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Dunia Finance LLC (A), (B), and (C) - Teaching Note
Venkatesan, Rajkumar; Bodily, Samuel E.; Yemen, Gerry; Gibbs, SheaTeaching Note DARDEN-M-0842TN-EMarketingTeaching note for product M-0842Starting at €0.00
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Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits? - Teaching Note
Venkatesan, Rajkumar; Gibbs, SheaTeaching Note DARDEN-M-0897TN-EMarketingTeaching note for product M-0897Starting at €0.00
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The Tata Nano: The People's Car (Abridged)
Venkatesan, Rajkumar; Gibbs, SheaCase DARDEN-M-0850-EMarketingThis case is an abridged version of UVA-M-0768, "The Tata Nano: The People's Car (A)," and UVA-M-0804, "The Tata Nano: The People's Car (B)."Starting at €8.20
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Highly Recommended: Collaborative Filtering Gives Customers What They Want
Venkatesan, Rajkumar; Gibbs, SheaTechnical Note DARDEN-M-0974-EMarketingNetflix 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 o...Starting at €8.20
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Netflix, Inc.: The Mouse Strikes Back
Venkatesan, Rajkumar; Gibbs, Shea; Shively, DanielCase DARDEN-M-0975-EMarketingIn 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 anno...Starting at €8.20
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Paid Search Advertising
Venkatesan, Rajkumar; Gibbs, SheaTechnical Note DARDEN-M-0860-EMarketingThis note provides a primer on paid search advertising, which is an important component of digital marketing. The mechanics of paid search is explained using the Google search engine platform. The note covers metrics for evaluating the performance of paid search, the strategic objective of paid search, the relationship between customer lifetime value and search ads, how to overcome sparse data problems using keyword clouds, and the nature of Goog...Starting at €8.20
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Multiple Regression and Marketing-Mix Models
Venkatesan, Rajkumar; Gibbs, SheaTechnical Note DARDEN-M-0855-EMarketingIn this technical note, the concept of regression using a single independent variable is first introduced and then some of the practical challenges associated with it, including multiple independent variables in a regression, are discussed. Particular attention is paid to bias in the regression coefficients in the presence of omitted variables. The concept of the economic significance of a model is introduced and is contrasted with statistical s...Starting at €8.20
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Text Analytics: Turning Words into Data
Venkatesan, Rajkumar; Gibbs, SheaTechnical Note DARDEN-M-0986-EMarketingThe 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 informati...Starting at €8.20
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Segmentation at Sticks Kebob Shop
Venkatesan, Rajkumar; Gibbs, SheaCase DARDEN-M-0866-EMarketingThis case is used in the Marketing Analytics elective at Darden. A Sticks executive team is interested in opening a second quick-service restaurant in Richmond, Virginia. But before doing so, the team wanted to gain a better sense of who were Sticks' customers, which location would attract the best customers, and how to best connect with customers. An opportunity to gather survey data presented itself. Would the demographic and psychographic assu...Starting at €8.20
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Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?
Gibbs, Shea; Venkatesan, RajkumarCase DARDEN-M-0897-EMarketingHundreds 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, increa...Starting at €8.20