This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an urgent audit of historical purchase data to assess customer growth patterns. Students are tasked as the CMO to analyze four years of transactional data, addressing questions on customer value, characteristics, and segmentation. The case study emphasizes the importance of a thorough customer-base audit to develop strategies for sustainable growth, serving as a practical learning tool for evaluating any company's customer base health.
"Unintended Consequences of Algorithmic Personalization" (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for algorithmic biases in marketing interventions, encompassing promotion, product, price, and distribution. The case is designed to enhance students' understanding of algorithmic bias in personalized marketing. It encourages discussions on its causes and strategies for detection and mitigation. A key learning is that such bias is often unintentional and can occur without data errors or underrepresentation in the sample. A central theme is the trade-off between optimization and fairness in algorithmic decision-making. Overall, these case studies provide comprehensive discussions on the causes, implications, and solutions to algorithmic bias in personalized marketing, complemented by the technical note "Algorithm Bias in Marketing" (HBS No. 521-020) that accompanies the case.
Hosam Arab (MBA 2009), cofounder and CEO of Tabby, a Saudi-based fintech startup, raised its Series D funding round in October 2023, four years after its inception, valuing it as a regional unicorn. Tabby's core product, a buy-now-pay-later (BNPL) service, allowed consumers to split payments into four equal installments without fees. The company earned revenue by charging commissions to partnered merchants, ensuring low customer acquisition costs-a key element of Tabby's model that facilitated rapid and cost-effective scaling. However, Tabby encountered a significant challenge in Saudi Arabia, its main market, where key retailers demanded adopting a competitor's pricing model that involved charging end consumers for BNPL services instead of merchant fees. This situation forced Tabby to consider whether to adhere to its consumer-friendly approach that spurred its growth or to adjust its strategy due to competitive pressures. The dilemma raised questions about the future standard for charging end consumers and whether Tabby should conform or maintain its original model. The case details Tabby's journey from its founding to October 2023, highlighting its business model focused on indirect consumer acquisition and risk management. It also outlines how Tabby gained a competitive edge, selected and partnered with merchants, and leveraged BNPL as a tool for expansion into related products, thereby diversifying consumer monetization strategies.
Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of customer acquisition, retention, and development, aiming to assist managers in achieving sustainable customer growth. MCG focuses on examining the complexities of managing customer relationships in an environment characterized by increasing data availability and rapid technological advancements. The course blends case studies and practical exercises/workshops, aiming to provide comprehensive insights into customer management strategies, key frameworks, and tools for enhanced decision-making. It also highlights the challenges that today's managers face, especially regarding the ethical and responsible use of data to protect consumer privacy and prevent algorithmic bias. The interactive workshops enable students to engage with various forms of customer data, which will be analyzed to inform strategic decisions. The aim of these workshops is to hone students' quantitative intuition and to improve their ability to effectively collaborate with data science teams, thereby enhancing their data-driven decision-making capabilities. This note outlines the course objectives, the key questions, and the core ideas explored throughout the course.
In late May 2023, Sarah Merino, the newly appointed manager of the Customer Insights group at Travelogo-an online travel booking platform-initiates a comprehensive analysis of clickstream data to understand the varied behaviors and needs of their users. In preparation for the upcoming marketing strategy meeting, Merino and her team delve into the extensive customer data collected by the platform as users search for flights, aiming to understand search patterns, trip characteristics, and clicking and purchasing behaviors. This deep dive is to identify distinct customer personas and actionable insights that will shape Travelogo's marketing strategies. The case provides students with a hands-on exploration of the fundamentals and benefits of customer segmentation, fostering an understanding of how to create detailed buying personas from complex data for strategic marketing purposes. Additionally, the case emphasizes the critical need for effective collaboration between decision-makers and data science teams, underscoring the importance of clear communication to ensure that data analytics are precisely aligned with business objectives, especially when crafting sophisticated strategies to engage and cater to a varied customer base with a deep understanding of their preferences.
This note provides an overview of the evolving landscape of customer data privacy in 2023. It highlights two pivotal aspects that make privacy a central concern for businesses: building and maintaining customer trust and navigating the intricate regulatory environments. The note examines common privacy risks and their potential impacts on businesses, including reputational damage and compliance issues. It also offers insights into the differences between European and American regulatory practices. Furthermore, the note explores prevalent business practices related to data privacy and the challenges faced by industry professionals. It concludes by analyzing the perspectives of both consumers and regulators on privacy, identifying key challenges, and posing thought-provoking questions for further exploration in this digital age.
Targeted interventions serve as a pivotal tool in business strategy, streamlining decisions for enhanced efficiency and effectiveness. This note delves into two central facets of such interventions: first, the design of potent decision guidelines, or targeting policies, through assessing individual reactions via A/B testing; and second, the strategic use of A/B tests to predict policy outcomes prior to their full-scale implementation. Complementing these insights, the note presents real-world data illustrations and essential coding guidance, furnishing readers with the hands-on skills needed to adeptly create and evaluate targeting policies.
In 2022, retail media was one of the fastest growing segments in digital advertising. A retail media network (RMN) allows a retailer to use its assets for advertising. Retailers set up an advertising business by allowing marketers to buy advertising space across their different channels, such as their website or mobile application as well as physical stores or other properties the retailer owns or partners with. The value proposition retailers offer advertisers is to potentially generate more effective advertising and to drive sales, by leveraging their exclusive data that includes customer behavior and transactions (both online and offline), allowing advertisers to close the loop from ad exposure to purchase across channels. This note provides an overview of retail media networks in 2022. First, it describes the role played by each key player-retailers, brands, and consumers-as well as the main drivers behind the dramatic growth of RMN in the last few years. Then, it describes the main challenges for retailers who aim to seize the RMN opportunity. Third, it positions the RMNs within the broader context of digital advertising, identifying key challenges and open questions in this industry.
This case explores the tradeoffs between product personalization and simplicity as companies grow. The case presents an opportunity to understand whether and how each of these approaches enables and/or limits companies' abilities to provide customer satisfaction while being efficient in their operations. In October 2018, Allianz was one of the world's leading insurers and asset managers with 103 million retail and corporate customers in 70 countries. It was one of only two insurers to rank amongst the world's 50 strongest brands in 2017, a sign that the company's customer-centricity approach drove value and resonated with clients. Allianz's ambition was to reach the top 25 brands in Interbrand's ranking by 2025. For the insurer, the key to success was to focus on simplicity-reducing the complexity of products and processes in order to create a more unified customer experience. However, such a move did not align with current trends in insurance markets, where Allianz's main competitors had opted for hyper-personalization. Furthermore, a strategy focused on simplicity implied a radical move in certain key markets where Allianz had traditionally offered a large diversity of products. Was simplicity the right strategy? Would Allianz be able to embrace customer needs successfully within and across markets while simultaneously growing its business?
In October 2020, Melissa Wood-Tepperberg, founder of the digital subscription wellness platform Melissa Wood Health (MWH) and creator of 'The MWH Method,' was evaluating the strategic directions of her company. What had started as a way to share workouts and wellness tips via Instagram only five years ago, today was a growing business enterprise. The MWH membership was reaching 92,000 paying subscribers who accessed the content in her platform. Wood's base of Instagram followers was also growing, now totaling more than 650,000, along with the number of brands who sought her attention and endorsement to promote their brands via her Instagram account. As the business grew, new opportunities arose. Today Wood was meeting with executives of a large fashion retailer who had proposed a partnership to create, launch, and sell Wood's own collection of apparel-an important step for MWH towards becoming a lifestyle brand. How should Wood approach the negotiations about the deal terms? What would this partnership mean to her members and followers? And how could she ensure this new partnership would help propel the other parts of her business?
This case introduces a new Amazon program that has consumers upload their receipts from transactions outside of Amazon, in exchange for money. Through the discussion, the case aims to explore issues in customers' privacy in the digital age, the value of customers' own data, and the change in regulations aimed to protect consumers that move companies from using third party data to first party data. In addition, the case offers an opportunity to discuss the power dynamics of online giants such as Amazon, Google, and Facebook.
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting"
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing-implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, "surprising" the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing-implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, "surprising" the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing-implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, "surprising" the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.