1. Customer Segmentation with RFM

Goal: Classify customers based on Recency, Frequency, and Monetary value to target marketing campaigns effectively.

Data Needed:

• Customer ID
• Last purchase date
• Number of purchases
• Total spend (Dataset source: Kaggle E-commerce Dataset)

Steps:

1. ETL: Clean missing values, remove duplicates.

2. Calculate R, F, M Scores:

   • Recency = Days since last purchase
   • Frequency = Total orders placed
   • Monetary = Total amount spent

3. Assign scores (1–5) for each metric and combine.

4. Use K-Means clustering to group customers.

5. Label segments: “Champions”, “At-Risk”, “Low Value” etc.

6. Build a dashboard showing segment distribution and KPIs.

Tools: Python (Pandas, Scikit-learn, Matplotlib), Power BI / Tableau.

Deliverable:

• Segment-wise count, revenue, retention recommendations.

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2. Cart Abandonment Behavior Analysis

Goal: Identify why customers add items to cart but don’t complete checkout.

Data Needed:

• Session ID
• Products added to cart
• Checkout status (Yes/No)
• Time spent before leaving site
• Device type, location

Steps:

1. Load and clean session-level tracking data.
2. Calculate cart abandonment rate = Abandoned carts / Total carts.
3. Analyze trends by device, time of day, product category.
4. Identify top reasons (e.g., shipping cost, payment failure).
5. Suggest interventions (discount pop-ups, email reminders).

Tools: SQL (to query behavior logs), Power BI for visual trends.

Deliverable:

• Dashboard with abandonment rate trends, heatmap of peak drop-off times.

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3. Sales Funnel Drop-Off Dashboard

Goal: Visualize where customers exit in the sales funnel and quantify lost opportunities.

Data Needed:

• Funnel stages: Visit → Product View → Add to Cart → Checkout → Purchase
• Session counts at each stage

Steps:

1. Calculate conversion % at each stage.
2. Create a funnel chart to visualize drop-off points.
3. Segment by channel (Organic, Paid Ads, Email).
4. Suggest stage-specific improvements.

Tools: Power BI, Tableau, or Google Data Studio.

Deliverable:

• Interactive funnel dashboard with filters for channels and time periods.

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4. Product Recommendation Insights

Goal: Analyze buying patterns to suggest related or similar products.

Data Needed:

• Transaction history
• Product category, price, customer ID

Steps:

1. Build a co-occurrence matrix (products bought together).
2. Use association rule mining (Apriori algorithm) for frequent itemsets.
3. Recommend products based on confidence/lift metrics.
4. Visualize product pairings in a network graph.

Tools: Python (MLxtend, NetworkX), Tableau for visualization.

Deliverable:

• Recommendation dashboard for cross-sell/upsell strategies.

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5. Seasonal Sales Forecasting

Goal: Predict future sales patterns using time-series analysis.

Data Needed:

• Daily/Monthly sales data
• Dates, product categories

Steps:

1. Aggregate sales over time periods.
2. Decompose into trend, seasonality, residual components.
3. Apply ARIMA/Prophet for forecasting.
4. Validate with train-test split.
5. Create charts showing predictions vs actual sales.

Tools: Python (Prophet, Statsmodels), Power BI for final visuals.

Deliverable:

• Forecast charts for each category, seasonality insights.

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6. Dynamic Pricing Analysis

Goal: Identify optimal price points based on demand and competition.

Data Needed:

• Product prices
• Sales volume
• Competitor prices
• Discount history

Steps:

1. Merge internal sales data with scraped competitor pricing.
2. Plot demand vs price curves.
3. Find price elasticity of demand.
4. Recommend price ranges for maximum revenue.

Tools: Python (Scrapy/BeautifulSoup, Pandas), Tableau.

Deliverable:

• Price elasticity chart + revenue simulation tool.

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7. Loyalty Program Impact Study

Goal: Measure whether loyalty program members spend more and churn less.

Data Needed:

• Customer ID
• Loyalty membership status
• Purchase history
• Retention/churn status

Steps:

1. Compare average spend between members vs non-members.
2. Calculate churn rates for both groups.
3. Run statistical significance tests (t-test).
4. Visualize impact on lifetime value.

Tools: Python (SciPy), Power BI.

Deliverable:

• KPI dashboard showing ROI of loyalty program.

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8. Churn Prediction in Online Stores

Goal: Predict which customers are likely to stop buying.

Data Needed:

• Purchase frequency, recency
• Average order value
• Customer service interactions

Steps:

1. Label churners (no purchase in last X months).
2. Encode features, split data into train/test.
3. Train models (Logistic Regression, Random Forest).
4. Evaluate with ROC-AUC, precision/recall.
5. Deploy in dashboard to flag at-risk customers.

Tools: Python (Scikit-learn), Power BI for visualization.

Deliverable:

• Churn risk scoring dashboard with intervention suggestions.

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9. Ad Campaign ROI Tracker

Goal: Track the return on investment for various advertising campaigns.

Data Needed:

• Campaign spend
• Impressions, clicks, conversions
• Revenue from campaigns

Steps:

1. Calculate ROI = (Revenue – Cost) / Cost.
2. Compare across channels (Google, Facebook, Email).
3. Identify best-performing ads.
4. Visualize trends over time.

Tools: Power BI, Google Data Studio.

Deliverable:

• Ad ROI dashboard with channel-wise performance metrics.

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10. Multi-Channel Purchase Journey Map

Goal: Visualize customer journey across multiple touchpoints.

Data Needed:

• Session logs by channel (Organic, Paid, Social)
• Sequence of interactions
• Purchase status

Steps:

1. Sequence events for each customer.
2. Create Sankey diagram for journey visualization.
3. Identify common conversion paths.
4. Suggest journey optimizations.

Tools: Python (Plotly Sankey), Tableau.

Deliverable:

• Interactive journey flow diagram with conversion rates.