7
June
2016

Big Data Transforming Traditional Retail

big data in retail

The introduction of latest technology is transforming the retail providing the shoppers with a new line of in-store engagement, where retailers can predict shopping trend of the customers, when they are in the store and push promotional message depending on their daily needs. This innovative, personalized purchase has not only simplified the shopping scenario in India, but also boosting the retail revenue and consequently adding to the GDP of the nation.

  • How the retail prediction generates accurate outcomes that drive more sales and increase the conversion?

  • How do retailers understand the right time to push the right promotional message?

  • How the retailers identify the promotional strategies of their peer and launch new products accordingly?

The answer is – DATA DRIVEN DECISION

 

The intervention of Big Data replaced Traditional Shopping Scenario

 

Customers are embracing multi-channel shopping platform widened the scope of growth as the retailers are bringing new lines to market-driven brand expansion. Data analytics are disrupting the way retailers merchandise goods and alter the business for the consumer product brands that line the shelves of brick-and-mortar stores. The introduction of data analytics is reviewing the way on how to sell, what to sell, when to sell, whom to sell and why to sell in the retail outlets.

 

Product popularity – Demand Forecasting

 

Russian retailers, for instance, found an exponential rise in demand for books and novels as the temperature starts dropping. Consequently, in Russia, you will receive a good number of the recommendations of novel and books in your feeds as the climate gets colder. Data collected over years analyze such trend and push promotional messages that increase the sales in a definite period of the year.

 

Product Placement in-store – The benefits of Kinetic Mapping

 

To elucidate this, you can look at the stores where the product placement is given the optimal importance. Store data help the retailers to study the previous trend of sale-spots in the store and place any product accordingly. The accuracy of product placement encourages optimal in-store product display, making the sale more dynamic and faster. Kinetic Mapping helps the retailers to track customer's footprints in the store that decide future product placement of any particular brand-product to enjoy the sale which depends on the outcome.

 

Product Affinity – driving the sale of Complimentary Product

 

Product Affinity is generated when the sale of one product drives the sale (or at least chance) of a complimentary product. Suppose, when a customer walks into the store and ends up buying a pair of shoes, his purchase generates a chance of buying shoes-polish, shoelaces etc. This affinity can also be categorized into natural affinity and non-natural affinity.

 

For instance, bread and butter have a natural affinity, while Diapers and Beer come under non-natural affinity product group. A shopper's purchase history can also reveal the purchase of Diapers and Beer together for the age group of 35-40 years, where the parents may buy Beer to meet their thirst, while Diapers will go for their toddlers.

 

So, what is the role of the Retailers in increasing the sale of the second product by knowing the Product Affinity?

 

  • Affinity Product Placement – The retailer can study the previous purchase data of a particular shopper and analyze the placement of complementary goods in the same or close aisle. If the product is a natural affinity group, they should definitely be placed in the same aisle, while non-natural affinity products can be placed at a distance. This strategy of product placement can boost the store sales.

  • High Margin Products Placement – Another way to boost sales is the placement of high margin products. These can be placed in the path of two affinity products to drive sales. The placement of one high margin product can lead to an extra minute of quality time, which will lead to a high degree of likelihood of buying the product.

    Example – Suppose one aisle are lined up with toys and dolls. To place candy, you should choose a distant aisle, but on the same floor and at the same line having a single path to walk for the customers. In this path, you can place other kids items having a high margin that will surely increase the chance of purchase.

  • Affinity-Based Promotion – After knowing the product affinity, retailers may also allow a discount on the complimentary product that drives the sale as well. This discount can be customized on allowing it for a new launch product, retailer's own brand item, high margin products etc. However, the products must be complimentary and the choice must be made after a proper analysis of the previous data from the store.

 

A Right Move Counts

 

Consumer Brands and Retailers leverage Big Data and Retail Analytics to drive better sales and optimize the process to earn revenue and thereby throws a challenge to the competitor.
 

With the deployment of the right tools, the retail business can run smart enough to tackle real-time challenges at any level. The analysis of Data is utilized to optimize inventory management and inventory operation. Real-time Data can also be collected across demographics to reach a data-driven suggestion for optimal in-store product assortment.
 

Retail Analytics Software also powered by transaction analysis, intelligence with which it can recommend offers, discounts and product benefits associated with a definite product. These are well utilized by the store to promote offers, discounts and thereby increase the market basket size of the consumer at checkout.
 

From the collected data of the customers, retailers can enjoy a better insight into their regular needs and purchasing habits. This helps in promoting the right message at a right time, even with the alternative suggestion. This ensures the importance of the customers to the customers and made them feel special. Customers remain stuck to the brand and the store that boost the customer loyalty and add to the revenue by earning a healthy market reputation. Big Data is radically transforming the shopping habits of buyers in today's world, ensuring a better sell and faster growth.

About Ajay Hirawat

Ajay Hirwat has 5 + years of experience in Program Management and has been the Key Contributor of success at Marque clients of Tickto. He has been involved in the development and making sure that product works as promised. An BE ( Hons) from CSVTU, Chattisgarh, Ajay started his career with Maketick as an QA Analytic and grew through the ranks to become a Lead in his current role. He shifted to Tickto from July, 2015 and since then has been integrally involved with key customer accounts.

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