NICE Systems is an established vendor of workforce optimization products that has long included analytics in its portfolio. Its latest release in this area, NICE Customer Experience Analytics, focuses on mapping, understanding and managing customer journeys and metrics. The product is built on NICE’s common technology platform, which consists of three functions: collect, understand and optimize. The Collect segment has tools to help manage customer-related data and ingest data from multiple data sources; Understand uses analytics tools to analysis the data and produce reports, dashboards and other forms of output; and Optimize uses the outputs to help users improve business tasks such as improve customer satisfaction and net performer scores, suggest next best actions and reduce customer effort.
I have written that the proliferation of systems and channels of interaction creates major problems for organizations in dealing with the volume and variety of data they have to process to create a complete analysis of customers. Our benchmark research into next-generation customer analytics shows the extent of the problem; organizations most often cited issues in availability of data (63%), lack of skills (49%) and lack of flexibility (40%). In 2012, NICE announced a partnership with IBM to use its big data analytics software, including InfoSphere BigInsights, and this remains the basis for its Collect segment. It provides capabilities to process all forms of customer-related data, including interactions and transactional data in both structured and unstructured forms, and to produce two forms of analysis – voice of the customer and interaction analytics. However, our benchmark research uncovers an even bigger issue, that of linking customer identifiers so companies can tie interactions from different channels to a specific customer to discover, for example, that an email message from one address is from the same customer as a phone call from a certain number. One of the key differentiators of NICE Customer Engagement Analytics is that the Collect platform includes an independent database and over time extracts identifiers from different systems and stores them in a common customer record, thus allowing linkage of interactions and transactional data. It can do this using current data, or if a new identifier is added, it can go back and tie all previous interactions from that channel to that identifier, thus updating the customer view.
The Understand layer of this product has a collection of tools that analyze the data in different ways, visualizes the outputs in different forms, and creates triggers and alerts to ensure action is taken. It includes NICE’s speech and text analytics products that can derive insight from call or text-based data; these tools combine data to help users understand why customers engage with the organization. It also has a tool that visualizes customer journeys from one channel to another. It includes capabilities to analyze journeys and flag alerts on the resulting maps to show, for example, that a certain type of interaction often results in an undesirable business outcome. There is a predictive modeling tool that uses historical data to predict possible outcomes such as potential defectors. A pair of tools create actions that are required to happen in real time and recommend next best action based on the customer’s profile and data collected during an interaction; for example, it can advise a contact center agent on a possible upsell opportunity during a conversation.
The output from these tools is used as input to the Optimize layer. This can be done in two ways. When the required action involves the use of a non-NICE system, that is flagged in a report, analysis or alert. For actions that involve other NICE products they can be automated by sending data or a transaction directly to that system. This has the advantage of “closing the loop” and ensuring that issues identified through NICE Customer Experience Analytics actually are taken; for example, a training session could be inserted into a contact center agent’s schedule. The product is modular so organizations typically choose a key business issue first and adopt just the modules needed to solve it. The product is offered in the cloud as software as a service, so it is easy to select just the required modules and deliver the outputs where they are needed.
I recently wrote that, although it is not easy to achieve, it is essential for organizations to “know” their customers so they can optimize business with them. For me the key is that the more data that is analyzed, the more complete the picture becomes, potentially leading to better decisions and actions. NICE Engagement Analytics facilitates this because it can ingest all forms of data, and its identification management capabilities can tie interactions, and thus customer journeys, together. The system is designed to ensure actions are taken, and the focus is on key business targets. Our next-generation customer analytics research shows that many companies use spreadsheets to try to reach this goal. However, these personal productivity tools simply cannot deal with the complexity of customer engagement. I recommend that companies assess how NICE Engagement Analytics can help them improve the customer experience and business outcomes.
Richard J. Snow
VP & Research Director