A now heavily discussed topic with regard to innovation and technology, big data has become a focus for some of the world's largest retailers, proving to be a viable solution to margin pressures and ever-increasing retail competition.

Big data is a relatively broad phrase to describe large data sets that are too large, fast or complex to be processed using traditional methods. But the phrase has come to be synonymous with the field of systematically processing and analyzing the information to reveal patterns and trends, particularly relating to human behaviour.

What is important is what organizations can do with this data, optimising for cost efficiency and revenue increases in efforts to increase margins.

While the phrase 'big data' was coined in the early 2000's by industry analyst Doug Laney – who attributed to big data three main attributes, volume, velocity and variety – the field has really hit its stride in conjunction with advancements in machine learning.

The Role of Big Data in Retail

For the retail industry specifically, Big Data has the capacity to track consumer trends in order to attract new customers and, arguably more importantly, increase the lifetime value of existing customers through targeted offers and loyalty programs.

However, critical to a retailers ability to utilise the benefits is "a robust and methodical way of collecting, managing and interpreting data, then linking that insight to the overarching business strategy," according to KPMG. With this capacity, a retailer has numerous opportunities.

Targeted Recommendations and Advertising

The extensive analysis of a large consumer dataset allows a retailer to predict what a customer is likely to purchase next. A retailer can study a consumer's previous purchases, purchases of similar customer segments, how they interact with online stores/apps/social media accounts, and even significant other factors like the weather. Using this data, they can personalise recommendations and advertising to ensure the greatest rate of return on marketing expenditure possible.

Brands like Walgreens and Pantene in the United States partnered with the Weather Channel to anticipate weather patterns and shift marketing strategies accordingly. Seeing a period of high humidity coming, Walgreens began to market anti-frizz products more heavily to women due to the increased demand during these conditions, resulting in a 4% increase in hair product sales over a 2 month period.

Similarly, with regard to the point about studying purchases of similar customer segments, Amazon attributes 29% of sales to their recommendation engine. The engine studies the data sets of 150 million customers to recommend products to customers throughout their website.

Merchandising and Stock Levels

In a similar manner to targeted recommendations and offers, a retailer can study data extensively to optimise stock variety and the availability/presentation of stock between outlets.

At the most basic level, a retailer can study data down to the day of the week to determine their allocation of stock. By optimising stock allocation, the retailer can simultaneously avoid lost revenue as a result of a lack of in-demand products and reduce the costs associated with holding stock, such as storage costs (utilities, warehousing etc.) and shrinkage.

Walmart, the world's largest brick and mortar retailer by both revenue and number of stores, is investing heavily in what will be the world's largest private cloud system, capable of managing 2.5 petabytes of data an hour. One of the major focuses of the Arkansas based analytics hub analysing and optimising stock levels.

Turning Trends into Revenue

The analysis and response to consumer trends has proved to be a lucrative option for industries globally. A study of the car industry and Tesla's rapid growth as a result of a growing number of drivers turning to eco-friendly options, provides ample proof of this fact.

The issue then is the reactive stance retailers have often taken; adapting to trends too late and missing out on revenue opportunities. Big Data provides a retailer with the capacity to be proactive with regard to trends. Studying large datasets gathered from social media, forums and existing customers, can reveal growing trends before they really accelerate, unlocking vast revenue opportunities.

The heavily publicized Dollar Shave Club capitalized on the consumer's increased engagement with subscription boxes for items they would otherwise have to purchase regularly. Combined with a remarkably effective satirical marketing approach, the company has managed to acquire 3.2 million subscribers, including 12,000 customers in its first 24 hours. In 2016 the company was acquired by Unilever for an estimated $1 Billion USD.

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Oracle Retail

The Role of Big Data in Retail

Targeted Recommendations and Advertising

Merchandising and Stock Levels

Turning Trends into Revenue

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