Calabrio & Amazon Connect: Paving a Path to the Cloud for Every Size Contact Center

One of the main reasons I joined Calabrio nearly two years ago was because the leaders and I shared a vision for the immense benefits a cloud-based contact center strategy could bring to customers worldwide. Since then, we’ve realized that vision and remain committed to making it easier for customers to gain the cost and agility benefits of deploying Calabrio in the cloud (proof point: just last week we announced Calabrio attained Advanced Technology Partner status in the Amazon Web Services (AWS) Partner Network!).

I consider Amazon and Calabrio to each be market disruptors in their own right, and I’m extremely proud of the work we’ve done together. Why do I think Calabrio and AWS are so compelling for customers? Here are two good reasons:

• Calabrio was the first workforce optimization (WFO) provider to partner with Amazon Connect. Calabrio was first to recognize that combining Amazon Connect—Amazon’s cloud-based contact center service that makes it easy for any business to build a robust, intelligent, omnichannel contact center in the cloud—with a cloud-based WFO solution creates one seamless platform for a better customer experience. Our customers benefit from that early connection and the strong relationship with Amazon Connect we’ve developed over time, along with a strongly-aligned go-to-market strategy.
• Unlike most other WFO providers, Calabrio is both an AWS partner and an Amazon Connect partner. The cloud-based version of Calabrio’s platform is built on AWS, so businesses considering the Calabrio ONE Cloud know AWS has certified it to run reliably, with the flexibility, scalability and pay-as-you-go offering of AWS.

Deployed together, Amazon Connect and Calabrio ONE give businesses a complete Contact Center as a Service (CCaaS) toolset. With integrated capabilities such as call recording, quality management, workforce management, multichannel analytics and Data Management—contact center leaders can more efficiently and effectively schedule agents, monitor performance and identify opportunities to improve the customer experience.

And we didn’t stop there. We’re leveraging artificial intelligence (AI) and machine learning in our joint solution to make mathematical approximations of both agent and customer behavior, and intelligent predictions about outcomes that most affect customers and the organization that serves them. Things like recognizing customer dissatisfaction indicators in time to remedy negative situations and retain customers, and discerning between customers bluffing they’ll drop a service and those that actually will churn.

For instance:

1. Predictive NPS. By assessing completed customer surveys and speech phonetics data, we identify the customer interaction characteristics that most impact satisfaction scores—such as the amount of time between the first reply and subsequent replies, whether text responses with similar wording result in satisfied customers, and how much agent effort is put into resolving the customer’s issue. The technology then uses this information to generate a predictive Net Promoter Score (NPS) for all customers—even those who didn’t complete a survey or otherwise provide feedback—and essentially tells the business whether a customer interaction will lead to a positive or negative customer experience. As a result, leaders can make more informed decisions on things like agent evaluations or customer outreach because they’re basing decisions on 100 percent of customer data.
2. Predictive evaluation. Predictive evaluation works in a manner similar to predictive NPS, but it applies a mathematical model to phonetic speech hits and previously scored quality management evaluations in order to identify the interaction aspects that make the biggest impact on quality scores. Using the resulting generation of predictive quality evaluation scores for 100 percent of customer interactions, evaluators are better equipped to identify and evaluate the right calls, and make better decisions regarding which agents may need additional coaching.
3. Sentiment analysis. Sentiment analysis leverages a custom-designed, contact center-focused lexicon in order to automatically score each call’s sentiment (positive, negative or neutral), so managers can spot sentiment trends as they happen, and quickly adjust accordingly the areas of the business that impact the customer experience. Freed from having to wait for lagging feedback sources like sales surveys or post-call surveys to understand the voice of their customer, managers instead use constantly evolving sentiment scores to identify prime opportunities for agent coaching and decide how to handle emerging issues.

If you’d like to find out more about what the joint Calabrio/Amazon Connect offering can do for your company, come see Amazon’s Joe Eisner and I co-present at Enterprise Connect 2019 in Orlando next week.

Customer Experience Intelligence with Calabrio and Amazon Connect
March 19, 2019 at 12:10pm, booth 2106