119% ROI):

  • 12 stores in the data have chatbots (pilot program)
  • Clear performance lift across satisfaction, online orders, and revenue
  • Provides evidence for the business case you’re evaluating

Use this data during Dragon’s Den deliberation to: - Validate the chatbot ROI projections - Identify which stores should get chatbot next - Inform your investment decision with real data

What You’ll Learn

About AI + Data Analysis:

  • AI can find patterns in minutes that take humans hours
  • AI excels at comparisons, correlations, and visualizations
  • You provide questions; AI provides speed and pattern detection

About Strategic Decisions:

  • Data informs decisions but doesn’t make them
  • Same data can support different strategic conclusions
  • Context and business judgment still required

Key Insight:

“AI shows you WHAT the data says. YOU decide WHAT IT MEANS and WHAT TO DO about it.

This is augmentation, not replacement.”

Tips for Effective Analysis

1. Start Broad, Then Narrow

  • Begin with: “What patterns do you see?”
  • Then drill down: “Tell me more about Pattern #2”

2. Ask for Structure

  • “Create a comparison table”
  • “Give me 3 options with pros/cons”
  • “Rank these by priority”

3. Ground AI in Data

  • “Only use data from this spreadsheet”
  • “Show me the calculation”
  • “Which stores specifically?”

4. Iterate

  • First answer reveals new questions
  • Follow up: “What if we only had $1M budget?”
  • Challenge: “What risks does this approach have?”

5. Visualize

  • Charts reveal patterns text misses
  • Ask for presentation-ready visuals
  • Use charts in your Dragon’s Den deliberation

Exercise Workflow

During Exercise 2 (optional advanced activity):

  1. Download the dataset (CSV file)
  2. Upload to your AI tool (Excel Copilot, ChatGPT, Claude, Gemini)
  3. Run 3-5 analysis prompts (from suggestions above)
  4. Capture key insights relevant to Dragon’s Den
  5. Share findings with your table during discussion

Time: 15-20 minutes if you want to explore this dataset

Files

Additional Resources

Ready to analyse? Download the CSV and start asking AI questions! 📊