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The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for traditional CPG firms in the new online age and learn what data such firms have access to and what kind of problems they can address with data.

The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for traditional CPG firms in the new online age and learn what data such firms have access to and what kind of problems they can address with data.

The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for traditional CPG firms in the new online age and learn what data such firms have access to and what kind of problems they can address with data.

Arcelik Turkey, the country's market leader in household appliances, was at an omnichannel crossroads in January 2020. Arcelik was a B2B player utilizing a dealership network with an umbrella of brands and had one of the largest brick-and-mortar store networks in Turkey. In 2015, Arcelik, which was focused on offline sales, realized that online demand was growing and they were behind the competition, which was intensified due to the entry of global household appliances brands and online marketplaces to Turkey. So, they decided on a company-wide omnichannel transformation scheduled to go-live by 2020. They faced resistance from both dealers and employees. While Arcelik tried to gain the dealers' trust and buy-in, they experimented with one of their smaller brands on online marketplaces to learn more about e-commerce. In early 2020, Arcelik felt the squeeze as pressure mounted from the growing presence of marketplaces and online demand. They needed to find a way to cater to the online demand quickly while keeping their established partners happy.

Spreadsheet supplement for case 521080.

Spreadsheet supplement for case 521078.

Spreadsheet supplement for case 521079.

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.

Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting"

Spreadsheet Supplement to "Artea: Designing Targeting Strategies" (521-021).

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.

This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P's of marketing - promotion, price, place and product-characterizing the marketing decision that generates the bias and highlighting the consequences of such a bias. Then, it explains the potential causes of algorithmic bias and offers some solutions to mitigate or reduce this bias.

This case provides a post-mortem of the advertising technology (adtech) company Sizmek. Sizmek grew via multiple acquisitions, with the vision of becoming an integrated adtech company that could leverage AI to buy digital media, while creating and serving display and video content. At its peak, one of its acquisitions had an IPO valuation of almost 1 billion dollars. However, Sizmek struggled with an integration that was taking longer than expected, but also with misalignment of agency incentives, stricter data privacy regulations, and the rise of the walled gardens - Google, Facebook, and Amazon - that made the adtech ecosystem very competitive, with dynamics of winners-take-all markets. By 2019 Sizmek had to file Chapter 11, and was sold in pieces. Is there anything Sizmek could have done differently in their go-to-market strategy, product road-map, or overall partnerships to avoid such rapid collapse?

Thingtesting, a brand discovery and testing digital community devoted to uncovering and exploring direct-to-consumer brands, had just received seed funding and was contemplating a second year of growth. The new year brought many challenges, as founder Jenny Gyllander had to decide how to scale her fledgling company. As the company matured, she had to decide what role she would play going forward: should she continue to operate as head curator and content developer of her own reviews or open up her platform to invite and crowdsource the reviews of her followers. Her value proposition positioned her at the nexus of several different audiences: millennial consumers eager to try new brands, venture capitalists eager to invest in them, and the companies and brands themselves, who were interested in seeing their new products succeed. What was the best path to monetizing the business? As Gyllander navigates the emerging landscape of influencer marketing, she needs to decide how to transform her online audience into a co-creating community and how to best wield her own influence to create value for her followers and other constituencies in a way that could fund a profitable business model.

Despite a heavy barrage of advertising, most consumers declare that their purchases are most influenced by the experiences, advice, and recommendations of others, and not by marketers. Interpersonal communication between and among consumers serves as a potent path for influence, and by 2022, marketing managers are expected to spend $15 billion on influencer marketing, a marketing technique in which companies partner with people with specialized knowledge, expertise, authority, social position, and/or personal relationships that enable them to have influence over others to co-produce marketing messages to promote their brands via offline and electronic word-of-mouth. This technical note provides an overview of influencer marketing and discusses the various roles that influencers play for marketers, including delivering marketing messages in a more authentic voice and setting than that offered by more traditional advertising media where messages are authored by firms. It categorizes different types of influencers, explains the social psychological processes that make interpersonal influence work, and provides guidelines on how to identify people with influence. It discusses the opportunities and challenges related to measuring the performance of influencers and assessing the costs and return-on-investment of influencer marketing programs. Finally, it highlights emerging risks in the contemporary influencer marketing landscape.

When customers seek out online deals, it seems like a win for everybody: Brands, retailers, dealers, and distributors sell more goods, and buyers get a bargain. What's not to like? Here's the problem: Lured by rock-bottom online prices, customers often end up dealing with disreputable digital resellers that mistreat them, which can erode brand equity and undermine authorized sellers. For brands selling in the United States and Canada, there is a potent countermeasure: crafting and enforcing pricing policies that discourage anyone from advertising or selling a firm's products at an unauthorized discount. This article lays out the four steps essential to designing and enforcing policies with teeth that stay on the right side of antitrust law.

In mid-2019, Carlos Hank, was deliberating over the results for Banorte Móvil - the mobile application for Banorte, Mexico's most profitable and second-largest financial institution. Hank, who had been appointed as Banorte´s Chairman of the Board in January 2015, had overseen Banorte's transformation (and multi-million-dollar investment) from a product and client volume-focused bank into a customer-centric, technology and data-driven organization with a radically new focus. Beyond investing in a new technology platform and deploying new digital channels, Banorte also invested in the development of internal capacities to convert data intelligence into profits. Spearheaded by José Antonio Murillo, an economist with a Ph.D. from Rice University, Banorte's Analytics Business Unit (ABU) kicked off early on 2015, reporting directly to upper management and focused on increasing customer lifetime value. Over the course of its four-year trajectory, through data analytics and experimentation, which involved both experimenting with client incentives and tailored communication strategies through multiple channels, the ABU had successfully achieved higher product placement rates, particularly under Banorte Móvil's platform. Yet, with 2.25 million active users, Banorte Móvil was still far from reaching its 4-million-user target by 2020. Adoption picked up, but Banorte Móvil was still losing many potential adopters along the mobile customer journey, particularly in its activation phase. Furthermore, 81% of app activity was for account balance or transaction views, with financial operations, such as card or service payments or acquisition of new bank products, accounting for a minority of the activity. If Banorte wanted to remain a top player in Mexico's financial sector, it was clear to upper management that growth in mobile banking needed to be a priority.

When the mother-daughter founders of DivaCup set out with a mission to disrupt the menstrual care industry with an innovative product form, they initially struggled to gain legitimacy and convince retailers to carry their unique product. Now, fifteen years later, the product was available online and in more than 35,000 retail stores worldwide, and the company was struggling with omnichannel distribution challenges and frequent violations of their pricing policy due to intense competition among channel partners. International distribution and the prevalence of gray market sellers were causing large price dispersions across markets, which was harming the brand further. In October 2018, the entry of a formidable competitor, P&G (owner of menstrual care brands Tampax and Always), into the niche product segment causes the CEO and her team to consider the best plan to reposition and differentiate its product to broaden its appeal to users of other product forms, while maintaining its market leadership in the menstrual cup segment. This challenge is made more difficult due to prevalent miseducation about menstrual care and menstrual cups in particular.