In the current economic climate in which businesses have great difficulty maintaining profit margins, it has become increasingly important to ensure existing customers are profitable. Businesses can rapidly turn their existing call centers into profit generating operations by predicting the needs of the individual customer in real time, during customer contact. Several companies have already successfully implemented predictive analytics software to achieve cost reductions in the call center, without any detrimental effect on service levels, and generate more profit by turning each customer interaction into a sales opportunity.

Marketing departments have already discovered the value of analyzing customer behavior and accurately predicting their future needs, profitability and risk. In the past, marketers often executed mass direct mail campaigns, with the same message being sent to each recipient. Using analytics, they are now able to do small and event-driven campaigns that are closely targeting individual customers. This has resulted in a substantial reduction in mail volumes and costs, and higher response rates.

Analytical technology is now also increasingly being deployed in call centers. A call center conversation is by definition a one-to-one contact and therefore an ideal opportunity for a fully personalized customer approach. Additionally, it is a rich source of new and up-to-date behavioral information. There are many ways in which the ability to predict and act upon customer needs in real time can contribute to reducing costs (1-3) and increasing revenues (4-5).

• 1. Differentiation in type and level of service. Not all customers want the same service. Investing in a customer relationship can sometimes mean providing a better service, but could equally be achieved by adjusting aspects such as the purchase price or functionality of the product. Analyzing customer data helps predict the messages that will appeal most to an individual customer. A British mobile telecommunications provider differentiates its offers and service as a function of customer needs. Studies have indicated that teenagers choose this provider because its telephones feature the most gadgets this group does not find service to be that important. Consequently, the provider decided to reduce service levels for this group. This made it possible to more effectively implement a three million pound expansion of the call center.
  
  
• 2. Differentiation in sales offers. Another mobile telco provider improves business results significantly by differentiating commercial offers to customers. It calls its best customers every year to remind them to renew their contracts, offering a substantial discount. Analysis of customer behavior indicated that some customers are so loyal that they will renew anyway. Other customers will churn in any case, hence don't need to be called. This operator now uses predictive analytics software to indicate the best approach for every type of customer, saving costly call center time and reducing the costs of unnecessary discounting by hundreds of thousands of Euros.
  
  
• 3. Predicting call center workload.
  Staffing costs account for a major proportion of call center expense. A travel operator's use of analytic software to predict when a customer likes to travel has led to improvements in travel capacity planning. This organization realized that if it were capable of predicting when somebody would travel, it could also predict when customers would get in touch with the call center to book their trips. This lead to a further reduction in call center costs of five percent.
  
  
• 4. Targeted calling campaigns. A bank used to do costly, mass outbound calling campaigns, which generated low response rates and even led to a high degree of irritation among customers. Using analytics, the bank is now able to predict whether individual customers are likely to be interested in a product only then the customer receives a phone call. The prediction is based on the most recent behavioral data, so that the cross-sell offer is not only relevant but is also made at exactly the right moment.
  
  
• 5. Targeted cross-selling and retention offers in a service call center. Some direct insurance companies have expanded their service call centers by introducing sales recommendations. After the service aspects of the call have been successfully handled, the agent puts forward a sales offer. Past experiments with standardized offers for all customers proved to have such a low chance of success that they were frustrating for both the customer and the call center agent. Currently, the ability to predict during the conversation that the customer is likely to respond positively results in a cross-sell suggestion during the call. The chance of a successful sale is very high (about 40 to 50%). The customer feels respected because the offer matches his needs. This approach has resulted in an improvement in call center sales performance of up to 50%.

In addition to reducing costs and generating increased revenues, predictive analytics software also offers companies an important strategic advantage: the business can identify an individual customer's wishes much more accurately and use this information in real time to approach the customer effectively. This results in enhanced customer loyalty and greater customer value. Intimate knowledge of customer needs truly allows a company to differentiate itself from its competitors and ultimately achieve market leadership.