Pegasystems App Susses Out Customers' Soft Spots
Pegasystems has rolled out the newest iteration of its marketing application. New or enhanced features include improved ability to perform real time event-triggered actions and new B2B recommendation functionality.
The key to understanding this app is its name, "Next-Best-Action," said Steve Kraus, senior director of product marketing.
"That is the concept behind our approach to marketing," he told CRM Buyer. "How can we help the user decide what the next best action is to take with a customer in a real-time scenario?"
400 Percent Conversion Increase
Say Verizon is trying to cross-sell something, but the customer refuses the first offer. Using Next-Best-Action, Verizon would be able to decide what might be the next appropriate offer to make while weighing any number of factors. For example, how much margin does Verizon want to make off the client? How much total value does the customer represent to Verizon? Are there signals the customer may intend to bolt?
Taking all of that into account, Kraus said, "the system is then able to dynamically recommend the best plan to that particular customer."
The system is self-learning as well, making it easier to carry out such analyses in subsequent calls or with different clients.
This real-time interaction and analysis has led to a 400-percent increase in conversion rates among Next-Best-Action's users, said Kraus. "That is what our customers have measured and told us they were experiencing."
Next-Best-Action has stepped up the real-time event triggering analysis, Kraus noted, "so if a customer has called twice about the same issue, the system will trigger a new scenario for the rep based on the previous calls and the unresolved issue."
Consider a retail store that has geofencing capabilities. When a customer who has signed up for offers walks into the store, the system can beam an offer to the individual's device, basing it on past purchases, Kraus explained.
Another feature is aimed at the B2B community -- the ability to analyze the buying patterns of a company and then make the best recommendations based on usage and corporate needs.
"We can do the same kind of analysis at the household level," Kraus said.
In addition, the system allows for campaign simulations, he noted. Some companies might program a particular user base for a marketing campaign but leave out 20 percent of the group to test a simulated challenger campaign.