If you’re into marketing and data science, this combo is honestly gold right now. Every brand wants to understand customer behavior, optimize ad spend, and boost conversions—and that’s where data science in marketing comes in.
So if you’re a student, a job-seeker, or just someone who wants to explore cool projects, here are 8+ marketing data science projects that’ll not only help you learn but also look amazing on your resume or portfolio.
1. Customer Segmentation Using Clustering
Group customers based on behaviors, preferences, or purchase history using clustering techniques like K-Means. This is useful for targeted email campaigns, personalized ads, and product recommendations. Try using e-commerce or retail data for this.
2. Predicting Customer Lifetime Value (CLTV)
Estimate how much a customer is likely to spend over time. It’s super helpful for retention strategies and budgeting marketing efforts. You’ll use regression models and maybe survival analysis to forecast spending patterns.
3. Marketing Campaign Performance Analysis
Analyze past campaign data to understand what worked and what didn’t. Use A/B testing data or multi-channel performance data to generate actionable insights. You’ll work with metrics like CTR, ROI, and conversion rates.
4. Social Media Sentiment Analysis
Scrape Twitter, Instagram comments, or Facebook posts and analyze how users feel about a product, brand, or campaign. Use natural language processing (NLP) to classify sentiment (positive, neutral, negative). Brands love this kind of insight.
5. Ad Click-Through Rate (CTR) Prediction
Build a model that predicts the likelihood of a user clicking on an ad based on user profile, device type, time of day, and ad creative. You’ll use classification models and learn a lot about imbalanced datasets.
6. Market Basket Analysis
Ever seen “People also bought this” recommendations? That’s what this is. Use association rule learning (like Apriori or FP-Growth) to analyze what products are frequently bought together and improve cross-selling strategies.
7. Churn Prediction for Subscription Services
Use historical user behavior data to predict if a customer is about to cancel a subscription. You’ll use classification models here and also dive into feature engineering around engagement levels and usage patterns.
8. Dynamic Pricing Models
Use data like demand trends, competitor pricing, and inventory levels to suggest optimal pricing. It’s a bit advanced, but it teaches you about regression, time-series, and even reinforcement learning if you’re up for it.
9. Email Open Rate Prediction
Predict whether a user will open a marketing email based on subject line, time of send, and user profile. It helps fine-tune email campaigns and boost engagement. A nice combo of classification + marketing psychology!
Final Thoughts
These marketing data science projects blend creativity with analytical power—perfect for showing off both your technical skills and your understanding of customer behavior. You don’t need perfect data either. You can always use simulated or public datasets to start building.
Pick the one that excites you most and dive in. And if you ever need help building it out or coming up with features to include, I’ve got your back!
