This field-based case describes Wahoo Fitness's (Wahoo's) data-collection process, focusing especially on the customer survey it used to gain insights into its current and potential customers. One month before businesses across the United States closed due to COVID-19 lockdowns, Wahoo launched the Elemnt Rival GPS smart watch, which catered specifically to triathletes. The team at Wahoo was excited about the potential of this new category. To decide on next steps, the company turned to its roots: data insights. The team started with quantitative data surveys to identify customer segments and extract insights into other market opportunities. The data uncovered potential sports activity customer segments-competitive, social, and leisure. The team wondered: Was now the time to engage with cycling hobbyists or leisure cyclists? Or did it make sense to expand on the initial success of the smart watch and offer more products in the running segment? The case includes a student spreadsheet with survey data, as well as R code scripts for analyzing customer data and noncustomer data. Students utilize Excel to perform a K-means cluster analysis on the survey data, then use the analysis to determine a product expansion strategy and present their findings as if they were on the management team of Wahoo. It is a practical application of a data-driven process for tailoring product offerings and marketing strategy. This case has been taught at Darden in the Master of Science in Business Analytics program and in second-year MBA electives. It would also be suitable in graduate-level marketing or analytics courses, in addition to Executive Education programs.
This note discusses the sources of competitive advantage for firms, including scale economies, network economies, counter positioning, switching costs, branding, cornered resources, and process power. It also introduces the features of sustainable competitive advantage, offers examples, and includes a framework for assessing the sustainability of each source of competitive advantage. This note is taught at Darden in digital marketing and would also be suitable in a module covering competitive marketing strategy.
In this set of three exercises, students are cast in the roles of brand managers of one of three categories of consumer goods companies. They must choose a specific brand in their category and identify keywords to advertise that brand on Google search, in order to leverage or react to evolving consumer behavior. Students research trend tools and Google's keyword planner, explore and analyze consumer demand, identify customer segments, research product features, identify competitors, and finally choose paid search keywords.
This set comprises six exercises: in each, a team of students must shop online for products to solve a particular problem. Problems range from outfitting a new apartment quickly to purchasing food for a festival to choosing car insurance. Students will experience a customer journey as they navigate shopping websites.
The searchable internet contains almost 2 billion websites. And new, text-rich sites are being added at a rapid pace: more than 700 million popped up from 2016 to 2017, according to the International Real Time Statistics Project. A lot of this web-based text is relevant to marketers: online product reviews, information about purchasing behavior, customer-to-customer interactions, and transcribed tele-sales calls. Marketers now have more information from consumers in the form of written words than ever before. The problem, as with any extremely large data set, is determining how best to use the information. The relatively new fields of text analytics and sentiment analysis offer marketers a solution, enabling them to turn vast amounts of emotion-rich, word-based data into actionable information about consumers. This note explores dictionary-based sentiment analysis using programming language R; it also introduces empirical sentiment analysis.
Netflix Top Picks, Amazon recommendations, the iTunes Genius button. They all have one thing in common: they are driven by clever algorithms that use a technique known as collaborative filtering. Often used in machine learning operations, collaborative filtering is the process by which a firm like Netflix generates predictions about a single user's preferences using data taken from a large number of users. This technical note offers an overview of three of the main collaborative filtering methods: slope one, a purely predictive nonparametric model; ordinal logit, a parametric regression model; and alternative least squares, a matrix factorization technique.
In 2017, Disney announced that in 2019 it would launch Disney Plus, a subscription-based streaming video service that promised to rival Netflix, the dominant player in the market. This was the latest advancement in the history of movie rentals, which had first exploded in the 1980s with the advent of videotape and had gone through several technological transformations before reaching the age of streaming in the 2010s. At the time of Disney's announcement, Netflix dominated the market due to its ever-improving algorithmic recommendation system and its investment in original content. How would Netflix withstand the competition from Disney? What would be an appropriate measure of relative strength for a streaming service? This case offers a way in to discussions of customer lifetime value, market capitalization, and discounted cash flows, as well as the role of technological change in business models and firm valuation techniques.
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing-an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.
Ford's F-Series of trucks were first introduced in 1948, and ever since they have represented American identity for their consumers. Both earned media, in movies like Urban Cowboy, and Ford's paid media positioned Ford as part of the pioneering culture. Ford also constantly introduced innovations to the F-Series to make the trucks more suitable to the changing needs of its consumers. In 2018, Ford's management decided to retreat from the low-margin cars segment and focus on trucks and SUVs. Ford was also working toward robot taxis and driverless delivery by 2021. These two parallel trajectories converge to pose a pivotal challenge for Ford: Should the company invest in developing driverless capabilities for its best-selling and highest-margin product, the F-150? The case provides students with a context in which to discuss the changing technologies in the auto industry and their implications for industry structure, along with the specific aspects of software-driven business models, consumer preferences, and brand identity. It also offers an opportunity to explore the challenges faced by traditional businesses as they develop digital capabilities and reimagine their business models to fully leverage artificial intelligence (AI). The competition among Ford, Google Inc. (Google), Uber Technologies, Inc. (Uber), and Tesla, Inc. (Tesla) in the automonous vehicle industry highlights the different routes these companies have taken to obtain develop autonomous vehicle capability that leverages their respective strategic capabilities.
This technical note gives students an overview of artificial intelligence (AI) and machine learning (ML) in order to help them understand how these fields can contribute to the future of marketing. To provide context, students are first introduced to the history of AI and the basic parameters of AI, ML, and deep learning (DL). The differences between ML and statistical modeling are also described to help students understand that collaboration between these two fields results in better decision-making. The note also provides a description of descriptive, predictive, and prescriptive analytics and how various ML tools span those categories. In order to illustrate AI's applications and the many ways managers can use it to promote their brands, real-world examples are provided, including: (1) 1-800-Flowers' collaboration with the Facebook messenger platform to process orders through chatbots (using DL), (2) Facebook's use of DeepText to determine the meaning of words within their contexts (using DL) and then direct users to related products; and (3) online educator Udacity's use of an ML algorithm to create a bot that advises salespeople on successful words and phrases, but also allows the humans to answer more obscure customer questions, among others. As students consider how AI advances are helping brands such as these market their goods and services to new customers online, students also must consider the ways that AI will continue to shape marketing in the future.
Supplement to case UV7459. In 2016 Andrew Rose, CEO of Compare.com, an online price comparison website for car insurance shoppers, faced a troubling problem. Completion rates for the site's detailed online questionnaire were at an alarming low. Site visitors, increasingly accessing the site on mobile devices, were proving they did not have the time or incentive to answer the site's requisite questions and thus were dropping off the site before purchasing policies from the site's partner insurance carriers. As Rose and his management team struggled to lift the completion rates, they narrowed their options to three potential solutions. A task for Kyle Brodie, a summer intern, was to design and run an experiment that could yield valuable insights from an estimates display, including which customer groups, if any, responded best to the estimates, and where the estimates should be included in the questionnaire. In the A case of the series, readers are faced with selecting the best solution for lifting Compare.com's completion rate. In the B case, readers must design an experiment to test the selected option, and decide on the location and content of an estimate display test. In the C case, readers are presented with the design implemented by Brodie and a summary of the results of that experiment. They must then figure out the implications of those results for Compare.com. This case was originally written for an MBA marketing class examining Marketing Analytics. It would also be suitable for similar classes in undergraduate, Executive MBA, and Executive Education programs.
Supplement to case UV7459. In 2016 Andrew Rose, CEO of Compare.com, an online price comparison website for car insurance shoppers, faced a troubling problem. Completion rates for the site's detailed online questionnaire were at an alarming low. Site visitors, increasingly accessing the site on mobile devices, were proving they did not have the time or incentive to answer the site's requisite questions and thus were dropping off the site before purchasing policies from the site's partner insurance carriers. As Rose and his management team struggled to lift the completion rates, they narrowed their options to three potential solutions. A task for Kyle Brodie, a summer intern, was to design and run an experiment that could yield valuable insights from an estimates display, including which customer groups, if any, responded best to the estimates, and where the estimates should be included in the questionnaire. In the A case of the series, readers are faced with selecting the best solution for lifting Compare.com's completion rate. In the B case, readers must design an experiment to test the selected option, and decide on the location and content of an estimate display test. In the C case, readers are presented with the design implemented by Brodie and a summary of the results of that experiment. They must then figure out the implications of those results for Compare.com. This case was originally written for an MBA marketing class examining Marketing Analytics. It would also be suitable for similar classes in undergraduate, Executive MBA, and Executive Education programs.
In 2016 Andrew Rose, CEO of Compare.com, an online price comparison website for car insurance shoppers, faced a troubling problem. Completion rates for the site's detailed online questionnaire were at an alarming low. Site visitors, increasingly accessing the site on mobile devices, were proving they did not have the time or incentive to answer the site's requisite questions and thus were dropping off the site before purchasing policies from the site's partner insurance carriers. As Rose and his management team struggled to lift the completion rates, they narrowed their options to three potential solutions. A task for Kyle Brodie, a summer intern, was to design and run an experiment that could yield valuable insights from an estimates display, including which customer groups, if any, responded best to the estimates, and where the estimates should be included in the questionnaire. In the A case of the series, readers are faced with selecting the best solution for lifting Compare.com's completion rate. In the B case, readers must design an experiment to test the selected option, and decide on the location and content of an estimate display test. In the C case, readers are presented with the design implemented by Brodie and a summary of the results of that experiment. They must then figure out the implications of those results for Compare.com. This case was originally written for an MBA marketing class examining Marketing Analytics. It would also be suitable for similar classes in undergraduate, Executive MBA, and Executive Education programs.
Hundreds of thousands of would-be hoteliers have been popping up all around the world, hoping to rent their own homes and apartments to complete strangers through a service called Airbnb. The goal of Airbnb's aspiring hosts was to use the company's website to attract guests who were willing to pay the highest rates to stay in their homes for a short time. For Airbnb, the goal was to improve customer review performance so it could, in turn, increase profits. How could the company achieve its goal? Enter text mining, a technique that allowed businesses to scour Internet pages, decipher the meaning of groups of words, and assign the words a sentiment proxy through the use of a software package. In order for text mining to be useful for Airbnb, its marketing professionals first had to gain access to customer review data on the company's own website. The team then had to analyze the data to find ways to improve property performance. Was the team going to be able to leverage this large amount of data to determine a strategy going forward?