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How can big data improve customer service in e-commerce?

Paul Newman Archive

A successful e-commerce venture does much more than simply market and sell products to consumers. Ideally, retailers in the online realm would look not just to make sales, but to provide a complete customer experience. That means satisfying people every step of the way, from the moment they first discover an online merchant to their final receipt of satisfactory goods.

Luckily, big data can help at each of these steps. Countless marketers and sellers are looking to integrate data-driven processes as much as possible, but too often, they focus solely on how data can help them make sales. There are several other aspects they're neglecting.

According to Practical Ecommerce, big data holds tremendous potential to improve customer service in the retail realm. "Customer service" in this case doesn't just mean handling consumers' complaints about their products - it means smoothing out the entire process.

According to Gagan Mehra, an expert in e-commerce and big data, merchants should look to integrate data-driven processes in a way that's efficient and cost-effective.

"Customer service is key for ecommerce businesses," Mehra stated. "If retailers could improve customer service while also reducing its cost, they would likely do it."

Mehra sees four key areas in which companies can improve their attention to serving customers.

Malfunctioning websites
Broken websites are one major reason why e-commerce sales go wrong and customers abandon the process. Website malfunctions can happen for a variety of reasons. Sometimes it's because a shopper is using a sub-optimal platform for the site in question, such as a web browser on a mobile device. At other times, it's because a site's servers are crashing, or there's a weak connection between the e-retailer and the end user. In any of these cases, data analysis can be used to
determine what's causing the problem and how retailers can fix it.

Acts of fraud
Unfortunately, many e-commerce customers are being victimized by fraud in 2013. As improving technologies have enhanced retailers' infrastructures, they've likewise had an adverse effect on the fraud landscape, as cybercriminals now have new and improved tools. They're better equipped to hack into people's accounts, steal financial information and make purchases with others' money. Part of the onus is on the consumers to protect themselves, but also, retailers should use data to closely monitor their sales and sniff out any suspicious activities. If they detect purchases that are irregularly large or otherwise fishy, they can nip criminal activity in the bud.

Returned products
One of the most common interactions that consumers have with their merchants is to return an unwanted product. People make returns for many reasons - they ordered clothes that didn't fit, or gifts for a person who didn't want them, or any one of countless other possibilities. Data analysis can be used to expedite the return process, analyzing how best to qui
ckly get products back to the seller. Furthermore, companies can devise better strategies for preventing returns from happening in the first place by targeting consumers with specific product recommendations that work for them. Returns can be a drain on merchants' time and money, so it's best to use data analysis to cut down on these problems.

Delayed deliveries
When consumers order products from online sellers, they expect to receive th
em in a timely fashion. If there are any unexpected delays, companies risk losing their customers to competing sellers. Big data analytics can help e-sellers analyze the shipping process and make sure people in all corners of the globe are receiving their products as quickly as possible.

In all of these endeavors, data quality should be a priority. Online buyers expect a seamless customer experience, and companies can ensure this by working with accurate information about their client bases.

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