ImageThe holiday shopping season has already begun, and for most retailers it’s an important time of year to boost sales and get profit margins up. With so many businesses vying for the consumer’s attention, it can be difficult to get that competitive edge, especially since customers can easily access hundreds of thousands of stores online to find the best deal. While technology presented new challenges for customer-oriented businesses, it also provides an opportunity to improve the customer experience and find new ways to boost sales. That solution is big data.

You’ve probably heard of big data by now—the ever growing collection of multi structured data, and the tools businesses are using to capture and analyze it, such as Hadoop or the cloud version, Hadoop as a Service. These tools can help retailers to reach out to their customers in a personalized way that hasn’t been possible since the consumer stopped shopping at the same local stores their entire lives. Let’s take a look at five examples that your business could implement this holiday season.

1. A Personalized Experience Across Channels

Reaching out to the customer isn’t just about the website or in-store experience; it’s about offering an experience across platforms from desktop, to mobile, to the physical store. Retailers can collect customer info based on purchase history, products they look at, completed and incompleted purchases and social data along with situational data such as age, sex and location to complete a digital profile of each customer. With that data, retailers can then create customized advertisements and landing pages featuring the products that the individual would be most interested in. If the goal is to get the customer in the store, a message with directing the user to the nearest location with an in-store shipping option would work.

2. Real Time Retargeting

Many retailers already used retargeted advertising. A customer visits their website, and then starts to see ads for that store everywhere they go. The problem is these types of ads are easy to ignore and often aren’t even relevant to that individual. Video has been used to draw the consumer’s attention, but using the same video for everyone is hardly the most effective method. Instead, companies can use big data analytics to use different videos depending on which stage of the buying process the consumer happens to be in. A customer who has a product picked out in their shopping cart may need a free shipping offer, but one who has only been on the homepage may want a broad overview of what the company has to offer.

3. Cross-Sell

The holidays tend to bring in a lot of new consumers, especially since the recipient of the product often isn’t the person who made the purchase. While this boost in sales is important, retailers can also see this as an opportunity to retain new customers long after the holiday season has ended. Using big data to offer personalized offers to consumers, be they discounts, tips or extra services, can go a long way to encouraging additional purchases and creating new loyal customers.

4. Customer Feedback

Getting customer feedback on products and services is difficult. People are too busy to take surveys, and focus groups are expensive and tend to miss important insights due to leading questions or groupthink. Looking at consumer’s opinions that they express online, however, can give businesses a much better idea of how a product is being received and make changes, if need be, to keep their customers happy and the profits up.

5. Award Loyal Customers

Finally, awarding loyal customers with big data technology can boost profits, as customers want to feel like they are important and are more likely to act on personalized offers. Sears created a customer loyalty program using Hadoop that captures customer activity on an individual level. Last year the program had 80 million members, and members tend to shop and spend more than nonmembers do.

This is just five examples of the many ways retailers can use big data to boost profits and improve customer service. What other ways have you seen retailers use data to get the most out of the holiday shopping season?

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