More from these Authors

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

  Facelift at Olay (A)

  Sunil Gupta, Rajiv Lal and Olivia Hull

  By October 2017, Procter & Gamble’s skincare brand Olay has been struggling with declining sales for several years. The team has tried many remedies, but none has returned the brand to growth. As pressure grows from Olay’s competitors, including hundreds of new direct-to-consumer brands catering to millennial women, North American Skin Care General Manager Chris Heiert must identify a winning marketing strategy that will appeal to women of all ages.

  Keywords: Advertising; Advertising Campaigns; Demographics; Age; Brands and Branding; Marketing Channels; Competitive Advantage; Competitive Strategy; Online Advertising; Transformation; Marketing Strategy; Social Marketing; Beauty and Cosmetics Industry; United States; Ohio;

  Citation:

  Gupta, Sunil, Rajiv Lal, and Olivia Hull. "Facelift at Olay (A)." Harvard Business School Case 521-011, August 2020. (Revised September 2020.)  View Details

  CiteView DetailsEducators

  Related

• Article | Marketing Science | September–October 2020

  Managing Churn to Maximize Profits

  Aurelie Lemmens and Sunil Gupta

  Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a retention offer. However, both approaches ignore that some customers contribute more to the profitability of retention campaigns than others. This study addresses this problem by defining a profit-based loss function to predict, for each customer, the financial impact of a retention intervention. This profit-based loss function aligns the objective of the estimation algorithm with the managerial goal of maximizing the campaign profit. It ensures (1) that customers are ranked based on the incremental impact of the intervention on churn and post-campaign cash flows, after accounting for the cost of the intervention, and (2) that the model minimizes the cost of prediction errors by penalizing customers based on their expected profit lift. Finally, it provides a method to optimize the size of the retention campaign. Two field experiments affirm that our approach leads to significantly more profitable campaigns than competing models.

  Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit;

  Citation:

  Lemmens, Aurelie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (September–October 2020): 956–973.  View Details

  CiteView DetailsSSRNFind at Harvard

  Related

• Supplement | HBS Case Collection | August 2020 (Revised August 2020)

  Case Exhibits for Digital Marketing at HBS Online

  Sunil Gupta and Rajiv Lal

  Citation:

  Gupta, Sunil, and Rajiv Lal. "Case Exhibits for Digital Marketing at HBS Online." Harvard Business School Spreadsheet Supplement 521-702, August 2020. (Revised August 2020.)  View Details

  CiteView DetailsPurchase

  Related