Enhancing Customer Service with Big Data

Customer service is crucial for ecommerce businesses. If retailers could enhance customer service while at the same time reducing its cost, they would likely do it. Substantial data will help achieve this goal in the following four regions.

1. Website Is Broken

This is the most common reason to contact customer support. The retail experience could be broken due to a missing product page, a marketing code that’s not working, inability to test out, a payment not getting processed, the website being down, or any other reason.

The best way to control a broken website is to automate the avoidance of it and, if needed, automate the resolution. The retailer should analyze things like customer support tickets, flaws identified in the system, and server log files. By way of instance, if the product pages are broken often, the issue may be in the validation procedure. This can be automated by creating a scheduled validation that will review all product pages before launching them. By analyzing all the motives and developing automated strategies to solve them, the user experience is improved, shoppers are happier, and the customer support team’s load is decreased.

2. Product Returns

A merchandise return is the next most popular customer-service interaction. A return could happen for many reasons, including a damaged product, the wrong item was sent, an item which didn’t match, the customer discovered the identical product cheaper elsewhere, or even the client didn’t like the product after it came and decided to use the free returns policy.

An analysis of the historical returns information will help determine the very best factors. They may be classified by product, region, vendors, customer segments, or even individual customers. This will help understand questions such as which goods are returned the most, what’s the most frequent reason for product returns, and if yields are more common for specific vendors. Furthermore, using a huge data tool such as a real-time analytics engine or a visualization tool, rules and thresholds could be generated that get automatically triggered to send alerts for odd returns action. When the merchant has all of this info, it can optimize the merchandise variety, determine which vendors are working better, and evaluate a client’s history. This will result in reducing the yields rate and lowering the amount of tickets opened by the customer support team.

3. Fraud

Fraud is an unfortunate fact of an internet retail company. It typically affects the customer support team. Frequent types of fraud include denying the item was delivered and buying with stolen credit cards. Both these results in credit card chargebacks, which hurt retailers.

Chargeback fraud can be reduced or prevented by assessing customer purchase patterns, confirming the purchase with the purchaser prior to the checkout is completed — especially for large ticket items — and for new clients, analyzing system log files to identify a client’s location and his IP address, browser, and operating system. Create alerts if anything appears different from the standard. All this can be achieved using large data solutions that offer a flexible framework to specify rules and application of these rules to many different data sources. These solutions also include connectors which pull data from log files and analyze them in real time. This automation will cause reduction in fraud, fewer calls to customer service, and enhanced profitability.

More also:







4. Delayed Delivery

Delayed delivery of an arrangement also leads to customer support calls and emails. Most retailers have automated the delivery communicating by providing the tracking information online. But there can be other reasons for delayed delivery, like the product being dropped, becoming discharged from customer’s doorstep, and delays in customs for international shipments.

Big data can only assist with this issue if the reasons for delay have been recorded through the years. The motives will help refine and communicate the lead times for delivery of unique products. The analysis could also identify the areas where next day dispatch isn’t viable, thereby easing the load on client service and improving the entire procedure.