Ten years ago, if a marketer told you he has a mine of data on his consumers what would you imagine would be the source and size of this information? Well, it would be a compilation of data the consumer would volunteer to offer while filling up her credit/debit card application form, a snapshot of her mobile phone usage pattern and may be the information captured by the odd loyalty card offered by her favourite retailer or airline.
Today when he says he has data on the consumer, you can be sure it’s a veritable treasure trove. In the digital age, there is a string of footprints left behind by every one of us: If a consumer swipes her card somewhere you know what she is up to. If she browses through online stores, you can pick up clues about her shopping behaviour. If she downloads your brand application on her mobile, you know how much she is ready to splurge on value add-ons. Along with third-party information on web interactions, credit card transactions, demographics and the like, this data can help marketers paint consumer portraits, get a head start on the competition and win over markets in the process.
Welcome to the world of big data. Managed correctly, big data is a powerful resource to improve decision-making for every business. More so in retailing. The reason is simple: the more information a retailer has on his consumer, the better he is able to predict her buying behaviour. And, therefore, tailor his products and offers to suit her needs. In short, improve his chances of making a sale. “The marriage of mathematics and business knowledge has created the discipline of predictive analytics,” says Naveen Jain, CEO of TransOrg Solutions and Services, which offers analytics and campaign-management services to the clients in BFSI, retail, travel, online, telecom and healthcare sectors. “It has created new opportunities for future-oriented analysis of large amounts of data leading to actionable insights.”
Indeed, retailers in India — also, across the globe — are on the cutting edge of harnessing this data. If the first wave of data centred on the flow of bits and bytes, and texts and emails, the next wave is about knowledge and discovery, powered by intelligent, always-on services that make sense of the big mass of information flowing in through smart devices. More than the size or volume, the potential of big data lies in the kind of insights it can offer businesses and the kind of questions it can help answer. As a McKinsey Global Institute paper on big data, puts it, “The widespread use of increasingly granular customer data can enable retailers to improve the effectiveness of their marketing and merchandising. Big data levers applied to operations and supply chains will continue to reduce costs and increasingly create new competitive advantages and strategies for growing retailers.”
Mind you, the applications mentioned above are the final steps. Typically, there are three steps to data mining and interpretation, say experts—recognising what is happening, analysing the how and why it is happening and lastly leveraging the information. While a huge majority of the Indian market is still at the ‘recognising’ stage, according to Ankur Shiv Bhandari, managing director for the Indian subcontinent, Kantar Retail (part of the Kantar Group, the insight and consulting arm of WPP), there are a handful of retailers such as Lifestyle International, Pantaloon Retail and Trent that are working to harness consumer data from every available source to make their enterprises agile and hard to beat.
The shift, whatever the magnitude, has come on the back of the evolution of modern trade and electronic point-of-sale terminals that have made it easier to capture the data in the first place. “Earlier there was only the sales-in data available on the quantitative side,” says Bhandari. “That is, sales from the manufacturer to the distributor, or the retailer, were tracked. It is only now that sales-out, that is sales to the final consumer, is being tracked, making it possible to actually understand consumption patterns.” Says Uma Talreja, head, marketing, Westside, a retail chain operated by Trent, the retail arm of the Tata group, “The more consumer-centric your business, the more you can use consumer data for decision-making in the business. Several decisions can leverage consumer data—this includes store layouts and adjacencies, buying decisions, merchandising decisions, property selection, pricing decisions, sales mix etc.”
Companies may say that they have a lot of data, a lot of information or a lot of insight. But they must be wary of using the phrases interchangeably, says Bhandari of Kantar Retail. “It is an insight only if it holds true to the four Rs—reality (what is happening in the market), resonance (relating it to what you are doing), reasons (for why it is happening) and lastly reaction from consumers,” he explains. This will also help explain some of the challenges retailers are grappling with currently.
“Technology and storage aside, the challenge lies in efficient use of large complex data, finding meaningful relationships, analysing and creating useful and applicable and actionable business intelligence,” says Talreja. This requires enhancement of talent along with technology. “Businesses need to invest more in using the data and leveraging it as compared to the investment in collecting and storing it and use it for smart decision making. As data grows, it is critical for the business to be able to slice and dice the data to create information that help answer questions critical to the business,” she adds.
Even before the large format modern trade outlets started mushrooming, the local kiranas or the friendly neighbourhood mom-and-pop stores knew their customers on fairly intimate terms. Then, times were simpler and the sheer volume of transaction much lower. Today, as competition is becoming intense and customer needs and preferences more complex and difficult to predict, it is precisely this one-to-one relationship that big retailers are looking to emulate. Big data is their ammunition in this battle. (Read interview with Gary Hawkins for challenges in mining big data on page 4).
“Indian companies are at different levels of data maturity — some companies are struggling to visualise their data while others are using predictive models and real-time analytics to drive their decisions. Agile analytics and campaign management tools used by new generation analytics companies have made analytics affordable for Indian companies,” says Jain of TransOrg Solutions and Services.
Loyalty programmes are a strong starting point for most retailers. They need not be restricted to just your own store. Consider Pantaloon Retail, the flagship company of Future Group. The chain tied up with Payback, a multi-partner loyalty programme around a year ago. This tie-up, as per Pawan Sarda, CMO, Future Group, helps the company look at its customers in a more holistic light. “We can understand a customer’s entire profile now — where he eats, how often he travels, what’s his fuel consumption like. This helps us in building a far more personalised relationship with him,” says Sarda. For him, the biggest application of data learnings can be the ability to predict a consumer’s behaviour and then personalising the solutions for him.
How can it be done? For instance, you find out — either from his online purchase data or credit card transactions—that your customer is travelling to the US in November. And you know he will need heavy woollens to brace up to the cold weather conditions. By leveraging this information accessed through a multi-partner programme, the retailer can design customised offers for him.
In fact, based on such transaction data, Pantaloon has actually created consumer profiles such as higher-trolley-load-low-frequency-of-visit, high-frequency-but-casual-shopper etc. Armed with this insight, the chain hopes to create personalised offers and experiences, something departmental store chain Shoppers Stop is working on as well.
Vinay Bhatia, customer care associate and senior VP, marketing and loyalty, Shoppers Stop shares an example. “When we looked deep into the First Citizen (the loyalty programme subscriber) base, we observed that customers who buy both men’s shirts and trousers, the average yearly spend is 60 per cent more than that of consumers who buy only shirts and three times that of those who don’t buy men’s shirts at all.”
Approximately 9 lakh customers were shortlisted for targeted ‘trouser communication’. This group of customers was divided into sub-groups based on purchase patterns and the group’s reactions to the various communication devices was observed. One group was given information on the variety available in trousers and new brand launches at the store, another was given offers on multiple trouser purchases. These customers were sliced into control groups to measure the success or failure of the promotion. With this equation in place, Shoppers Stop raked Rs 9 crore worth additional sales in a three-week period, a lift of 30 per cent, when there was no such coordinated effort.
Transaction data can also help with store layouts (more popularly known as adjacency analytics) and inventory management. Like, if more customers are buying a combination of belts and shoes, it would be a good idea to put the two items close by. A retailer can trigger purchases by simply putting some items simply close together. Even if the consumers don’t necessarily want the two together, putting them next to each other may trigger off an impulse purchase. Analysing consumer buying patterns can throw up some insights that may prompt a retailer to go as far as changing a store lay-out completely. Shoppers Stop, for instance, found that very often when ladies shopped for Indian clothing, the other item on their list was men’s innerwear. The company hopes to include its finding in the next round of changes it plans to make in the store layout.
From the point of view of the retailer such insights can offer perspectives on product bundling options to bolster sales. Sample this: onions and potatoes are a household staple and sharp price increases have begun to hurt. To spruce up lean midweek sales, food and grocery retailer, Big Bazaar, decided to throw in a kilogram of onions and potatoes free with every bill that adds up to Rs 999. While exact numbers are not available, the company says Wednesday footfalls have increased dramatically after the free offer was introduced.
While chains can push sales through pricing and promotion, they must also remember that consumers may simply wander off if they don’t get the right in-store experience. Monitoring consumers can help gain insights on improving shopper experience dramatically. “It (in-store monitoring) is not happening in India to the same extent as it is prevalent in the West. But quite a few are undertaking shopper behaviour studies,” says Bhandari. This helps one understand the occasions or motivations for buying products, activating these insights and mirroring them in a path to purchase. This can be done through store layout, on-floor instructions and graphics, clear demarcation of categories through storage etc.
The final victory for analytics will come in the form of real-time application. Analytics create opportunities for actions which may be offline or on a real-time basis. Says Devarajan Iyer, vice-president, marketing, Lifestyle International, “Big data aided with location-based technology can be used to update customers in store on the latest offers and shopping suggestion based on their previous shopping history.” He added that the chain hasn’t implemented it so far but is currently exploring this technology and its application for future use.
Here’s one more example of retail stores taking quick decisions based on real-time data. Surely you have come across stores that quickly open an additional counter at the tills whenever more than a certain number of customers are in a queue to pay for their purchases and check out. This is part of the in-store experience — it is based on the insight that even if the store sells the cheapest goods, a longer wait time at the point of purchase is a big put off and can actually customers to a rival. So chains like Big Bazaar and Spencer’s have put in place systems that monitor the traffic towards the tills — to make the check-out process hassle free they open up more counters during rush hours and then deploy the same set of people for other tasks as the traffic begins to wear off.
A corollary benefit of data analytics is the possibility of generating an alternative revenue stream. Retailers are the point of contact between stockists and consumers. Now, stockists of FMCG players such as HUL, P&G, Pepsi etc can benefit from the insights gleaned by retailers through their data. In fact, globally some retailers have even set up data services that sell the insights to FMCG players like Unilever, P&G, Nestle, Coke, Pepsi etc which benefit from this data driven insights in the ecosystem.
“As we trade a multitude of brands in our stores, our data is a rich source of information for them to understand the behaviour and spending patterns of the customers. Therefore, an opportunity for alternative source of revenue for big data is to allow brands to pay up for the access fees for this data. They, in turn, use this for targeting consumers for new brand launches, new season merchandise launches, understanding response to their latest product lines etc,” adds Iyer of Lifestyle International. He also says that Lifestyle does send out targeted communication on behalf of brands that it retails and charges them a fee to reach out to their “loyal” member base, without sharing any data with any external party.
Evidently, retailers are waking up to the potential of big data. “If you want to understand the transformations in the marketplace, you will have to understand and master analytics,” says Jain of TransOrg Solutions and Services. Those who have made an early start in capturing and trying to make sense of all the data will definitely enjoy an advantage but analysts say this is one area that is rife with confusion making it easy to lose sight of the true potential.
Additional inputs by Ankita Rai