Big data isn’t just for developers and analysts in the technical arena. In today’s digital age, big data has become a powerful tool across industries. In fact, many retailers are experiencing major growth thanks to the big data industry.

According to research by Monetate, retailers have big plans in store for big data. Let’s take a look at big data initiatives throughout the retail industry.

Photo credit: Monetate; The Retailer’s Guide to Big Data

As you can see, big data initiatives are extending across all aspects of the retail channel. In the same research project, Monetate lists five major challenges that retailers face with big data.

  • 51 percent say that the lack of sharing data is an obstacle to measuring marketing ROI
  • 45 percent say that it’s difficult to use data effectively to personalize marketing communications
  • 42 percent have a hard time linking data together at the individual customer level
  • 39 percent can’t collect their desired data quickly or consistently enough
  • 29 percent feel they have too little customer data

The majority of these problems are associated with the separation of data into data silos. This is a common byproduct of traditional big data solutions. Thankfully, new big data innovations such as Apache Hadoop have enabled organizations to consolidate their data silos and are making big data an even better fit for an already powerful partnership with retail.

Here are just a few of the many big data use cases for the retail industry:

1. Fuzzy Matching

Most consumers have a pretty good idea about what they want. When shopping online, consumers have the power to drill down on their preferences and usually find several options. In some cases, however, the results can be a detriment to both consumer and retailer. Sometimes consumers think they know what they want, but haven’t had exposure to enough options to know for sure. Other times, the retailer may not have exactly what consumers want, but may have an option that would be equally pleasing to them.

This is where fuzzy matching comes in. Fuzzy matching uses algorithmic processes (fuzzy logic) to figure out if there is any relationship or similarity between different data elements.

Fuzzy matching can be used to provide consumers with “close enough” results. An example of this could be airfare being offered for a different day of the week than what is specified, but with the requested price and destination. Another example is a recommendation for a jacket that is three-quarter sleeved instead of wrist length, but is in the right size, color and style. If a retailer doesn’t have exactly what the customer had in mind, both parties benefit from having the available options that are close to a shopper’s specifications.

2. Smart Merchandising

Moving inventory is always a top priority for retailers. One of the ways big data helps move inventory is by adding some “intelligence” to the online shopping process, specifically regarding those who don’t follow through with their purchases.

For example, one major retailer uses big data to make money from its customers’  abandoned online shopping carts. If a customer adds a product to his/her cart, but then abandons it later, the retailer would look at the consumer’s location. The retailer then cross checks the consumer’s location with the inventory at the closest store. If the product is in stock at the location, the retailer would retarget the consumer. The system would calculate a discounted price that would incentivize the customer to buy the product. This calculation would also keep the discount at a rate that is still profitable to the retailer. The discounted offer is then emailed to the consumer. The final result if the customer then purchases the item is a win for the consumer because of the savings and a double-win for the retailer because they were able to move product and still remain profitable.

3. Fraud Detection and Prevention

By leveraging a retailer’s rich data environment, specific areas of fraud exposure can be identified. For example, retailers can use Hadoop to analyze data from daily sales transactions as well as from activities such as accounts, payable, sales forecasts, warehouse inventory, and even employee shift records in order to help pinpoint fraudulent activities and protect the bottom line.

4. Inventory Forecasting

Big data has made forecasting inventory much more reliable. In the past, limited data points meant a limited field of vision. Companies depended on simple variables like the month or season. Today, big data crunches variables like weather, trending topics, promotional outcomes, and so much more. With predictive analytics, retailers can spend less money holding stale inventory and more time selling product.

5. Consumer Gamification

Gamification has always been a successful tool to increase engagement and loyalty from consumers. Big data has made gamification virtually limitless for the half of national retailers that are projected to take advantage of it over the next two to three years - a figure estimated by Scott Silverman, Co-Founder and VP of Marketing for Ifeelgoods.

Gamification via loyalty rewards has exploded for retailers over the past several years. Today, loyal customers are earning physical and virtual rewards for desirable activities. Referral programs provide incentives for consumers to become an unpaid salesforce for brands they champion. Return-visit loyalty points make it easy for certain retailers to become the consumer’s preferred vendor. Loyal customers with competitive personalities get a buzz from virtual prizes that showcase their performance versus other patrons. In short, gamification grows a community around brands quite successfully.

In addition to driving customer loyalty, gamification can also be used to analyze customer behavior, giving retailers even greater insight into their customers.

Conclusion

Big data is infiltrating almost every corner of the retail industry and has helped boost the effectiveness of nearly every aspect of retail operations. More and more retailers are adopting an enterprise big data solution to reduce fraud, gain customer loyalty, improve current processes, increase sales, and gain a competitive edge.