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Predicting the Outcomes of Customer Engagements

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In the contact center industry, customer engagement is our bread and butter. And, as Gallup’s recent State of the American Consumer reports, “When it comes to winning over customers, successful businesses know that everything depends on their customers’ experience with their organizations.”

The mission of the BroadSoft CC-One team is to provide companies with cloud contact center solutions that not only improve engagement, but build long-term loyalty with customers.

One of the opportunities that we see for improving engagements lies in a contact center’s ability to model interactions between customers and agents in order to predict customer-agent outcomes. These statistical and analytical models can be used both to predict customer propensity, and to rank agents and recommend optimal matches between the two.

The figure below breaks customer interactions into two categories, sales and service, and the intent of each participant. When customers contact a company to buy their goods or services, for example, call centers connect them to sales agents who can sell and take orders. When customers call to cancel their orders or subscriptions, companies connect them to service agents who can save the orders and retain those customers.

The outcomes of these customer engagements are impacted by a combination of Customer Propensity (to buy or cancel) and Agent Ability (to sell or save).

A customer’s propensity to buy a company’s goods or services is typically driven by several key attributes such as demographics, behaviors and life changes – all of which are relatively objective and easy to model and use in predictions.

Agents on the other hand, bring with them talent, motivation, experience and education, which are not all objective attributes, so more is needed to determine their ability. The secret lies in a contact center’s ability to analyze data from past agent-customer interactions, and then develop models that can predict agent behavior. By combining their attributes with their past performance over an extended period of time, their future performance can be predicted.







BroadSoft CC-One’s Statistical Performance Analytics looks at historical data of customer behaviors and agent performance, combined with their personal attributes, to predict customer propensity and agent ability. From there, it’s simply a matter of matching customers with the agents that can best handle his or her call. That means more satisfied customers, fewer abandoned calls, greater engagement and increased revenues.

We’ve seen a lot of negative news in recent months about customer service gone wrong. When it comes to the BroadSoft CC-One team, our goal is to continue building cloud contact center products and solutions for the omni-channel contact center that help companies make the most out of every customer interaction. To see how we’re currently doing that, check out the CC-One customer service analytics solutions, and stay tuned for more exciting news in the near future.

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