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Big Data is Changing the Retail Industry   Featured

Big Data is Changing the Retail Industry    Jordan Nix

For years, many people have believed that great marketing is an art. However, this seems to be changing with the entry of big data into the equation. Big data has introduced a scientific element into marketing campaigns. With this, smart marketers now rely more on data to inform their decisions concerning product design, tests, and development strategies. With the increasing demand for personalization by customers, this is a welcome relief for companies and marketers who want to understand exactly what the customer wants. The rising demand for online stores makes big data even more necessary to improve the online shopping experience. So, how exactly is big data changing the retail industry? Here are a few areas, some of which you might have already heard of before.

  1. Predicting spending

Retailers like Amazon use big data to recommend items to customers based on their past searches and purchases. With this approach, customers don’t have to look for items they need. Rather, recommended items are shown as soon as they visit the website. This kind of prediction enhances customer experience and increases sales. For instance, through big data-enabled prediction, Amazon generates over 29 percent of sales through recommendation engines.

  1. Pricing optimization

Real-time merchandising systems are now a priority for big retailers like Walmart. The retail powerhouse is currently working on a private cloud as it seeks to track millions of transactions per day. Through big data initiatives, critical elements such as inventory levels, competitors, and demand for specific products can be tracked and the pricing of items adjusted accordingly. With big data systems, the retailer can respond to market changes automatically.

  1. Demand forecasting

In addition to big data, artificial intelligence (AI) algorithms are used to analyze the social media and web browsing habits of specific groups of people in the retail market. The data is then collected and used to make retail decisions and stocking of products based on demand. Walgreens, for example, used this type of forecasting to predict the potential demand for anti-frizz products and developed ads and promotions to drive sales. This saw an increase in sales of hair care products for up to 4 percent. Retail forecasting using AI and big data can increase profits.

  1. Trend analysis

Customer needs keep changing from time to time. This is even worse at the age of the internet, where trends can be influenced by a few people and within a short time. As such, marketers need to find a way of assessing the changes in the market and adjusting accordingly. Marketers are now using sentiment analysis, a sophisticated machine learning algorithm that takes advantage of data to determine trends. The collected data can then be used to predict the bestselling products in certain categories, and such products are then stocked accordingly.

  1. Customer experience personalization

Each customer has different demands and preferences. This makes it hard to identify and accommodate each one of them. However, big data gives retailers a new capability to enhance customer experience. Retailers like Costco use transaction data to keep customers healthy. Big data allows retailers to build a profile of every customer and use the data to develop a relationship with them. For instance, you can personalize product types that your customers purchase, promotional messages, size, color, and the type of products the customer likes.

As retailers continue to look for a competitive edge in service provision, big data is the leading area to make customers get the best. Although big data is still in its early stages, the entry of artificial intelligence and other technologies associated with it, such as machine learning and the internet of things, will indeed change odds for retailers and customers. 

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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