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Kaleidofin was co-founded in 2017 by Puneet Gupta and Sucharita Mukherjee; former CFO and CEO of IFMR (Institute for Financial Management and Research) Holdings Pvt Ltd. As part of their roles at IFMR, Gupta and Mukherjee focused on designing products and developing technology to push for financial inclusion. In their field interactions, the co-founders had an epiphany of the challenges faced by people while trying to save towards important life goals. They saw an opportunity in the large segment of financially under-served people in India and quit their jobs to start Kaleidofin. Kaleidofin was conceptualised as a digital platform that offers customised financial solutions to help customers meet their life goals. The start-up partnered with mutual fund companies for solutions on one hand and network partners (NGOs, microfinance organizations, cooperative banks) on the other for access to their existing customers.

Kaleidofin grew from 50 customers in January 2018 to 15,000 customers by March 2019. Aiming to grow to 1 million customers in the next 30 months Kaleidofin faces a dilemma about its future course. The start-up could continue to grow by expanding its current target segment which is the low-income households and preserve its vision at the risk of increasing costs. The second option would be to look at other potential target segments, such as, middle-income households and risk diluting their vision. The case study highlights the unique customer-centric model of Kaleidofin and the need for start-ups to understand the value proposition of their products/services.

The case is meant to be used in an introductory course on Data Visualization or Analytics in either a business school or any other context where students have had some exposure to management topics. This case primarily focuses on introducing students to the basic philosophy and techniques of exploratory data analysis (EDA). The Steaming Mug, a US based hot beverage chain is provided as a context for engaging in a hands-on exploratory data analysis exercise to understand the strengths and weaknesses of the company's operations and performance. The learning from exploring this case will help students understand that many different insights can be drawn from the given data. The students will understand that there is no “one correct path” for this analysis. The values of getting multiple groups to present their findings is that students realise that there are many other angles that could have been explored, than the path they had gone down.

An asset management company must replace the manager of its two signature mutual funds, who is about to retire. Two candidates have been short-listed. The management team is divided and cannot decide which of the two candidates would make the better mutual fund manager. The retiring manager presents a linear regression model to examine success factors of mutual fund managers. This linear regression is the starting point for the subsequent analysis.

Application of linear regression analysis to analyze the performance of mutual fund managers.

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

This case study outlines the marketing, strategic and organizational issues facing the ever-expanding agri-inputs market in India, through the perspective of Agroy – an agri-products company. This case can be used to assist in the teaching courses such as marketing management, rural marketing, business strategy, operations and logistics management, among others, for students of MBA or other specialized courses in management. The case has been developed to make students aware and to understand the arduous nature of setting up a company catering to the huge Indian agri-inputs market. This case delves into the complexities of marketing in rural India that is characterized by low technological awareness, low volumes of digital transactions and immense language barriers. The Indian agricultural market is huge and has undergone a considerable amount of change owing to competition among multinational companies and traditional local micro-retailers. This case discusses the various challenges faced by multinational companies in entering India and how they need to strategize to modify their Western model of a distribution channel which faces huge challenges when put to test in India. Specific learning outcomes include: the case study would help students to comprehend the new business strategies that an MNC could adopt in emerging markets. Some companies work on changing traditional and conventional value chains of activities to fit the emerging market customer’s best and hence companies needs to figure out a unique business model to compete in emerging markets. This case study gives readers the opportunity to think about strategy in an uncertain environment. The case illustrates the challenges associated with innovating new business ideas that would help the company serve a greater number of people from a diverse background. It highlights the importance of thinking about real options, a portfolio of projects and the type of organizational structure required to tackle the uncertainties associated with foreign companies aiming to enter the Indian market. It also explores marketing and distribution issues – which are the type of customers to target and which are the suitable geographic areas with suitable linguistic compatibility in which there shall be ease in doing business. Finally, it is an avenue for students to think about the changes necessary throughout the distribution channel to successfully implement and commercialize a project in rural India. The case is intended to work well as a learning tool for strategy implementation where uncertainty is inherent and as an application to lectures on real options and risk or for discussions related to marketing and distribution channels and its challenges.

The Indian agricultural market plays an important role in India’s economy having a staggering 58 per cent of rural households depending on it as the principal means of livelihood. However they have very small landholdings, and hence, they find it difficult to order either large quantities or in bulk, as a result of which the cost of agricultural inputs gets enhanced. Agroy, an MNC, is one of the many companies that have stepped in to bridge this gap by trying to tap into the huge agricultural market. Agroy aspires to be the “UBER of agriculture.” Agroy is a cloud-based buying platform for farmers to buy agri-inputs efficiently at scale and at the best price from around the world. With big data and smart farming, the company aims to enhance farm sustainability and productivity. Agroy’s competitors like Agro Star and Big Heart also have similar business models and hence the competition is stiff. The three debatable questions that the case poses are: Will Agroy be able to shatter the age-old loyalty that Indian farmers have toward local retailers and other Indian companies that have an existing strong foothold in the market? Will similar distribution models as practiced in developed Western countries work in India, given the distribution challenges in deep rural Indian hinterland? Will Agroy be able to create sustainable business models by marketing agri-inputs at low prices in India?

MBA in courses such as entrepreneurial marketing, strategic marketing, agricultural marketing.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 8: Marketing.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy

Faced with declining market share and sales, Natura, Brazil’s second-largest brand in the cosmetics, fragrances, and toiletries market, expanded its customer reach by moving from a direct-sales company to a multichannel company. In 2014, Natura added online catalogs, physical stores, and drugstores to its well-established direct-selling model, but the results were disappointing. Between 2014 and 2016, three different Natura CEOs attempted to lead the company in the strategic transition to focus less on the direct sales consultants and more on reaching the end consumers directly with multiple channels and touchpoints. In October 2016, the company’s board appointed its former commercial vice president, João Paulo Ferreira, as the most recent CEO. Ferreira’s challenge was to find the right balance between the direct-selling and other channel formats to market Natura, thus enabling it to thrive in the face of intense competition in the beauty and personal care market in Brazil.

This case is intended to be part of a first-year MBA marketing course, or a second-year elective in advertising, integrated marketing communications, market research, or marketing analytics. The case provides students with examples of two real advertising experiments and the challenges involved in executing the experiments. It allows for a discussion of the need for advertising experiments, and also, at a more general level, the need to measure the return on marketing. Biases surrounding the field experiments allow for a discussion of the problems with establishing a causal relationship between advertising and sales.

Cardagin, a new start-up in the increasingly competitive space of consumer/merchant apps for smartphones, is reaching an inflection point: decisions it makes at this stage can determine whether it becomes a national presence or an also-ran. The CEO needs to demonstrate the value of Cardagin's service to retailers and consumers in compelling ways. The case provides students an opportunity to analyze a new digital venture, explore expansion options, and evaluate the challenges in working with many small businesses, each with its own idiosyncrasies.

After experiencing long, multiyear surges and slides in past decades, by summer 2013, the dollar had been range-bound against the euro. In this case, by assessing potential capital flows, students consider whether global currency market trends would propel the dollar higher, or if the past few years were just a pause in a much longer dollar depreciation episode. Suitable for both core and elective MBA courses in global financial markets and international finance, this case explores factors pointing to further euro appreciation and to others favoring the dollar. Sorting through mounds of evidence is necessary before forecasting the exchange rate's likely path. Filtering that evidence requires thinking about FX markets, prospective monetary policies, and past and prospective international capital flows.