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Leveraging Big Data to Improve Customer Satisfaction

Companies today understand that improving customer satisfaction is vital to their success, and that it means more than simply tracking complaints. Combining structured data from areas such as sales, marketing and supply chain with unstructured or semi-structured data from surveys, syndication data and other outside sources can give companies a new perspective on their customers.

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For example, merging structured with unstructured content to find underlying customer satisfaction issues allows enterprises to proactively monitor customer satisfaction levels. In many organizations, sales and customer service still work in separate silos and customer feedback is often not allowed to flow freely between the different operations resulting in ineffective distribution channels. A COO would be interested in the convergence of sales information, call center operations and social media. Big Data can create correlation between product sales, support and the customer voice to validate the true issues impacting customer satisfaction – and for targeting new customer segments, even competitors’ customers can be analyzed for industry trends to reveal propensity to buy certain products or services.

Another customer satisfaction challenge solved by Big Data is identifying the most valuable customers from a 360-degree view, with the goal of presenting them with offers and benefits relevant to their interests. And to exclude those customers who merely take advantage of discounts without maintaining any level of loyalty for the merchant. Store operations, customer service, and to some extent marketing would be interested in this solution to get the most benefit from sales and promotions. The purpose of this is to keep loyal customers by making them feel rewarded and special, and these insights enable better focus and less waste in that effort

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