NMIMS - Fundamentals of Big Data & Business Analytics

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NMIMS

PGDIT - Semester 3

Fundamentals of Big Data & Business Analytics

Q1. The healthcare industry is inundated with massive volumes of data generated each minute. With the adoption of electronic health, mobile health and wearable technologies, this is poised to increase dramatically over the next few years. This comprises of data being generated by patients in the form of reports generated by the diagnostic labs, the wearable devices an individual wear that constantly monitor his vital stats, body patches, data from medical insurance companies to name a few. Would this data be classified as Big Data? If yes, what are the characteristics of Big Data? Explain any two Big Data Techniques. (10 Marks)

Q2. Digital music is gaining firmer ground in India. 56 percent of digital music revenue in Asia comes from music streaming. Players like Gaana, Hungama, Saavn, Wynk etc. offer users to stream music online and save songs offline with a premium subscription. They have grabbed a significant share of the audience who have given up the traditional methods of downloading music to streaming it online. Advertisers and telecom providers have also joined the bandwagon. The primary reasons for this growing popularity can be attributed to the rise in the number of digital natives, improved internet connectivity, more localized curated song lists, personalization of content, competitive pricing, huge library, availability across different platforms, simple user interface and sharing digital music with others across social platforms. How can the music industry use analytics to predict future hits, describe current trends and recommend best offerings for customers? (10 Marks)

Q3. Retailers use analytics in a variety of ways. Specialty retailers use video analytics to study customer paths and behavior, helping them to design more effective store layouts. Big Box retailers invest in Wi-Fi networking and new mobile way-finding apps to help customers navigate through large stores or malls, getting them to desired products faster. Resorts and hotels are investing in mobile analytics to gather shopper information from their retail spaces. Mall operators are using the network to track social media and shopping patterns, and delivering this value-add information to tenants. Grocery and fast-moving goods retailers are utilizing video analytics for traffic and conversion analysis, and then using the same information to integrate workforce management and re-align staffing based on traffic trends. Specialty retailers are using social sentiment analytics to improve “voice of the customer” feedback to assess overall brand status and the launch of new products, services, or offers. Retailers can use analytics tools to measure traffic, wait times, and queue lengths, proactively anticipating resource demands across the store. For example, front-end staffing demand in grocery can be anticipated using a combination of real-time traffic counting, trip time data, and data on staff on hand. Resources are thus dynamically allocated based on real-time information, improving productivity of labor hours and improving customer satisfaction. Through presence and location-based mobility analytics, retailers pinpoint the location of opt-in shoppers when they are close to a store location. With personalized reminders or discount offers sent directly to their smartphones, consumers are more motivated to visit the store if they are nearby. Combining social and mobile analytics with loyalty information, retailers can create personalized, more relevant engagements with shoppers. For example, say that a customer enters the shoe department. Their store history shows that 60% of past purchases included a coupon. The retailer can improve the chance of another sale by sending, in real time, a special offer or communicating through Twitter about a current promotion. Such communications change the customer/store relationship from transaction-based to more value-based, creating more sustainable brand loyalty.

(Source: Beyond Big Data: How Next-Generation Shopper Analytics and the Internet of Everything Transform the Retail Business. https://www.insight.com/content/dam/insight-web/en_US/article-images/whitepapers/partner-whitepapers/beyond-big-data-how-next-generation-shopper-analytics-and-the-internet-of-everything-transform-the-retail-business.pdf)

a. Give an example of how an Indian retailer has used analytics to improve customer experience within the store. (5 Marks)

b. Give an example of a how an Indian retailer has used social and mobile analytics for better customer engagement. (5 Marks)

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