Big Data Analytics for the retail industry

Retail is the process of selling products and services through multiple channels. Retailers identify the buyer’s need and deliver the product or services through a medium. The medium could be the traditional physical shops or e-commerce websites, or multi-channel. The present landscape of retail is very different from what it was some years ago. For instance, the number of touchpoints for a consumer has increased more than ever before. While visiting a store, a consumer does not make the purchase decision by evaluating the product or talking to a retail salesperson. They use multiple information sources like browsing the web on their phones to compare prices, reading product reviews, checking in with an immediate network like friends and family, visiting social media pages to gather more information about particular products or services. In this process, the consumers generate a lot of data for future consumption or use the data to make their current decisions. Thus from the customer point of view, the purchase journey, including researching product, quality, price, convenience, availability, etc. leaves a lot of data traces

On the seller side and in the retail ecosystem, there are several strategic decisions to be made. Some of these include analysis of the current market scenario, understanding the customer, benchmarking of competition, supply chain process efficiency, channel & distribution strategy, pricing and placement of products strategy, etc. All of this relies on some data. Thus, both seller and customer use several data points at their respective levels in their buying/selling journey. The number of sources available and used has also increased manifold at both ends.

The large amount of customer data collected through the point of sale and other sources can help in producing valuable insights for retailers. Earlier, the retailers provided the product to the customer basis on the requirement mentioned at the point of sale. But the scenario is different now – retailers can use the previously collected data and behaviour of a cohort of consumers and predict the customer choices well in advance, even before buying the product. As a result, the data is growing exponentially in terms of volume, velocity, veracity, and value. These insights give them understanding and an edge over their competitors.

Therefore, big data analytics techniques, Artificial Intelligence and machine learning algorithms are now used more than ever. Big data is helping retailers in inventory management, packaging, cost-effectiveness, fast transportation, forecasting the demand and customer experience. With these analytics techniques, consumer cohorts and profiling are done to a certain level of accuracy and help sellers understand the interests, choices, and lifetime value of the consumer.

In the years 2020 and 2021, due to the Covid19 pandemic, this was further amplified by the digital-first behaviour of consumers. It further enables understanding and generating valuable information to serve the consumer better. Now, the brands know more about the consumers through their online behaviour, which benefits both the buyer and seller. Due to this deep knowledge about consumer likes/dislikes, choices and behaviour – offerings or even communication pre and post-offer can be customized, leading to increased customer loyalty and helping retailers return as repeat purchases and referrals.  While the pandemic situation is improving and the economy is on the recovery route, some shifts in product demand and consumer behaviour are long-term changes and therefore termed as new normal for the retail industry.

The present paper promises to provide a framework for using the various big data methodologies and data science algorithms, which can help retailers make better decisions. We will review the latest Machine learning techniques that can help solve core business needs of expanding the customer base. The paper will deliver some helpful methodology for big data analytics techniques for growth and sustainability in retail.

Disclaimer: All views, thoughts and opinions expressed belong solely to the author and don’t represent any organization that he is/has been a part of.

(The above article was published as an Abstract at the ICMIT2021 conference)

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