By Lori Boyce
Senior Director - AFFINA
Through hundreds of thousands of contacts across multiple channels, your customers are saying, “I have this product,” or “I use this service,” and most importantly, “here’s what’s happening,” and “what can you tell me about it?”
When analyzed carefully, data can uncover trends and customer feedback that can be used to help improve the service experience and reduce overall service cost. Using data to your advantage should start with an overall review of the information at hand. A few suggestions include:
Fully understand call arrival patterns and channel distribution. Optimize staffing so your agents are available to handle contacts in a timely manner. After all, overstaffing can drive up your cost per contact; understaffing can cause customer dissatisfaction and abandons. Both scenarios are counterproductive to your overarching goals. Look at volume arrival history in 15 minute increments to determine staffing and scheduling.
Don’t ignore customer satisfaction and loyalty survey data. Made up of 100 percent customer perception, these data can help verify or validate other trends, and when viewed in tandem with customer case data, can uncover the specific drivers to satisfaction and loyalty (or dissatisfaction and disloyalty). If you could pinpoint exactly which of your multiple call types, products, or reasons was causing a customer satisfaction metric to dip, wouldn’t you want to know?
Similarly, if you ask your customers for their opinions on your products or services, be sure to listen and then respond.
Customer care interactions with customers offer a voluntary market research pool. Don’t abuse it, but a few quick questions can glean spectacular data that can help shape your service delivery. For one of our clients, we were able to reduce inbound contacts by 27 percent by conducting a quick packaging survey and recommending changes to package instructions.
Use data to make decisions on the level of service offered. When brainstorming ways to reduce costs, use data to back up your suggestions and test customer tolerance. For example, we helped a client reduce the number of agents required to staff their customer care program by analyzing the impact of service level variability on customer satisfaction and loyalty. Using service level performance data and analyzing it with parallel timeframe data from our customer satisfaction survey, we were able to isolate specific customer tolerance levels to hold time. Specifically, we were able to recommend a longer hold time and reduce the number of agents, which lowered our client’s overall cost of service. At the end of the day, our data proved that as long as customers’ inquiries were resolved to their satisfaction, they were more tolerant to wait for a representative.
Your Customers. Our Priority.
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