NICE Systems and Customer Engagement Analytics


I recently wrote about NICE Systems’ acquisition of Causata to enhance its analytics capabilities and expand from workforce optimization into customer experience management. NICE recently released Customer Engagement Analytics, which is designed to analyze customer interaction data to help companies improve the customer experience at every touch point. NICE calls this optimizing the customer journey.

vr_NGCE_Research_05_who_handles_customer_interactionsThere are two aspects to the customer journey. The first is often called the customer lifetime cycle and includes moving prospects and customers through marketing, sales, service, retention and up-sales. The second is what is now called customer service: It addresses how potential and actual customers engage with the company to resolve issues. This includes such activities as learning about a product, buying it and using it, making a query about a bill, making a payment, reporting a fault and having it resolved. Both aspects involve customer engagement, which includes the touch points people use to engage with a company and the company’s response to their inquiries.

My benchmark research into next-generation customer engagement shows that this is complex, involving multiple communication channels and nearly every business unit. Understanding and refining how it all works together is the key to success, and customer engagement analytics is designed to give companies that understanding and guide their actions.

The concept of customer engagement analytics is straightforward. Companies should begin by capturing all interaction data, then sequence events to understand the customer journey and visualize the outputs so management can understand what happened. Next, analyze why the interaction began and why it took the path it did, and identify what can be changed either to remove the need for the interaction (for example, by improving product documentation) or to ease the customer’s way through it. To assist this process companies should use, in real time, analytics to personalize responses (for example, to notify the agent of what to do next and the information to provide to the person). Advanced analytics can learn from each analysis to improve predictions of future customer behavior and determine steps to prevent, mitigate or reduce issues.

To deliver these capabilities, NICE Customer Engagement Analytics has many key components. First, a connectivity layer supports the capture of interaction data from multiple communication channels, in real time or batch mode. In addition a series of APIs support the transfer of data from business applications such as customer relationship management (CRM) and customer data warehouses. Then there is what NICE calls a cross-channel interaction hub, which consists of real-time, recent applications and big data, plus aggregations and business intelligence (BI). NICE also offers a component , which in my mind is key. Multiple channels create multiple customer identifiers such as the customer’s name, address, email address, phone number(s) and Twitter handle. Multiple business systems also contain multiple identifiers such as account numbers, record numbers, names and addresses. The entity manager ties all these together so that the company can identify the people who make interactions  and map the complete customer journey. A component that has cross channel logic, which supports segmentation, categorization, path and pattern analysis, and filtering across all the data. A series of analytics modules analyze structured data as well as voice, text and event data. All of these fit inside an infrastructure that does system management and administration with workflow triggers and a rules engine, a set of APIs and a data model.

This is a lot of software, a combination of in-house developed capabilities, acquisitions and third-party products like IBM Cognos, which is available through NICE’s longstanding partnership with IBM. To simplify things NICE is adopting its normal practice of offering preconfigured versions of the product to meet defined business goals such as optimizing call volumes, the customer journey and interactive voice recognition (IVR). Along with canned reports and analysis, as I saw in a demonstration, point-and-click capabilities enable users to build new templates based on canned models or build their own.

My benchmark research into customer relationship maturity shows that a major differentiator for more than 90 percent of companies that described themselves as customer-centric was the use of customer journey maps that plan how to engage with customers over the full customer life cycle and across all communication channels. NICE Customer Engagement Analytics allows them to move from planning to monitoring, basing actions on actual customer journeys. I believe this is essential to meeting expectations of customers, agents and businesses regarding customer engagement. I recommend companies evaluate this software and how it can help with those efforts.

Regards,

Richard J. Snow

VP & Research Director

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