A team of Tippie business analytics students translated over 20 million rows of transactional data into behavior-driven shopper segments. Their insights revealed opportunities to grow revenue through targeted offers and promotions that increase basket size and the frequency of shopper visits.

Tools: R, Tableau
Technique: Cluster Analysis

Tippie’s data analysts worked with over twelve million cells of data in order to build a month-by-month predictive model for “roll off.” (Roll off is an industry term for business that will “roll off” the books in the future.) Paired with new business projections, accurate roll off projections provide BMO Harris Transportation Finance with critical insights used for operational planning across the organization.

Tools: R, RStudio, Rattle
Techniques: Decision Tree, Survival Analysis

Tippie analysts evaluated hundreds of thousands of data cells and built predictive models that work around noisy data and predict probability of application and enrollment. The results were ranked and showed 90% of the positive results were found in the top three deciles of the prediction. The resulting models help admissions teams identify the best candidates on which to focus their recruiting dollars.

Tools: R, RStudio, Rattle, Tableau
Techniques: Decision Tree, SVM, Logistic Regression, Lift Charts

The Tippie Analytics Cooperative worked with millions of data records provided by Paperless Transactions in order to reveal important behavioral indicators of fraudulent activity in online donations and giving. The insights revealed by this engagement provide Paperless Transactions with the validation and insights they need to begin developing the next generation of fraud detection for their products.

Tools: R, RStudio, Rattle
Techniques: CRISP-DM, Decision Tree