What Companies Can Do to Reduce Customer Effort
Here are five best practices that work in a majority of cases:
1. Don’t try to solve everything with technology at once.
“When organizations acquire technology for customer service, there are many drivers behind the decision to acquire that technology,” LaRoche says. “My advice to these organizations is to not try to do everything at once. Instead, prioritize your most important needs for reducing customer effort. Then, develop a benchmark for performance that you would like your agents to strive for. You might want to reduce the amount of silence on calls by focusing on silence reduction. If you can reduce the pause times during calls, like when the agent needs to research something while the customer waits, your call center will be able to handle more calls.”
2. Develop a comprehensive knowledge base that your service agents can use.
“What we found from large-scale consumer and agent surveys is that finding the right answer to questions is the biggest challenge for both consumers and agents,” Subramaniam says. Implementing a smart, AI-infused knowledge management system can help lift multiple metrics, including AHT, FCR, and NPS.
In one case, an African bank leveraged eGain’s knowledge and AI solution in its contact center to reduce agent training and ensure best-practice compliance across its entire service organization. It moved from No. 3 to No. 1 in NPS among its peers because of this initiative.
In another case, a financial services technology client implemented eGain’s solution in its contact center and saw response time go down 47 percent, resolution time go down 80 percent, user complaints go down 45 percent, customer satisfaction go up 5 percent, and agent training time go down 30 percent.
“This allowed faster deployment of agents, which helped reduce customer effort,” Subramaniam points out.
3. Integrate your systems.
When United Healthcare wanted to improve its customer service, it looked at how much effort it was asking of customers. When the findings came back, United Healthcare worked to improve the process by integrating information for all of the customer touch points across the entire organization.
“Whether a patient called billing, or inquired about a claim, or asked about a benefit, the agent responding to the issue knew everything about the history of that customer with the company, including what the last open issue was. This allowed the agent, no matter which part of the organization he or she was working in, to be on the same page as the customer without having to re-ask for information,” Jacobs recalls.
4. Use real-time speech analytics to assess emotional clues from calls.
“By performing speech analytics during calls, agents and supervisors can get real-time feedback on whether a customer is getting frustrated or angry,” LaRoche maintains. “The software can analyze the tone of voice or the number of pauses in a conversation.”
Speech analysis software can also send the agent a screen pop to notify her of possible customer irritation or whether she is taking too long or talking too much. If the call needs to be extended or becomes hostile, an alert can be flashed to the supervisor for immediate intervention.
“At the end of the day, or at any time, supervisors and agents can also look back on call analytics reports to assess how calls went and to see where coaching might be needed,” LaRoche adds. “What’s valuable about the tool is that you are able to look at 100 percent of the calls coming into your call center. You no longer are limited to evaluating random samplings of calls, which may or may not give you an accurate and complete picture.”
This big picture matters because if supervisors are monitoring a pool of 100 call agents who take thousands of calls per month, they might be able to listen to only 1 percent of the calls. This probably presents too small of a sample to fully represent what really is going on.
5. Use real-time analytics as an agent-training tool.
“Clients tell us that by using speech analytics, they have been able to get a more accurate understanding of the individual skill sets of their customer service agents,” La Roche says.
The technology can, for example, identify agents who could be classified as having high skills but low will, meaning the agents know the subject matter but are not very willing to help or to empathize with the customer. In other cases, agents have high will but low skills, willing to do everything they can for the customer but lacking the subject matter knowledge.
“By understanding the skill sets and personality types of your agents, you can do a better job of deploying them so that the right person is on the right call with the right customer. You can also target training to fit the needs of individual agents that is based on their service profiles,” LaRoche says.
This prescriptive training also tends to level the playing field among agents.
“There can be a feeling sometimes among agents that some are evaluated differently than others, when this isn’t necessarily the case,” he adds. “By using prescriptive and targeted training, you can help alleviate this feeling and improve retention numbers for your customer service agent workforce.”
Mary Shacklett is a freelance writer and president of Transworld Data, a technology analytics, market research, and consulting firm. She can be reached at firstname.lastname@example.org.