Retail Analytics Dashboard
2025-07-122 turns4,270 charsgpt-4o
Summary
User wants to create a retail analytics dashboard using Plotly with complex diagrams like Sankey charts.
Messages
Dashboard
Examples here https://plotly.com/examples/dashboards/ and https://plotly.com/examples/predictive-analytics/
Should be able to tell a story. (Atleast 2-3 complex diagrams in it)
The level of complexity should be similar to these examples.
Sankey Diagram
Icicle chart
TreeMaps
Multi-level sunburst
Detailed heatmaps
Here's a **data storytelling dashboard project** that includes at least 2–3 complex Plotly diagrams and matches the complexity of the examples from the [Plotly dashboards](https://plotly.com/examples/dashboards/) and [predictive analytics](https://plotly.com/examples/predictive-analytics/) pages. This example combines:
- **A Sankey diagram** to show flow relationships
- **A Sunburst chart or Icicle chart** to show hierarchical breakdown
- **A Heatmap** for temporal/spatial/metric correlations
---
## 🎯 Dashboard Title: **"Customer Journey & Profitability in a Retail Chain"**
### 🧩 Storyline:
Track how customers move through the purchase funnel, how they contribute to different product category revenues, and discover what factors correlate most with profitability across locations and demographics.
---
## 📊 Visuals & Breakdown:
### **1. Sankey Diagram – Customer Journey Funnel**
- **Objective:** Visualize how users move from Ad Click → Website Visit → Add to Cart → Checkout → Repeat Purchase
- **Nodes:** Ad Source (Social, Email, SEO), Actions (Visit, Cart, Checkout, Return)
- **Flows:** Volume of users through each stage
```python
import plotly.graph_objects as go
fig = go.Figure(data=[go.Sankey(
node=dict(
pad=15,
thickness=20,
line=dict(color="black", width=0.5),
label=["Social Ads", "Email Campaign", "SEO", "Website Visit", "Add to Cart", "Checkout", "Repeat Purchase"],
color="blue"
),
link=dict(
source=[0,1,2,3,4,5],
target=[3,3,3,4,5,6],
value=[200,120,150,400,300,120]
))])
fig.update_layout(title_text="Customer Journey Funnel", font_size=10)
fig.show()
```
---
### **2. Multi-Level Sunburst – Revenue Breakdown by Region → Category → Product**
- **Objective:** Show how total revenue is distributed across geographic regions and drill down into product performance.
- Can be switched with an Icicle chart if you prefer vertical display.
```python
import plotly.express as px
import pandas as pd
df = pd.DataFrame([
["North America", "Electronics", "Phones", 50000],
["North America", "Electronics", "Laptops", 60000],
["North America", "Clothing", "Jeans", 20000],
["Europe", "Electronics", "Phones", 40000],
["Europe", "Clothing", "Shirts", 25000],
["Asia", "Electronics", "Phones", 45000],
["Asia", "Clothing", "Shoes", 22000]
], columns=['Region', 'Category', 'Product', 'Revenue'])
fig = px.sunburst(
df,
path=['Region', 'Category', 'Product'],
values='Revenue',
color='Region'
)
fig.update_layout(title="Revenue Breakdown by Region → Category → Product")
fig.show()
```
---
### **3. Heatmap – Profit Margin by Region & Customer Segment**
- **Objective:** Identify what region + customer segment combinations are most profitable
- X-axis: Region
- Y-axis: Customer Type (New, Returning, VIP, Discount Buyers)
```python
import plotly.express as px
import pandas as pd
data = {
"Region": ["NA", "NA", "NA", "EU", "EU", "EU", "AS", "AS", "AS"],
"Segment": ["New", "Returning", "VIP"] * 3,
"ProfitMargin": [0.12, 0.18, 0.25, 0.10, 0.14, 0.22, 0.11, 0.15, 0.20]
}
df = pd.DataFrame(data)
fig = px.density_heatmap(df, x="Region", y="Segment", z="ProfitMargin", color_continuous_scale="Viridis")
fig.update_layout(title="Profit Margin Heatmap by Region & Segment")
fig.show()
```
---
## 🚀 Optional Add-ons:
- Add **predictive regression plots** (e.g. using `px.scatter` with trendlines from `statsmodels`) to show profit projections
- Show **time series data** on sales and overlays for events (holidays, campaigns)
---
## 🛠️ Deployment Tips
- Use [Dash](https://dash.plotly.com) to make it interactive
- Include dropdowns for date ranges, filters for region/category
- Add callbacks to link sunburst selection → update Sankey flows or heatmap dynamically
---
Would you like the full **Dash app code** integrating all these components into one dashboard?
gpt-4o