Big Data is changing the retail sector

E-commerce sites have used big data the most and have, thus, reaped the most benefits. Big data promotes a better understanding of consumers. It provides an accurate vision of current consumer trends and consumer behaviour.

Big data is all about information. Using big data in the retail industry has turned out to be a huge boon for many retailers. It is a cost-effective method to study a consumer's in-store and online purchasing patterns. Hence via intelligent analytics, retailers are able to effectively target their potential customers. They can also strategize product creation and supply chain planning using big data.

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The retailing industry has now adopted data-driven customization technology. The data available in social-network conversations, online purchases, and geo-tagged smart phone interactions aids big data in creating suggestions. According to a study by the Mckinsey Global Institute, retailers that embrace big data analytics yield a 60% boost in margins and a 1% improvement in labour productivity.

For example, Amazon recommends products to you based on an algorithm that predicts what you may be interested in purchasing. Amazon has generated 29% of sales through their recommendations engine which suggests popular products to specific customers. It analyses customer data coming from over 152 million accounts. Amazon has realized that big data is the real secret behind sales success.

Predicting Trends

Big data includes a wide range of tools for retailers to determine what this season's "must have" items will be. Trend predicting algorithms are combined with social media posts and web browsing habits. This helps to find out what products are causing a buzz. Big data can be used to accurately predict what the high grossing products in a category are likely to be. Thus, retailers can target their customers effectively.

Forecasting demand

Once there's some clarity on what products people are expected to buy, retailers work on figuring out where the demand will be. This involves gathering customer data (including demographics and economic indicators) to understand the spending habits across the target group. If a retailer realizes that the sale of umbrellas and gumboots increases in the monsoon season, they can increase the number of suggestions of these products. These suggestions can be shown in a customer's feed when the monsoon arrives.

Smart Merchandizing

A big priority for a lot of retailers is moving inventory out of the warehouse. Big data can help in this by adding some intelligence to online shopping, particularly when it comes to those who do not complete their purchases.

For example, one large retailer makes use of data analytics to check which customer abandons the cart before making an online purchase. On seeing this, the retailer checks the inventory closest to the customer's location. The system calculates a discount, which the retailer offers to the customer, via email or the phone. The consumer gets interested by looking at the discounted rate and/or faster delivery option. Thus, the customer, in all probability, ends up making the purchase.

Optimizing pricing

Retailers use algorithms to track demand, inventory levels, and competitor activity. This allows them to take action based on insights in a matter of minutes. Big data also plays a part in helping to determine when prices should be dropped. Before the age of analytics, most retailers would just reduce prices at the end of a “season”, because they would presume that demand would reduce. This regularly happened in fashion and utility-wear (such as raincoats or gloves). But, big data now provides data-driven, not assumption based, insights to regulate the prices of products.

Almost every aspect of retailing has been impacted by the emergence of big data. This has helped boost the effectiveness of all aspects of retail operations. Are you in the game yet?