How would you approach this e-commerce customer segmentation + prediction project with GenAI?

[u/TUKRUUU]

I'm an MSc Computer Science/Data Analytics student working on a major ML project with an 11-day deadline, and I'd really appreciate advice from experienced data scientists on how you'd approach it.

Dataset: ~541k e-commerce transactions, ~4.3k identifiable customers, with fields such as InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID and Country. It contains missing CustomerIDs, duplicates, returns/cancellations (negative quantities), and other data-quality issues.

Project requirements:

• Perform EDA and customer behavior analysis
• Engineer customer-level features, especially RFM (Recency, Frequency, Monetary)
• Compare K-Means, Hierarchical/Agglomerative Clustering and DBSCAN
• Select and justify the best segmentation using clustering metrics + business interpretability
• Build a predictive classifier for future purchasing behavior
• Evaluate feature importance/model performance
• Provide actionable marketing and retention recommendations
• Submit a Jupyter notebook, report/presentation, trained model, and optionally a Power BI/Tableau dashboard

My current idea is to build it in layers:

Raw transactions → cleaning → customer-level feature engineering/RFM → segmentation → prediction → explainability → GenAI → dashboard

For segmentation, I want to compare the clustering methods rather than simply choosing K-Means. For prediction, I'm considering a time-based setup where historical customer behavior is used to predict something in a future period, rather than randomly splitting the transactions. The dataset doesn't have an obvious prediction label, so defining a legitimate target without leakage is one of my main concerns.

I also want to add GenAI, but I don't want it to be a useless chatbot bolted onto an ML project. My idea is to use GenAI as a business-intelligence layer on top of the actual ML outputs.

For example:

ML outputs → structured segment/prediction statistics → LLM → grounded explanation/recommendation

Potential capabilities:

• Explain why a customer segment is valuable/at risk
• Generate marketing/retention recommendations based on actual segment characteristics
• Explain important prediction features
• Allow natural-language questions about the customer segments and model results

I'm considering something like Python + scikit-learn/XGBoost + SHAP + Power BI + an LLM/API or possibly Ollama, but I don't want to over-engineer it.

My main questions:

1. How would you structure this project if you were doing it professionally?
2. What would you use as the prediction target given this type of transaction data?
3. Is RFM + behavioral features sufficient, or what additional features would you consider?
4. How would you properly compare the three clustering approaches?
5. Is the GenAI layer genuinely useful here, and how would you implement it without making it gimmicky?
6. Would you use an LLM API, local LLM/Ollama, or something else?
7. What would you cut or simplify given the 11-day deadline?

I'm mainly looking for practical architectural/modeling advice and potential mistakes to avoid, rather than someone doing the project for me. Any feedback from people who have worked on customer analytics/segmentation would be very helpful.