Analytics in India: Going from Prehistoric to Predictive

By Nitin Aggarwal, Associate Vice President-Data Analytics and Divya Sharma, Senior Analyst at 'The Smart Cube'
What India’s retail market is anticipated to grow from $630 billion to $1,200 billion during 2015–2020, driven by a rise in income levels, influx of youth in the workforce, and increase in nuclearisation and urbanisation of families. At the same time, the organised retail industry is facing new challenges from today’s value-conscious and digitally connected consumers, regulatory barriers, and lower bargaining power with suppliers, and strong competition from e-commerce companies. To compete effectively, organised retailers need to re-imagine how they create and capture value.

According to the Harvard Business Review, companies in the top third of their industry in the use of data-driven decision-making are on average six percent more profitable than their competitors. Scalable analytical models can help organised retailers to readjust their value propositions to remain at the top of their game.

Analytics: A Comparison between Indian and Global Retailers

Analytics in India is still at a nascent stage. Historically, the Indian retail sector has not operated in a manner conducive to the use of analytics. Customers were mostly anonymous, and retailers had much less data on factors such as buying behaviour, product stacking, accessibility and promotions. This kind of evolution history has slowed down the pace of incorporation of analytics in the Indian retail sector. However, a transformation has been witnessed in recent years. Indian IT companies are now offering analytics solutions tailored to the retail industry.

For instance in May 2015, Future Group, which owns retail brands such as Food Bazaar, Big Bazaar, Home Town and Nilgiris, partnered with an analytics firm, which provides them with real-time actionable information for store-level decision-making. In July 2015, fashion retailer Shoppers Stop announced plans to adopt digital retail channels. Additionally, in December 2015, Tata Group’s Trent announced plans to use analytics to better understand its customers and develop in-house analytics in the near future. The Indian retail industry is still in an accelerated growth mode-it is using a lot more customer and marketing analytics to grab market share even at the cost of profitability. On the other hand, competition in mature retail markets is typically on the basis of profitable sustainable growth. Thus, there’s higher focus on supply chain optimisation and social media monetisation to
increase margins.

With regards to practices of analytics in the global market, retail giant such as Walmart is on a different plane with their advanced approach towards analytics. Walmart Labs analyses every action of its consumers in its stores, online, on its Smartphone app and on social media. This data can be then used for multiple end uses from decoding trends on Twitter to analysing how weather conditions affect buying patterns. On the other hand, organized Indian retail is yet to foray into the designing of such a scalable, seamless and connected customer journey across
multiple channels.

Catching Up Strategies for the Future

Retailers need to start moving from descriptive to predictive analytics. While building scalable technology systems for analytics is a continuous process, one can start by making improvements in key functional areas where analytics can be used in specific applications.

Marketing Analytics: From Product Discovery to Purchase

Calculating organized retail’s return on investment from marketing spend should be an outcome of resource allocation based on marketing analytics, which will reveal misallocations, and areas and magnitude of opportunities. Hence it is important to push more customised promotions towards targeted customers through location data available on social media platforms. In addition, utilising the Smartphone revolution in India by using mobile analytics and creating apps is vital.

Customer Analytics and Operational Analytics

By analysing transactional and social media data for customer segmentation, retailers can use campaigns based on consumer preferences. Customer analytics is useful in renewing store formats by enabling physical stores to evolve to create “experience “stores with sleeker and customised retail store’s layout. Other application is identifying optimal product mix and pricing by gauging customer preferences, which can also assist in preventing cannibalisation. Operational analytics can be used to reduce the cost of carrying goods and inventories by forecasting demand based on past experiences and the current market situation. In addition, it helps avoiding overstocking or under stocking, reduces the efficiency deficit of offline retail supply chains compared with those of online retail. Operational analytics can also help in stock keeping unit rationalization and mitigation shrinkage and wastage.

Conclusion: Begin by Assessing your Requirements

Before delving into specific analytics solutions, organised retailers should start by assessing their organisation’s needs from an analytics program. By analysing your organisations data needs, assessing the importance of data management speed, understanding organisational risks, and security and government requirements, and examining how precise the data needs to be, executives will be better placed to make the correct analytics-related decisions for their companies.

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