At CRM we use numerous proprietary and universal analytics, methods and tools. To understand the key drivers of desired outcomes we often use regression analysis. Regression analysis helps to not only capture Business Intelligence, but it can easily translate into both strategic and tactical plans for refining your relationship with your customers and your organization. Properly identifying and aligning your customers’ priorities to your organizational activities (customer-centricity), has been a focal point in our recent Customer Insights to Action – User Group discussion topics, quarterly reviews and monthly EQM meetings. It is also a major focus for the competitive marketplace as a whole.
We support and teach call centers on how to use Business Intelligence created from our analysis to identify agent coaching, monitoring and training priorities. Skills and behaviors that rank high among customers are given the greatest attention to detail, while call center agent performance within the lower customer priorities, is maintained.
Extenuating circumstances, such as abnormally high wait time, the launch of a new knowledgebase or CTI system call for more frequent use of this key tool because you need to adjust to customer needs. The skills that your call center agents draw upon during the normal course of their day are quite different from those required to calm a frenetic or incensed customer who has waited longer than their liking to reach an agent. Regression analysis can identify the skills and behaviors which can help your call center agents successfully navigate this thorny situation. This type of activity is what separates “the best” from the rest.
The regression analysis below is based on a three-month time frame in which the business partner’s call center operated within the set Average Speed of Answer (ASA) goal. This regression analysis indicates that the behaviors or skills most valued by customers (in declining order of importance) are gaining the customer’s confidence in the information presented and quickly understanding the reason for the call.
The following regression analysis was generated for the same call center during a two-month period when ASA exceeded goal by approximately 30%. Note the dramatic shift in customer priorities. When wait time exceeded customer tolerance, the agent’s ability to quickly understand the reason for the call (and communicate this understanding) became by far, the largest driver of the customer’s satisfaction with the call center agent. The importance of all other agent skills dropped off, as the one skill that pointed to the efficiency of the remainder of the call emerged as key.
So at what point on the wait-time-continuum do call center agents need to shift their approach to customers? The answer will be different for every industry and business partner, but the good news is that you already have all the data you need to answer this question! By “marrying” customer survey records to the wait time they experienced, you can determine the point in (wait) time when overall customer experience and brand loyalty begin to significantly degrade.
What is your customer-centric tipping point for Average Speed of Answer (ASA)? Or do you use a call center benchmarking status quo? When do your agents know to do something different? Are you coaching and training them to be more customer-centric (and successful) with Business Intelligence such as this? One thing is certain…your customer expects it.
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