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• Technical Note | HBS Case Collection | August 2020 (Revised September 2020)

  Assessing Prediction Accuracy of Machine Learning Models

  Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne

  The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools are then described: mean squared error (MSE), mean absolute deviation (MAD), Brier score, and cross-entropy, true/false positives/negatives, the confusion matrix, true positive rate (sensitivity or recall), false negative rate (Type II error rate), precision, true negative rate (specificity), false positive rate (Type I error rate), receiver operating characteristic curve (ROC) and area under the curve (AUC), and precision-recall curve.

  Keywords: Machine learning; statistics; econometric analyses; experimental methods; data analysis; Data Analytics;

  Citation:

  Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)  View Details

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• Case | HBS Case Collection | September 2020

  JOANN: Joannalytics Inventory Allocation Tool

  Kris Ferreira and Srikanth Jagabathula

  Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and optimize store-specific inventory allocation decisions guided by these predictions. After the analytics tool had been in use for a full season of products, Joyce was surprised to hear that not everyone was pleased with its outcomes. With all of the concerns raised, should Joyce halt the use of the analytics tool for the next season?

  Keywords: analytics; Machine learning; optimization; Inventory management; Mathematical Methods; Decision Making; Operations; Supply Chain Management; Resource Allocation; Distribution; Technology Adoption; Software; Change Management; Fashion Industry; Consumer Products Industry; Retail Industry; United States; Ohio;

  Citation:

  Ferreira, Kris, and Srikanth Jagabathula. "JOANN: Joannalytics Inventory Allocation Tool." Harvard Business School Case 621-055, September 2020.  View Details

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• Case | HBS Case Collection | September 2015 (Revised February 2020)

  Managing Consumer Touchpoints at Nissan Japan

  Thales Teixeira, Nobuo Sato and Akiko Kanno

  In 2015, Nissan was third place in the Japanese auto market, behind Toyota and Honda. The challenge of increasing market share was that 80% of car shoppers who were non-Nissan owners did not consider Nissan during their purchase process. This process involved three main consumer touchpoints: mass media advertising, internet auto websites, and the physical dealerships. In the last five years, the importance of the dealers in influencing car sales had greatly diminished as consumers researched and chose which make and brand of car to buy online, going to the dealer only to negotiate price, purchase, and receive the car. Given this trend, how should Nissan's top marketer make changes in how Nissan manages these three key consumer touchpoints? And how to better integrate them to maximize sales.

  Keywords: Nissan; Automobiles; cars; touch points; digital marketing; Internet; Web Sites; Marketing Channels; Advertising; Auto Industry; Japan;

  Citation:

  Teixeira, Thales, Nobuo Sato, and Akiko Kanno. "Managing Consumer Touchpoints at Nissan Japan." Harvard Business School Case 516-035, September 2015. (Revised February 2020.)  View Details

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