Which side of the self-service double-edged sword is cutting into your call center’s results? While self-service applications have delivered many benefits over the past several years, such as lower agent costs and speedier customer service, the systems have also delivered several negative side effects, like silent customer attrition and agent skill misuse.

Left untreated these self-service pain points will get worse. But there are now innovative remedies to help companies cure their most painful self-service problems. Today call centers are deploying predictive analytic solutions that add high-impact customer intelligence and proactive capabilities.

These technologies elevate self-service to a much more refined and profit-centric customer service tool by pulling unique sets of customer data from companies’ existing databases, and processing the data in innovative ways to address key questions including:

• What is the value of the customer you’re sending to the IVR?
• What is the cost of an agent’s time versus the value of this call?
• Is this customer likely to attrite without proactive interaction?

Cures for Your Self-Service Pains

Predictive analytic solutions that effectively answer these questions heal today’s three leading self-service pain points.

PAIN POINT 1: Treating all inbound customers the same.

CURE: Identify and deliver optimal treatment to each individual customer.

While self-service technologies have allowed companies to segment inbound callers into broad general categories, such as gold-card members, they have not provided ways to segment inbound calling customers by their value to the company and their probable behavior. The result is that high-value customers who would accept sales offers often fall into queues behind low-value customers who won’t. What’s needed it a way to move high-value customers up the service hierarchy.

New inbound predictive analytic solutions predict the value and behavior of every inbound caller against the value of all other inbound callers, as well as the call center’s available resources — before each caller chooses their own destination. The solutions then assign each caller an appropriate service level, such as sales agent, retention agent, or automated self-service, and priority routes them there. As a result, the highest value customers receive the optimum levels of service available at all times.

This solution worked perfectly for a leading national financial services company that wanted to both increase sales with inbound callers and lower agent talk times. By making offers only to callers who both selected live agent services and ranked high on “potential to accept cross-sell offers,” the company both boosted sales success rates and lowered agent costs.

PAIN POINT 2: Rising customer attrition.

CURE: Target high-value customers for retention offers.

Aiming to lower costs, companies have expanded self-service to the point that customers often feel lost in a sea of “press # now” commands. The most disgruntled are clicking a button to close their accounts and move on for good. High-value customers are typically the most intolerant of disconnected relationships. When their needs aren’t met, they terminate relationships and go elsewhere. Yet, in many self-service systems they are often kept waiting behind a long line of low-value callers; or they are left alone to close their accounts in self-service without first receiving retention offers.

To reduce the loss of high-value customers through silent attrition, companies are adding predictive analytic solutions to their call-routing capabilities. After analyzing customers’ value and risk of attrition, the applications route callers to the appropriate service levels. For example, they send high-value customers who are at risk of attrition to retention agents and route low-value customers who want to close their accounts to self-service.

A large credit card company recently found itself at risk of losing high-value customers to competitive offers. So, it deployed a pre-call-routing predictive solution. Now, high-value callers at risk of attrition are sent to retention agents. The results have included both higher levels of customer retention and higher customer satisfaction.

PAIN POINT 3: Not maximizing agents’ time.

CURE: Align agent skills with each customer’s propensity.

To truly optimize self-service technologies requires knowing not only when to use them, but also when not to. There are times when high-value customers should be routed directly to agents for retention or upsell actions — versus completing calls in automated systems.

To assist in this critical pre-call-routing step, intelligent predictive analytic solutions can not only predict the value of each inbound caller and rank them according to the value of every other caller, but also determine the ranking of callers according to agent skill levels and availability. With the right analytics on the job, companies ensure that their agents’ time always delivers the highest ROI.

This proved true for a leading telecom company that wanted its skilled agents to talk only to customers who were most likely to purchase. By deploying a pre-routing predictive analytic solution in its inbound operations, the company directed the flow of inbound callers exactly where it wanted them to go. As a result, it achieved its goal of maximizing its skilled agents’ time, while boosting sales.

Healthy, Wealthy, and Wise

To build profits in today’s call center, the goal cannot be only to complete more calls in self-service. Instead, what’s needed is an intelligent way to predict several vital insights including: the future value of all incoming callers, which actions support the optimum result, and where agents’ time will be spent the most wisely. By curing their chronic self-service pains, contact centers will elevate self-service to the high-value tool it was always meant to be.

Tom Evans leads product management efforts for Austin Logistics (www.austinlogistics.com). He’s responsible for driving the product direction and go-to-market strategy for Austin Logistics’ predictive analytic solutions. Tom has over 20 years of high-tech business experience helping start-ups and Fortune 500 companies plan and launch new products. His experience crosses sales, marketing, business development, and engineering across multiple industries, including financial services, call centers, manufacturing, and defense. Tom has a Bachelors degree in Electrical Engineering from the United States Air Force Academy and an MBA from the University of Texas at Austin. Tom can be reached at tevans@austinlogistics.com. Austin Logistics