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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

Our recently released research into next-generation customer analytics shows that the most participants (52%) use spreadsheets as a customer analytics tool. I recently wrote that while these popular tools are adequate for some tasks, they are not suitable for analyzing large volumes and many types of customer data. So I think it is appropriate that one in four (26%) participants have adopted a dedicated customer analytics tool and a further 29 percent are planning to invest in such a tool in the next 24 months.

There are good reasons to use a capable tool for this critical areavr_Customer_Analytics_08_time_spent_in_customer_analytics of analytics. The research shows that the biggest issue for companies in producing customer analysis is data; users spend most of their time preparing (47%) or reviewing (43%) the data before they can perform any analysis. If companies don’t take action to correct this, the situation is only going to get worse. In my various research I have identified 23 sources of customer data; they include transactional data in business applications such as CRM, ERP and knowledge management, call recordings, text-based interaction data such as letters, forms, text messages, chat and Web scripts, event data such as agent desktop clicks as they try to resolve interactions, and social media posts. So not only do users have very large volumes of data to deal with, but the data comes in many different formats, several of which are unstructured. To produce as complete an analysis as possible, companies need systems that can handle almost all of these sources of data, that can automate the process of extracting the data from them, and that can standardize the data to ensure it is of the highest quality and all data relating to a single customer can be integrated. I believe that making the right product choice for customer analytics depends first on what and how much data it can process.

That said, the research reveals some other factors that impact the choice of customer analytics including real-time (21%), advanced vr_Customer_Analytics_06_most_important_customer_analytics(19%), statistics (14%), predictive (12%) and visual (10%) as first ranked priorities. Many customer-related tasks require information that is as up-to-date as possible; for example, a contact center agent needs to know what a customer attempted to do before calling the contact center so the response can be put in the context of previous interactions as well as the customer’s profile. Product evaluations thus should look for systems that not only process all forms of data but that can collect the data in real time or near real time and produce the analysis likewise. Another factor is that in dealing with customers, it is increasingly important to have predictive capabilities. To keep up organizations must move from relying on historical analysis to predicting likely future action; for example, an unusually high volume of complaints might lead to customer defections, and real-time capabilities could, for example, indicate when a negative post on social media is likely because of what the customer is saying during a phone call. I suspect that the majority of users who rely on spreadsheets do not have high expectations about the way the results are presented. But I believe that as engaging with customers becomes more complex, users will need information presented in more visual ways that help them quickly see areas that need addressing or present the data in more useful forms, such as showing the customer’s location on a map to help find the nearest service engineer to deal with an emergency.

Ventana Research tracks six technologies that are changing the vr_Customer_Analytics_07_new_technologies_for_customer_analyticsways users access and consume technology that our research finds important beyond analytics itself: big data (60%), cloud computing (44%), collaboration 62%), mobility (38%) and social media (35%). My research into next-generation customer engagement shows that companies expect analytics to have the greatest impact on the way they engage with customers in the future; more recognize that without a complete view of customers it is hard to develop a focused customer service strategy, enhance customer-related process, provide personalized responses to interactions or understand how their company is performing from the customer’s perspective. Each of the other five next-generation technologies is also having a direct impact on customer analytics. By whatever definition you use, customer data is “big” – it comes in large volumes and in multiple forms, has to be processed in real time and requires predictive capabilities. Increasingly more of it resides in the cloud and must be integrated with on-premises data, and many companies are looking to cloud-based services for customer analytics. Because many business units engage with customers they should share a single set of customer reports and analysis so that all actions and decisions are based on the same information. To do this, more companies are looking at collaborative capabilities that allow users to share customer information and work together on actions such as resolving customer issues. In addition many employees need access to customer data while away from their desks; nearly two-fifths (38%) of participants in the customer analytics research said that mobile access to their customer analytics systems is important. And finally, there is no doubt many consumers use social media, and more are doing so all the time; many of these users are also employees, and they want their work systems to be socially enabled. Add to this that companies need to understand what their customers are “saying” about them on social media, so at the very least a customer analytics system should be able to processes social media data feeds.

One of the latest buzz phrases is the Internet of things, which will serve the connected customer on more devices than ever. People now engage with companies increasingly electronically, often using smart mobile devices – they are more connected and can do things much faster than ever before, including look elsewhere if they are not satisfied with a company. Knowing your customers therefore has never been so important. Ventana Research recommends that you evaluate the options now available in customer analytics tools to help improve customer service and the outcomes of customer engagement.

Regards,

Richard J. Snow

VP & Research Director

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