Big Data: A Game Changer in Retail Industry

Kunal Mehta, General Manager, IT, Raymond Mumbai based Raymond is a diversified conglomerate with majority business interests in Textile & Apparel sectors and presence across diverse segments such as FMCG, Engineering and Prophylactics in national and international markets.

The retail industry continues to accelerate rapidly, and with it, the need for businesses to find the best retail use cases for big data. Retailers have started relying on data more than ever to inform, test, and devise their strategies.The big data revolution has given birth to different kinds, types and stages of data analysis. Boardrooms across companies are buzzing around with data analytics.

For brands and retailers, information is also a game changer. Big data analytics has the ability to help companies understand shopping trends by applying customer analytics to uncover, interpret, and act on meaningful data insights, including online shopper and in store patterns. The retailers both offline and online have started to adopt the data first strategy towards understanding the buying behaviour of their customers, mapping them to products, and planning marketing strategies to sell their products by reducing product design to market lead time, better product segmentation and increased profits.

Today, retailers attempt to find innovative ways to draw insights from the ever increasing amount of structured and unstructured information available about their consumer's behaviour. With digital technology becoming ubiquitous, shoppers can make informed decisions using online data and content to discover, compare, and buy products from anywhere and at any time. Today's consumers interacts with the retail companies through multiple interaction points - point of sale systems, mobile, apps, social media, in-store sensors, e-commerce sites and many more. Hence, it is extremely important that retailers have started applying Big Data analytics at every step of the retail process - right from predicting the popular products to identifying the customers who are likely to be interested in these products and what to sell them next.

Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what distinguishes a best data scientist or data analyst from others is their ability to identify the kind of analytics that can be leveraged to benefit the business at an optimum. The three dominant types of analytics Descriptive, Predictive and Prescriptive analytics, are
interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight:

Data analytics at every step of the retail process right from predicting the popular products to identifying the customers who are likely to be interested in these products and what to sell them next.

a) Descriptive Analytics: 90percent of the organizations use descriptive analysis. This form analyses the data coming in real-time and historical data for insights on how to approach the future. Most of social analytics are of this nature

b) Predictive Analytics: provides answers to questions that cannot be answered by BI.It can only forecast what might happen in the future as they are probabilistic in nature.

c) Prescriptive Analytics: is an advanced analytics concept based on stochastic optimization that helps how to achieve the best outcomes and identify data uncertainties to make better decisions

Big data at this point is mainly used for:
1) Generating Recommendations: Based on a customer's purchase history,one can predict what the customer is likely to purchase next. Machine learning models are trained in historical data which allows the retailer to generate accurate recommendations.

2) Forecasting trends: Retailers are able to understand what the market demands are using economic indicators and demographic data.

3) Utilizing Market Basket Analysis: A standard technique used by retailers,the market basket analysis helps figure out what products customers are most likely to purchase together. Using Hadoop, retailers can now analyze more data.

4) Retail dashboards will give a high- level overview of important competitive performance metrics, including pricing promotion and catalog movements.

5) Inventory and Pricing: Track millions of transactions every day. Inventory levels, competitors, and demand can be tracked and market changes can be responded to automatically.

6) Social Media: Listening to what your customers have to say on social media have become very important. NLP or natural language processing is used to extract information from social media sites. Machine learning is then used to make sense and give the retailer an edge over competition.

7) Enhancing customer experience: Retail analytics will now be used to anticipate the demand of the shopper as well as produce a seamless customer experience across all channels and improve customer loyalty.

8) Predicting trends: Marketers are using what is called sentiment analysis. Sophisticated machine learning algorithms are used to gather data that can then be used to predict the top selling products in a specific category.

In order to maintain a competitive edge in an accelerating marketplace,it is becoming increasingly important for retailers to seek proactive methods of harnessing new and extensive data sources in innovative ways. Hence, when all of customer data is aggregated and analyzed, it can yield insights you never had before e.g.,who are your high value customers,what motivates them to buy more, how do they behave, and how and when is it best reach them? Armed with these insights,you can improve customer acquisition and drive customer loyalty.