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Agent Performance Optimization: Making it Work for Mid-Size Organizations

Frost & Sullivan's recent research on the contact center, based on a survey of 305 contact center decision makers in the U.S., offers insight into how companies are focusing on the customer experience. One of the most interesting areas is around Agent Performance Optimization (APO)--a broad term that covers everything from workforce and performance management, quality monitoring, analytics and virtual agents.

As consumer expectations and actions change, so must the ways in which contact centers serve them--and that requires a clear understanding of how and when to best deploy and support customer service representatives.

Historically, large enterprises were the first to focus on the metrics around APO; now, our research shows mid-size companies are embracing the opportunity to fine-tune their contact-center operations to compete not only within their own markets, but also against smaller and larger competitors. Smaller companies can offer more personal, hands-on service, but not the same level of sophisticated knowledge as mid-size businesses; large enterprises, on the other hand, are often very good at crunching numbers, but they still lag when it comes to delivering a truly meaningful customer experience. Mid-size companies can find the sweet spot between the two poles, providing a high level of personalization based on clear data and analytics--if they get the set-up right.

Toward that end, respondents in the Frost & Sullivan survey expect to see increasing usage over the next two years among all the systems and applications we asked about. Virtual agents (avatars) are expected to see the highest growth rate (22%), followed by Web interaction analytics (17%) and Web collaboration tools (16%). Today, mid-size contact centers lead the way in terms of using performance management and big data analytics; in the future, they expect to adopt workforce management, Web collaboration and virtual agents more than their smaller and larger counterparts.

A quarter of agent performance optimization applications are fully integrated, while the largest proportion (41%) is mostly integrated. But by 2016, 51 percent of respondents expect to be fully integrated (a 132% growth rate), primarily due to the fact that 49% of "mostly integrated applications" are expected to transition to full integration.

When it comes to quality monitoring systems, 66% of mid-size contact centers use them to improve the effectiveness of marketing campaigns, a number that is significantly higher than that of small and large-size operations. Other tactics for used include identifying process improvements (63%); understanding customer behaviors, preferences and intentions (63%); and call recording, adherence, scheduling and training (59%). And fully 92% of respondents with mid-size contact centers say that if they could get timely data into the hands of their sales, marketing and product departments, they would use that information "frequently" or "extensively."

Of course, not all organizations see a clear road to leveraging such customer insights. Among mid-size organizations, the following roadblocks are cited: budget/cost (40%); lack of tools to capture the entire customer experience across all channels (39%); lack of tools to analyze the customer experience across all channels (38%); not a strategic priority (32%); lack of in-house resources (31%); and no process to use the insights even if they were able to capture and analyze the information (29%).

The take-away from all this data is clear: Mid-size contact centers can see a significant competitive advantage by embracing APO, especially when it comes to workforce management, virtual agents and web collaboration. Better still, companies that enable and leverage quality monitoring systems--so that they're not just collecting data, but also using it to drive decision making and improve the customer experience--will be in a much stronger position against not just other mid-size businesses, but even compared with large enterprises.

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