6 Ways Online Retailers Can Use Big Data Analytics

Small retailers that think big data analysis is only important for larger retailers should think again. The fact is using big data analytics is key for small businesses that want to compete with the larger companies.

And it’s even more critical to help online retailers interact with their customers in real time, according to an article in Practical eCommerce.

Practical eCommerce offers six ways online retailers can use big data and big data analytics to improve ROI.

1. Personalization. Different customers shop with the same retailer in different ways. That means online retailers have to process the data from these various touch points in real time so they can provide personalized content and promotions to each customer.

“For example, do not treat loyal customers the same as new ones,” says Gagan Mehra, author of the article. “The experience needs to be personalized to reward loyal customers. It should look attractive and ‘sticky’ for new customers.”

2. Dynamic Pricing. Dynamic pricing is a type of “price discrimination that companies use to change prices on the fly based on circumstances and estimated user demand,” according to Money Crashers. Online retailers need dynamic pricing if their products compete on pricing with other sites.

Dynamic pricing means taking data from a number of sources, such as competitor prices, product sales, regional preferences, and customer actions and then figuring out the price needed to close the sale for a particular product. Supporting this functionality will give businesses a big competitive advantage, according to Mehra.

3. Customer Service. The success of an e-commerce site depends in large part on superior customer service. To excel at customer service, online retailers must use big data analytics to give customer service representatives a 360-degree view of each shopper’s interactions with their companies.

“For example, if a customer has complained via the contact form on your online store and also tweeted about it, it will be good to have this background when he calls customer service,” Mehra says. “This will result in the customer feeling valued, creating a quicker resolution.”

4. Manage Fraud. Online retailers can use big data analytics to process their sales transactions against known patterns of fraud, to detect fraud in real time – or it could be too late to catch the criminals.

5. Supply chain visibility. Retailers can use big data analytics to give customers information on the availability, status and location of their orders, Mehra notes. “This will require your commerce, warehousing, and transportation functions to communicate with each other and with any third-party systems in your supply chain,” he says. “This functionality is best implemented by making small changes gradually.”

6. Predictive analytics. Online retailers can use predictive analytics to predict the revenue from certain products in the next quarter. “Knowing this, a merchant can better manage its inventory costs and avoid key out-of-stock products,” Mehra says.