This case builds directly on the case Predicting Purchasing Behavior at PriceMart (A), in which VP of Marketing, Jill Wehunt, and analyst Mark Morse build a logistic regression model to predict whether a customer household is expecting a baby. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the logistic regression. Wehunt and Morse then compare the models and select the model most effective at correctly classifying households as expecting. Students explore the relationship between the model's confusion matrix, which organizes the model's correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Wehunt and Morse construct. Students can then follow the link directly from their model to their marketing strategy. Technical topics covered: Ridge logistic regression (or L2 regularization) as a modelling technique; Lasso logistic regression (or L1 regularization) as a modelling technique; Comparing models, thinking about coefficients, and selecting model for deployment; Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to carry out marketing strategy.
LendingClub was founded in 2006 as an alternative, peer-to-eer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns grades and sub-grades to each application using its own risk evaluation model, it also makes detailed data on each loan applications available to both kinds of investors for their own analyses. The case follows MBA graduate Emily Figel as she researches LendingClub as a potential investment vehicle for the small wealth management firm she will join in the fall. Using LendingClub's historical data, she learns the fundamentals of predictive analytics to see whether she can build models to predict whether a borrower will repay or, ultimately, default on the obligation. This first case (A) presents students will relevant, detailed data about how the LendingClub model works. This includes LendingClub's business model, the grading of loans, the unique opportunities and risks. It also follows Figel as she dives into the data to use it to build a model. In the B and C cases, Figel explores several specific techniques for training models. Technical topics include: understanding the data, data preparation, balanced and unbalanced data sets, constructing training-validation-holdout sets, cross-validation, predictions and target leakage.
This case builds directly on the case LendingClub (A). In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan. Technical topics include: (1) Decision trees as a modelling technique, overfitting and induction bias, model validation; (2) Random forest as an ensemble-style modelling technique, bootstrapping, random feature selection; and (3) Log loss as a metric for evaluating and comparing models, feature impact.
This case builds directly on the cases LendingClub (A) and (B). In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default on his loan. Having now built three models, Figel compares them to determine which model is most effective at classifying borrowers correctly then uses that model to determine how to invest in a portfolio of loans. Students explore the relationship between her model's confusion matrix, which organizes the model's correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Figel constructs. Students can then follow the link directly from the model Figel builds to her specific investment decisions. Technical topics include: (1) Gradient boosted trees as a modelling technique; hyperparameters and learning rate; model validation; and (2) Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to compare investment opportunities.
This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers' purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups of customers who have purchased any number of 32 specific "deals" Booth offered over the year, differentiated by the wine varietal as well as its country of origin and a minimum number of bottles to purchase. Insights from this analysis may help him understand themes across the deals that can inform construction of new deals in the future. Topics include: Unsupervised learning; similarity and proximity; K-means clustering, with measures of Euclidean distance and cosine similarity; Gaussian mixture models; interpreting clusters.
This case builds directly on Chateau Winery (A). In this case Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those customers closest to them. Topics include: Supervised learning; collaborative filtering; K-nearest neighbor as a modeling technique; collaborative filtering with cosine similarity of customers; collaborative filtering with cosine similarity of products; comparison of different prediction models.
Lexoo, a UK-based online marketplace for legal services, was facing the strategic choice of how to grow from early start-up to mature platform. Daniel van Binsbergen, Lexoo's CEO, and web developer Chris O'Sullivan, CTO, had set up Lexoo to help Small and Medium-Sized Enterprises (SMEs) to find legal advice at low prices. At the time of the case in 2018, Lexoo had just started attracting larger companies in need of specialized legal advice as new customers. Larger companies had higher value and more frequent needs. Lexoo could become their "go-to" place across a broad range of legal services. Nevertheless, larger companies also required a more personalized approach and were more difficult to acquire than SMEs. Van Binsbergen and O'Sullivan were debating whether Lexoo should diversify to serve both SMEs and larger companies, or whether to pivot their focus toward larger companies altogether.
On a sunny Monday morning, Rev. Morgan-Thomas walked into her newly acquired hat factory thinking, "What have I gotten myself into? Things are worse than I imagined. Can I ever turn this company around given all of the known and unknown problems? Can I make it profitable?"
Care Unlimited was a start-up in Kolkata, India, engaged in providing support and services to elderly customers whose children lived far away. The venture, four years old, had garnered a clientele who strongly believed in supporting the well-being of their elderly parents. The start-up endeavoured to change the experience of old age by supporting its customers in various chores, and by meaningfully engaging them in the various activities they enjoyed. By April 2018, the set-up had captured a share of the early market in Kolkata; however, the concept that the needs of elderly people extended beyond a need for emergency services had yet to gain wide-scale acceptance. Should the founder consider adding emergency services? Or should he continue to focus on driving home the point that elderly people needed far more than emergency services? Was there a need to explore alternative ways of earning revenue and diversifying his business?
Headquartered in Gurgaon, India, Vaidam Health Private Limited (Vaidam) was an online medical-value-travel (MVT) facilitator that had been hosting overseas patients travelling to India for medical treatment since its inception in January 2016. It had partnered with over 100 world-class health care facilities across the major cities in the country. Given the improved attractiveness of the MVT services, the founders of Vaidam realized that they would face tough competition in their attempt to further leverage this massive unaddressed business opportunity. Although they had successfully bootstrapped and come this far, they now had a tougher challenge to face in May 2018: as an MVT facilitator, how could Vaidam continuously offer value to medical travellers so that they would continue to choose Vaidam to plan their treatment and travel to India?
In 2016, Somany Ceramics Limited (SCL), a ceramic tile manufacturing company in India, needed industry-ready workers to meet the company’s expansion plan; however, the whole ceramics industry was affected by a shortage of labour. Most of the technically competent ceramics and construction personnel in India preferred to work in the construction industry or join other sectors such as information technology. The company’s human resources team needed to develop a strategy to hire an adequate number of skilled employees and to retain them as the competitors also attempted to take advantage of growth opportunities in the global ceramics industry.
In 2015, the assistant general manager at JSW Steel Ltd. (JSW), one of India’s largest steelmakers, faced a dilemma. Should JSW continue to transport the company’s end products to clients, or should JSW instead outsource the transportation to a third-party provider? Outsourcing would ensure timely delivery but would increase the cost. Another option was to pay an agency a premium for sharing information on the availability of Class I and Class II barge vendors. Class I barges were more reliable in terms of on-time and damage-free delivery, whereas Class II barges had a smaller capacity, were less reliable, and had a greater risk of goods being damaged, for which JSW could face both monetary and non-monetary losses. Although the Class I barges led to higher payoffs, the outsourcing option offered a fixed and relatively lower payoff. The assistant general manager’s objective was twofold: to meet the customer requirements in time and to benefit JSW financially. How should he decide which option to pursue?
Brown Bag Films produced programming for Disney, Amazon, BBC, and Nickelodeon, reaching an audience of more than 100 million pre-school children around the world. This case examines the development of Brown Bag Films, which began in 1994 as a partnership between two art school students in Ireland who had a passion for making animated children's films-but no business plan, and only a few thousand pounds of borrowed funds. Early successes like an Oscar nomination meant name recognition, but the company had a decidedly non-linear track before it became known for producing popular animated series. The company pivoted a number of times, yet still saw itself as a start-up, a technology venture that was also a creative venture, at the cutting edge of 3D animation. Topics discussed include strategic thinking, mission alignment, management training, and corporate culture.
Brown Bag Films produced programming for Disney, Amazon, BBC, and Nickelodeon, reaching an audience of more than 100 million pre-school children around the world. This case examines the development of Brown Bag Films, which began in 1994 as a partnership between two art school students in Ireland who had a passion for making animated children's films-but no business plan, and only a few thousand pounds of borrowed funds. Early successes like an Oscar nomination meant name recognition, but the company had a decidedly non-linear track before it became known for producing popular animated series. The company pivoted a number of times, yet still saw itself as a start-up, a technology venture that was also a creative venture, at the cutting edge of 3D animation. Topics discussed include strategic thinking, mission alignment, management training, and corporate culture.
Brown Bag Films produced programming for Disney, Amazon, BBC, and Nickelodeon, reaching an audience of more than 100 million pre-school children around the world. This case examines the development of Brown Bag Films, which began in 1994 as a partnership between two art school students in Ireland who had a passion for making animated children's films-but no business plan, and only a few thousand pounds of borrowed funds. Early successes like an Oscar nomination meant name recognition, but the company had a decidedly non-linear track before it became known for producing popular animated series. The company pivoted a number of times, yet still saw itself as a start-up, a technology venture that was also a creative venture, at the cutting edge of 3D animation. Topics discussed include strategic thinking, mission alignment, management training, and corporate culture.
Supplement to case W17363. In 2015, the assistant general manager at JSW Steel Ltd. (JSW), one of India's largest steelmakers, faced a dilemma. Should JSW continue to transport the company's end products to clients, or should JSW instead outsource the transportation to a third-party provider? Outsourcing would ensure timely delivery but would increase the cost. Another option was to pay an agency a premium for sharing information on the availability of Class I and Class II barge vendors. Class I barges were more reliable in terms of on-time and damage-free delivery, whereas Class II barges had a smaller capacity, were less reliable, and had a greater risk of goods being damaged, for which JSW could face both monetary and non-monetary losses. Although the Class I barges led to higher payoffs, the outsourcing option offered a fixed and relatively lower payoff. The assistant general manager's objective was twofold: to meet the customer requirements in time and to benefit JSW financially. How should he decide which option to pursue?
Care Unlimited was a start-up in Kolkata, India, engaged in providing support and services to elderly customers whose children lived far away. The venture, four years old, had garnered a clientele who strongly believed in supporting the well-being of their elderly parents. The start-up endeavoured to change the experience of old age by supporting its customers in various chores, and by meaningfully engaging them in the various activities they enjoyed. By April 2018, the set-up had captured a share of the early market in Kolkata; however, the concept that the needs of elderly people extended beyond a need for emergency services had yet to gain wide-scale acceptance. Should the founder consider adding emergency services? Or should he continue to focus on driving home the point that elderly people needed far more than emergency services? Was there a need to explore alternative ways of earning revenue and diversifying his business?
In 2016, Somany Ceramics Limited (SCL), a ceramic tile manufacturing company in India, needed industry-ready workers to meet the company's expansion plan; however, the whole ceramics industry was affected by a shortage of labour. Most of the technically competent ceramics and construction personnel in India preferred to work in the construction industry or join other sectors such as information technology. The company's human resources team needed to develop a strategy to hire an adequate number of skilled employees and to retain them as the competitors also attempted to take advantage of growth opportunities in the global ceramics industry.
Headquartered in Gurgaon, India, Vaidam Health Private Limited (Vaidam) was an online medical-value-travel (MVT) facilitator that had been hosting overseas patients travelling to India for medical treatment since its inception in January 2016. It had partnered with over 100 world-class health care facilities across the major cities in the country. Given the improved attractiveness of the MVT services, the founders of Vaidam realized that they would face tough competition in their attempt to further leverage this massive unaddressed business opportunity. Although they had successfully bootstrapped and come this far, they now had a tougher challenge to face in May 2018: as an MVT facilitator, how could Vaidam continuously offer value to medical travellers so that they would continue to choose Vaidam to plan their treatment and travel to India?