Fantasy sports is the fastest growing online gaming industry worth several billion dollars. An important debate associated with fantasy sports across the world is whether it is a game of skill or chance. In India, the online gaming laws are not very well defined and hence there is always a threat to fantasy sports companies of getting into legal battles. While the game of skill is permitted by Indian laws, game of chance is strictly prohibited. Indian courts have recognized that no game is a game of pure skill or game of pure chance. When there is a chance involved, the Indian courts decide based on the dominant factor test that requires determining whether skill is dominant, or chance is dominant factor for a given instance. Ramasubramanian Sundararajan (Ramsu), the head of AI at the Cartesian Consulting was convinced that the debate around whether the dominant factor is skill or chance should be settled using data. With this objective, Ramsu approached Dream 11, one of the largest fantasy sports companies in India. Dream 11 has created fantasy games for sports such as cricket, football, hockey, kabaddi, and the National Basketball Association (NBA). Being a cricket fanatic, Ramsu wanted to check whether fantasy games played in cricket involves skill or not. Ramsu was convinced that several hypotheses can be designed and tested to check whether fantasy sports is skill dominant or chance dominant.
Eureka Forbes, part of the conglomerate Shapoorji Pallonji Group, is currently one of the world's largest direct sales company known for its water purifier brand Aquaguard with a turnover of more than INR 30 billion. The company is estimated to have a customer base of 20 million across 53 countries. The company's distribution channel includes a direct sales force of dealers, institutional channels, business partner network and a rural channel across 1500 cities and towns in India. The company's previous customer acquisition model ensured that interested customers were individually visited for demonstration of the product and for completion of purchase. While this made the company a household name, it kept the acquisition costs on the higher side. With the imminence of online retailing, the brand had been taking steps to establish their digital presence and build a stable online sales channel. The company website (www.eurekaforbes.com) attracts online traffic from various sources such as organic searches, google ads, email campaigns, etc. The company has started to use this click stream data to build a rich database of visitor acquisition factors and behavioral variables such as session duration, device category, pages visited, lead forms filled, etc. using the Google Analytics Reporting API. The company identifies these visitors as potential customers and is actively deploying remarketing campaigns with optimism to convert them. While these campaigns have shown some success, they have resulted in substantially high retention costs. The business goal is clearly defined for the company - they want to target potential customers while keeping the cost per lead (CPL) as low as possible. For Kashif Kudalkar, the Deputy General Manager for Digital Marketing and Analytics, the task is to achieve better conversion at lower costs. This is achievable when the target audience is narrowed down to a sizeable number for remarketing campaigns.
Janarthanan Balasubramanian, Division Head, Information Technology and online Communications at ISKCON wanted to understand the visitor feedback so that appropriate measures can be taken to improve the visitor experience at ISKCON. The primary problem at hand for Janarthanan was to reduce the existing manual effort for his team. Currently three resources are involved in collecting the reviews from social platforms and labeling each review into one of the four classes, viz. positive, negative, neutral and mixed. Two other resources convert the reviews from paper feedback forms/feedback registers placed at different points inside the temple, to an Excel file. The team begins its day by manually collecting, labeling and collating the reviews in an Excel file. At the end of the day, these labelled reviews would be stored in the database. At the end of the week, the total count of reviews for the four classes, viz. positive, negative, neutral and mixed was calculated to understand the overall sentiment. This was an extremely time-consuming manual process from data collection, that is, manually copy pasting the comments from social mediums to data collation and labeling. Janarthanan wanted his team to spend time and effort on analyzing the data and working on remedial actions. He wanted to understand the issues/topics that ISKCON should work on, rather than manually classify reviews and get the count of each review type. Janarthanan felt that getting only the split of positive/negative/neutral/mixed classes is not enough to draw inferences.
Jayalaxmi Agrotech (JAT) is a company based out of a small village called H.B. Halli in Bellary district of Karnataka, which helps farmers in availing correct and timely information at their fingertips using mobile technology. JAT created a mobile app which can be used to access information about various crop varieties, diseases, fertilizers, and pesticides and other details on irrigation, micronutrients, etc. This app pushes data on almost real-time basis to a server regarding the features accessed by the farmers from the app. This data can be analyzed to assess the usage of the app and some of the attributes can act as proxy data to understand prevalent diseases and the varieties of crops grown in specific areas. Anand, the founder of JAT, wanted to understand this data and gain some valuable insights which could help farmers with decision making. The major dilemma for Anand was to either prove or disprove several claims about plant diseases that were prevalent among the farmers.
Jayalaxmi Agro Tech (JAT), a company based out of Bellary in Karnataka and co-founded by Anand Babu, strives to keep the Indian farmer informed about the modern best practices, thereby boosting the agricultural yield. The company's flagship product is a suite of crop-specific mobile apps in several regional languages with heavy emphasis on audio-visual content to break the language and literacy barrier prevalent in rural areas. The farmer is empowered with the right information at the right time to make agriculture sustainable and more profitable. JAT intended to collect data on sericulture (rearing of silkworms for producing raw silk) to improve income of silk producers. Karnataka is one of the largest producers of raw silk in India. Sericulture requires less investment but offers high returns if done correctly. Sericulture also involves cultivation of mulberry trees, the leaves of which are used to feed the silkworms. The yield of sericulture is heavily dependent on the quality of inputs such as the type of silkworm breed used, quality of the mulberry leaves and environmental conditions of the silkworm rearing house. Jayalaxmi Agro Tech collected farmer level data on sericulture practices in the districts of Belagavi, Bellary, Chikballapur, Mandya, and Tumakuru in the state of Karnataka. The company wanted to analyze the data collected to gain insights so that they could make grassroot level impact by fine tuning sericulture as an occupation. These insights could possibly help towards building better policy interventions to improve the welfare of sericulture farmers.
iD Fresh Food (India) Private Ltd., is a leading ready-to-cook and eat packaged food company serving several cities in India. The company is known for its popular product, Idly-Dosa batter that it sells through retail outlets. iD started with packaging Idly-Dosa batter and has since diversified into Malabar Parota, Wheat Parota, Chapati, and Chutneys. In 2017, iD Fresh was a 1000+ member team with seven factory locations and eight offices - two plants in Bengaluru, one each in Chennai, Mumbai, Hyderabad, Mangalore, and Dubai. They manufactured more than 50,000 kg of Idli-Dosa batter per day which is equivalent to a million idlis. The company produced and sold nearly 15 ready-to-eat packaged food products and their flagship products include Idli-Dosa batter, Mini Parota, Malabar Parota, Whole Wheat Parota, Whole Wheat Chapati, and Whole Wheat Junior Parota. iD Fresh Food was in expansion phase and adding several outlets to its distribution network. Since all the products sold by iD Fresh Foods had short shelf life, anywhere between 4 and 7 days, forecasting demand accurately is important. iD would like to be in a state where there will be a greater degree of predictability in its operations. Ideally, they would like to know how much of each SKU should be loaded into each vehicle for the following day when a salesman starts his beat journey. The forecast for each store, based on past performance of each store in each beat, should be fairly accurate. This would then enable a macro-view of the business operations over a month and consequently help in production planning and operations for the future.
Managed word-of-mouth and viral marketing have emerged as one of the biggest trends of the decade. Every brand, big or small, wants its content to go viral. The backbone of successful campaigns is not limited to content or influencers who can make the message go viral. Marketers need to think of value creation at every stage. Jiten Gajaria, the convener of the social media cell of Bharatiya Janata Party (BJP) Maharashtra, implemented a marketing transformation program for his political party while campaigning for the 13th Maharashtra Legislative Assembly Election. He gained consumer insights, calibrated market segments, devised communication strategy, recruited and managed human resources, designed campaign implementation process and developed capabilities to execute it. The case details how Gajaria rolled out a successful influencer marketing program to create WOM communication on WhatsApp. It can serve as a classic example of devising a cost-effective customer outreach program where total marketing expense was reduced by about 199%.
VMW is a leader in software virtualization with approximately USD 6.5 billion annual revenue. VMW sells Workstation that can be bought online (store.vmware.com) and is used for running Mac on Windows. Workstation forms a significant portion of store revenues and most of it is bought online. There is rich digital/clickstream data for the visitors which can be combined with their past purchase history and other offline features as well. The business would like to increase sales of the product by targeting the right customers and needs a propensity model to be built using machine learning that can target the right set of customers. Michael Butler, the WW head of the store wants to leverage Parag's data sciences team to help him target the right workstation prospects that visit the store. A business conversation between Michael and Parag is followed by a technical discussion between Ravi, the data scientist and Parag. The following are the key questions that Ravi seeks to answer: -Cross-validation and evaluation in the context of huge imbalance in the data -Feature selection techniques -Communicating internal results such as lift curves back to the business -Different modeling approaches that can be followed -Interpreting the results for business decision making
VMware (VMW) is a listed software company with headquarters in Palo Alto, California selling products in the Software Defined Data Center that supports multiple devices, apps, and cloud to create an enterprise-ready cloud infrastructure. The company exclusively caters to business customers, that is, in the B2B environment. The company is characterized by 100% digital supply chain which implies that all products are downloadable from the website (www.vmware.com). The company also promotes these products online. Varied individuals across companies worldwide visit the site to familiarize themselves with the products and their features before making a decisive purchase. Along with the overview and use of the product, there are various customer-interaction triggers or ''digital assets'' that are shown to the VMW audience. These include triggers such as hands-on-learning, seminar/webinar registration, downloads, etc. Kiran R, the Director of the Data Science & Analytics team at VMW wants to know the optimal order of digital actions to be pushed towards customers to engage them effectively. Kiran's team has a rich source of online and offline available to model user's response to each of these digital assets. Kiran realized that the data is highly imbalanced and hence should be handled carefully. They wish to come up with a multinomial classification model for this purpose. Kiran decided that the model should fulfil the following objectives: 1. Determine the right order of digital assets to display to an individual e-mail id. 2. Since the website would like to target groups of e-mail ids, come up with a set of segment rules to identify top individuals for a digital asset and to target them with personalization on the website. 3. Have substantiated marketing and sales implications.
MCA Technology Solutions Private Limited was established in 2015 in Bangalore with an objective to integrate analytics and technology with business. MCA Technology Solutions helped its clients in areas such as customer intelligence, forecasting, optimization, risk assessment, web analytics, and text mining and cloud solutions. Risk assessment vertical at MCA technology solutions focused on problems such as fraud detection and credit scoring. Sachin Kumar, Director at MCA Technology Solutions, Bangalore was approached by one his clients, a commercial bank, to assist them in detecting earnings manipulators among the bank's customers. The bank provided business loans to small and medium enterprises and the value of loan ranged from INR 10 million to 500 million. The bank suspected that its customers may be involved in earnings manipulations to increase their chance of securing a loan. Saurabh Rishi, the chief data scientist at MCA Technologies was assigned the task of developing a use case for predicting earnings manipulations. He was aware of models such as Benford's law and Beneish model used for predicting earnings manipulations; however, he was not sure of its performance, especially in the Indian context. Saurabh decided to develop his own model for predicting earnings manipulations using data downloaded from the Prowess database maintained by the Centre of Monitoring Indian Economy (CMIE). Daniel received information related to earning manipulators from Securities Exchange Board of India (SEBI) and the Lexis Nexis database. Data on more than 1200 companies was collected to develop the model. MCA Technology believed that machine learning algorithms may give better accuracy compared to other traditional models such as Beneish model used for predicting earnings manipulation.
Bigbasket was India's largest online grocery and food store established in 2011 by a group of entrepreneurs Hari Menon, Vipul Parekh, V S Ramesh, V S Sudhakar, and Abhinay Choudhari. In 2016, Bigbasket sold more than 18,000 products and 1,000 brands operating across 12 Indian cities. Online grocery market in India has been small, but a rapidly growing segment. According to "The Retailer" Ernst and Young's publication in consumer products and retail sector, during July-September 2015, India was among the top-10 food and grocery markets in the world, with an estimated size of INR 22.5 trillion (approximately USD 350 billion). The market has grown at 10-12% CAGR between 2010 and 2015, with food and grocery being the largest segment, accounting for close to 60% in 2015 alone. The protagonist of the case, Pramod Jajoo, Chief Technology Officer, at Bigbasket was trying to solve two problems frequently encountered by customers of online grocery stores. It was estimated that about 30% of Bigbasket customers place orders through smart phones. Unlike other e-commerce companies such as Amazon, Bigbasket customers place order for several products in a single order, sometimes as high as 80 in one order depending on their purchase frequency. When the basket size is high, using smart phones to place order is challenging. Also, it is a common phenomenon that customers forget to place order few grocery items which may result either in placing additional orders or customers purchasing those products from neighborhood stores resulting in a financial loss to online grocery stores. Jajoo and his team wanted to create a "Smart Basket" that would make placing orders easier for their customers and "Did you forget?" feature that would identify the items the customer may have forgotten to order.
Siddharth Sinha is the CEO of an apparel retailer WE SELL STYLE (WSS). The retail chain was set up in 2008, housing more than 100 brands. In 2015, they operated over 200 stores in all four regions of the country. They primarily focused on providing good quality fashion at a remarkably low price. Markdown planning has been an important aspect of the apparel business. It is important to understand that the demand for fashion apparel is seasonal - affected by current fashion, variations in the seasons, festivals and hence difficult to estimate. An apparel retailer could go off target - either by overestimating or underestimating the demand, with overestimating being prevalent. The ordering-manufacturing-stocking cycle is easily a 6-month cycle before the selling actually starts; with an expectation to improve sales year on year, the procurement team buys more, making an increase in the variety of colors and styles to offer more to the consumer. However, not all styles sell as expected, leaving higher than expected stocked inventory, which requires an impetus to sell. The impetus in the industry comes in the form of ''end of season sale (EOSS)''. Decision on the percentage of markdown for EOSS is one of the most critical tasks for an apparel retailer. This activity starts months ahead of the EOSS. The product team and the planning team sit together and come up with an EOSS plan at the style level. In the decision process, procurement and planning team use their domain expertise and judge the performance of style using metrics such as rate of sales, full price sell-through, inventory left, and more. The key decision is to quantify the degree of non-performance of styles that did not sell as forecasted and by how much to markdown for the EOSS.
Arthur Andersen collapsed in May 2002 and a significant part of Arthur Andersen India merged with Ernst & Young. Bobby Parikh, Mukesh Butani, Rajiv Dimri, Sanjay Mehta and Ajay Mehra, all of whom were senior leaders at Arthur Anderson, had joined Ernst & Young after the merger in various roles; however, they left one by one to establish a firm in their chosen practice areas, which would be differentiated by quality of thinking and enable clients to experience a different way of engagement execution. Thus, BMR Advisors was started to provide high quality professional services; wherein it wanted to differentiate itself and compete solely on quality. BMR started with services in three areas - Tax, Mergers and Acquisitions (M&A) and Risk Advisory - a unique combination which could solve any business problem. Audit services and management consulting were deliberately not offered. BMR focused on maintaining a low leverage ratio, that is, the number of team members per partner, to ensure higher amount of partner time on each assignment. BMR Advisors thus far had achieved success with its low leverage ratio, that is, partner to team members' ratio, which in turn had ensured higher quality of strategic inputs to complex client engagements and higher amount of partner face time with clients. With increased growth in recent times, maintaining a lower leverage ratio was becoming a challenge, since growth entailed more engagements and needed additional team members, which in turn diluted the leverage ratio. New partners had joined from other organization and ensuring a seamless integration of culture was another challenge. Developing the next set of leaders to take over the mantle from the founding partners was one more challenge to be dealt with. Besides these unique challenges, BMR advisors also faced regular challenges such as talent war, technology, undercutting by competition, etc.
Flipkart, the poster child of Indian e-commerce, was an early entrant in the nascent Indian e-commerce market and quickly established itself as the leading company in this space. Flipkart has grown into an online retail giant, valued at over USD 15.2 billion as of 2015. Flipkart has been selling over 30 million products from more than 50,000 sellers in 70+ categories as well as has 30 exclusive brand associations with an in-a-day guarantee in 50 cities and a same-day guarantee in 13 cities. Flipkart was 33,000 people strong and had over 50 million registered users with over 10 million daily visits and 8 million shipments per month. Flipkart has been putting in much effort and emphasis on the use of Analytics in every aspect of decision making. Headed by Ravi Vijayaraghavan, the analytics team had over 100 data scientists in 2015. Customer churn is a major concern for Flipkart since it has direct impact on Customer Lifetime Value (CLV). CLV is an important measure to differentiate customers, which can further help the organization manage them effectively. The main challenge in calculating the lifetime value of customers of e-commerce companies such as Flipkart is that the exact life of the customer is unknown owing to data truncation, that is, the actual point in time of customer churn, which may not be identified in e-commerce since there would be no prior communication from the customer about the churn. Hence, traditional models of CLV calculation may not be appropriate for e-commerce companies such as Flipkart.