WHAT ARE SOME EXAMPLES OF BUSINESS ANALYTICS CAPSTONE PROJECTS

Customer churn prediction and prevention: For this project, you would analyze a company’s customer transaction and demographic data to build predictive models to identify customers who are most likely to cancel their services or accounts. The goal would be to predict churn with reasonable accuracy. You would then make recommendations on how to prevent churn, such as targeted marketing, incentives to stay, or improving customer service. Some key steps would involve data collection, data cleaning, EDA, feature engineering, model building using techniques like logistic regression, random forests, exploring different predictive variables and their impacts, and recommending a prevention strategy.

Customer segmentation: For a retail company, you could analyze past transaction and demographic data to group major customer types into meaningful segments based on their spending patterns, purchase behaviors, product preferences. Common clustering techniques used include k-Means clustering, hierarchical clustering etc. You would need to select appropriate variables, preprocess the data, find the optimal number of clusters, label and describe each segment, their characteristics and differences. Recommend a customized marketing strategy for each segment. For example, discounts, loyalty programs etc. targeted to each customer group.

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predicting movie box office revenues: For a movie studio, collect data on variables like movie budget, genre, ratings, critics reviews, social media buzz, cast, director etc. for past movies. Build predictive models to forecast the box office revenues for upcoming movies based on similar independent variables. Models like multiple regression, decison trees can be used. Also analyze factors influencing success and failure. Recommend data-driven strategies for marketing budget planning and movie development decisions.

Market basket analysis for online retailers: Analyze past purchase transaction data to determine which products are frequently bought together. Identify affinity patterns using association rule mining techniques. Provide insights on related/complementary products to showcase together to increase average order value and cross-sell opportunities. Recommend new product bundles or packages for marketing based on the analysis. For instance, showing snacks together with beverages or batteries along with electronic devices.

Predicting customer churn for a telecom operator: Collect customer data like demographics, usage patterns, payment history, services subscribed, complaints etc. Build predictive models to identify customers who are most likely to switch operators in the next few months. Techniques like logistic regression, random forests can be employed. Understand driver attributes for churn like pricing plan dissatisfaction, network quality issues etc. Recommend targeted retention strategies like loyalty programs, bundled discounts, network upgrades in probable churn areas. Regularly rerun models on new data to catch drifting behavior over time.

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Predicting risks of credit card/loan defaults: Partner with a bank to analyze past loan application and repayment data. Develop predictive models to assess the risk level associated with approving new applications. Consider applicant factors like income levels, existing debts, credit history, collateral etc. Recommend risk-based pricing, underwriting criteria refinement and loan rejection guidelines to optimize portfolio quality vs volume. Models like decision trees, neural networks can be used. Evaluate model performance on new data batches.

Sales forecasting for retail stores: Obtain point of sales, item attributes, store attributes, promotions, seasonal data for chains of outlets. Build forecasting models at item/product, store and aggregate chain levels using statistical/machine learning techniques. Recommend inventory replenishment strategies, optimize allocation of fast-moving vs slow-moving products. Suggest test promotion strategies based on predicted lift in sales. Evaluate accuracy and refine models over time as new data comes in.

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Predicting tech support ticket volumes: For an IT company, analyze historical support tickets, system logs, downtimes, software release notes to identify patterns. Develop predictive models using time series/deep learning methods to forecast probable weekly/monthly ticket volumes segmented by type/priority. Recommend optimal staffing levels and training requirements based on the forecasts. Suggest process improvements and preventive actions based on driving factors identified. Regularly retrain models.

These are just some potential ideas to get started with for an analytics capstone project. The key is to find meaningful business problems where analytics can create value, obtain reliable structured or unstructured data, apply appropriate techniques to gain insights and make actionable recommendations backed by data and analysis. Regular evaluations on metric tracking and model performance over time is also important. With in-depth execution, any of these projects have potential to exceed 15,000 characters in the final report. Let me know if you need any clarifications or have additional questions.

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