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Tuesday, August 6, 2013

Predictive Analytics in Action: Part 3 Demand Conversion

By Bernie Lillis, Vice President of Strategic Solutions for Insurance Dialogue

As discussed in part 1 and part 2, brands must take an analytical and predictive approach to better anticipate consumer behavior on an individual basis. The predictive approach can be broken down into three parts that can work together to manage a fully engaged experience: demand creation, demand activation and demand conversion.

Demand conversion removes the guesswork of contacting leads by determining the best dialing strategy. Conversion-based routing and predictive analytics predict the right message, product, incentive, and channel to effectively convert a lead through the call center.
With the use of predictive technology in the call center, a company can now route a call to the call center agent with the highest probability of converting based on common behavioral attributes, also known as conversion-based routing. This allows for the consideration of multiple attributes, including gender, location, product type, and CRM data. This process increases the efficiency of a company's sales pipeline by analyzing the prospect database to determine which customers are of greatest value and will close the fastest. Removing the guesswork reduces acquisition costs when trying to contact leads by determining the best dialing strategy. Through predictive technology tools, all historical data, including attempts, contacts, call outcomes, feedback, and recorded conversations, can be captured in real time for each lead to give clients a complete view of interactions. This information helps determine which lead sources to buy from again. Reported information is then put back into the artificial intelligence engine to further analyze and predict the value of a lead.
By capitalizing on the power of predictive analytics, a performance increase averaging 120 percent is not uncommon. Success depends on numerous factors, though, including the accuracy of the data that is used to train the model and the scale of the program that enables multiple paths.
To learn more on the benefits of predictive analytics, read part 1 and part 2 of this article and sign up for our August 15 Best Practices Panel presentation.