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ML for Marketing

ML for Marketing

We built ML models to optimize revenue through marketing by predicting whether there will be coupon redemption in a transaction or not.

Shanelle Recheta

March 18, 2021

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  1. What will work? Who to target in their specific campaigns?

    What is working? Which campaigns are likely to succeed? What has worked? Which coupons should be revisited?
  2. Customer-first Analytics • Profiling the customers through K-means Clustering •

    Observe customer behavior through Aggregation of features • Comparing the performance of campaigns through descriptive stats
  3. Opportunity to Market: Purchasing Power Get median purchase duration between

    customers Recommend a coupon at about the same time they will be purchasing again Make the product stickier
  4. Underlying Magic: Feature Extraction Customer-centric variables Research-based (domain knowledge) Time-centric

    and Categorical variables ML-first EDA (getting high variance trees from Random Forest)
  5. Underlying Magic Strong Positive Correlation • brand vs item type

    ◦ e.g. Packaged Meat • brand vs campaign release date ◦ Day of week ◦ Month (seasonality)
  6. Challenges Encountered • Integrating all datasets • Feature Extraction ◦

    Effectively encoding categorical columns ◦ Clustering and extracting features from what the trees say have highest feature importance • Dealing with missing variables ◦ KNN impute • High memory usage: Large number of rows = long processing time ◦ Used SQL for queries
  7. Challenges Encountered • Models tested: Random Forest, XGBoost, fast.ai ◦

    bootstrapped significantly: bagging principle ◦ Fast iteration: we didn’t have a model that went through the entire dataset ◦ All models were bootstrapped instances that were averaged out ◦ fast.ai to ensure trees weren’t correlated to maximize the feature insights acquired • Overfitting ◦ L1 regularization does the trick
  8. Insights and Recommendations Factors affecting Success of Campaign • Day

    of the week that promos come out have relatively significant effect purchases and campaign came in • There are shoppers that shop heavily on specific days • Customers buy the same way • Brand type released on certain dates (start of week, seasonality) • Coupons that can be used for many items are redeemed more often
  9. Future Work • Integrate Brand-level Categorical Variables • Product Category

    Clustering: Looking at Price Sensitivities across Brands and Categories • Item code - Categories - Brand: Mapping and Analysis • Matrix Factorization for Coupon Recommendation • Collect data on profit per item (sales =/= profit) to effectively measure profitability of the marketing campaign