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By its own admission, SAS has a very large software portfolio (of more than 250 individual products), and it continues to develop and release more products and updates to existing ones. Some of the products are sold alone, and others are bundled into “enterprise solutions”. Some are for technical users, and others are business applications. This complexity can make it hard to identify which product or bundle serves a particular need. Three are most relevant to my research practice: Customer Intelligence (CI), which I wrote about after attending the 2013 SAS European analysts event; SAS Visual Analytics; and a new one, the Customer Decision Hub that SAS has developed to support multichannel customer engagement.

When I last wrote about Customer Intelligence I noted that it was designed mainly to process structured customer data (such as found in CRM and ERP applications and customer data warehouses) and the analysis it generated was largely for use in marketing. At this year’s analyst event SAS highlighted several developments, but most are to support marketing better, although some directly impact customer engagement. One of the challenges in understanding CI is that it is a bundle of 11 products, and that doesn’t include products that are part of the underlying SAS technology platform. Of the 11 business applications, six relate directly to marketing, and one, SAS Profitability Management, allows companies to understand and manage profitability at a detailed level. The remaining four products relate more to customer engagement: SAS Customer Link Analytics (designed to identify the communities in which customers interact), Real-Time Decision Manager (to deliver personalized offers derived from rules-based analytics), Adaptive Customer Experience (to create profiles of customers based on interactions and other customer data) and Social Media Analytics (to view and analyze customer activity on social media). Collectively the CI bundle of products supports the end-to-end marketing process, but a lot of the capabilities also relate to sales and customer service. The issue for potential customers thus becomes which of the products directly serve their business objectives and what impact picking among them has on pricing, implementation and ongoing operations.

SAS Visual Analytics is a product that makes it possible for business users to create and run their own analytics. This is especially relevant in the contact center and customer service business units. My benchmark research into next-generation customer analytics shows that unlike most other business units, these tend not to have data scientists or analysts to help them produce analysis of customer-facing activities. Instead they rely on managers to produce their own reports and analysis, and as the research shows, they rely heavily on spreadsheets to do this. After IT sets up access to the right data stores, Visual Analytics helps business users create their own analysis requirements and run these against the data sources to produce the analysis and metrics they need. It thus enables managers to keep up with the ever increasing demands of customers and to base decisions on the most up-to-date information, without having to rely so much on IT assistance.

My benchmark research into next-generation customer engagement shows that customer engagement is a multichannel task that is carried out by multiple business units, which CI and Visual Analytics both support. Companies thus need to recognize that vr_Customer_Analytics_09_technology_used_for_customer_analyticscustomer engagement is a cross-business responsibility that should be based on a single view of the customer, be rules-based to ensure consistency and the best possible outcomes, and should use multiple forms of analytics, on all sources of customer data, to provide the analysis and metrics to monitor and assess past performance and influence future actions. My benchmark research into next-generation customer analytics shows that many businesses have not made this transition yet and still rely on tools not suited for these tasks. The most common tool (used by 52% of companies) to monitor and assess customer-related activities is spreadsheets; only 26 percent have deployed a dedicated customer analytics tool. While spreadsheets have their place, they cannot process unstructured data and cannot work in real time to provide advice such as next best actions a contact center agent should take while talking to a customer.

To meet these requirements SAS has developed its Customer Decision Hub. This bundle of products can help businesses achieve an omni-channel experience ­– that is, consistent, personalized and in-context experiences at all touch points. It includes APIs that allow businesses to capture all customer interactions in real time or batches, regardless of channel, including unstructured interactions such as calls, email, text messages and social media posts. The hub can also capture data about marketing, sales, service and other ad hoc actions. It uses this data to produce analyses, insights and metrics about those actions, put them in context and show history, risks and potential opportunities. The hub also has a rules engine that can recommend actions and the channel through which to communicate with the customer; among the focus of rules are priorities, strategy, constraints, customer preferences, channel restrictions, budgets and contact permissions. The optimization engine is set up using SAS CI Studio, which uses drag-and-drop techniques to create intelligent, rules-driven workflows to create more relevant, personalized customer experiences. The hub thus links external, customer-related interactions and internal processes to provide the analysis and orchestrate actions.

This combination of products, if used properly, could help companies improve customer experiences that cross the boundaries between marketing, sales and service. However, as with CI the Decision Hub includes many applications and capabilities, and much of SAS’s messaging relates to marketing, which I don’t believe does the package justice. Potential customers should make the effort to understand what products are included in Decision Hub, what is involved in running it and the impact it is likely to have across the organization.

One of the strengths of SAS is its range of products, but this can also be a weakness. In their own right, each product supports a robust set of capabilities, but choosing the right set to meet a specific business need seems to be a complex process that often involves third-party consulting services. My colleagues wrote about SAS recently on its focus on business analytics and it work to unify big data across business and IT that also demonstrate how they are bringing many products to a singular focus for business and IT. Also in my view both SAS CI and the Customer Decision Hub focus too much on marketing and not for use across the business. Anything to do with customers is an enterprise issue, not a departmental one. Improving the customer experience is now such a critical issue that companies should look beyond some of the marketing messages and carefully evaluate how SAS can support their customer interaction and overall engagement efforts.


Richard J. Snow

VP & Research Director

vr_Customer_Analytics_09_technology_used_for_customer_analyticsLast year I assessed how Nexidia had advanced its products to support customer interaction analytics. Since then the market has changed, and Nexidia continues to expand its products to meet a broader set of needs for analyzing and optimizing customer interactions. Companies are recognizing that they need complete information about their customers, including interactions, and need to change the metrics they use to monitor and assess customer-related activities. My research into next-generation customer analytics shows that the most common tools used to produce customer analytics is spreadsheets (52%) and only 26 percent of companies have implemented a dedicated standalone customer analytics tool to help them respond to these requirements; however, the results also show that more companies plan to adopt dedicated customer analytics products in the next 12 to 24 months. For good reason as spreadsheets are known for errors that impact business and use of general BI tools can lengthen the time to value and not support the specific data and analytic needs like that needed in customer interaction analytics.

Companies face challenges in keeping up with customers’ use of more channels of communication, their increasing expectations for service, and the huge volumes and many types of customer data they are now generating. They also need to understand the behavior and sentiments of customers and of their own agents. The latest version of the Nexidia product, Interaction Analytics 11, that was announced has been designed to meet these challenges. At the heart of the product is the ability to extract information and insights through speech analytics, which Nexidia has focused on since its inception.

In general speech analytics comes in two basic forms: automatic speech recognition (ASR) and phonetic speech analysis. There are some differences in the two. ASR essentially searches through audio recordings to spot words previously specified by users. To prepare to use it, a company must create a dictionary of words its users want to find; then they run the software against the audio source (typically call recordings), and it produces an analysis showing where and how often it found the words, the contexts in which it found them and trends such as a word appearing more often than in previous analyses. To change the words being looked for, users have to change the dictionary and repeat the process. The same applies to working in another language: Users create another dictionary and run the process. In the fast-changing world of customer engagement, such a process can be too slow and cumbersome to keep up with business demands.

In contrast, phonetic analysis, which Nexidia uses, doesn’t require a dictionary. The software runs against any audio source and creates a phonetic index that is time-stamped. Users create structured queries that the software uses to find and analyze a raw audio source, and produce the required reports and analysis in much the same way as ASR does. In this case users can concentrate on building queries, and new ones can be run against the same audio source; it requires no new dictionary and works in any language by changing the language pack included with the product.

Interaction Analytics 11 takes this process further, into what Nexidia calls neural phonetic speech analysis. In addition to audio, the software can process text data from sources such as email, surveys, chat scripts and text messages. The system uncovers information from these combined sources and applies the analysis, thus giving users a fuller picture of customer interactions. The system can also use predefined rules to uncover agent and customer sentiment, adding another dimension to the analysis. The outputs from the initial discovery phase and sentiment analysis can be run against prebuilt models concerning business issues such as customer churn and retention or sales effectiveness. All of these outputs can be included in enhanced reports and analysis, which can be extended to include key metrics.

As well as adding these functional capabilities, Nexidia has rearchitected the product to be more scalable and able to process data vr_Customer_Analytics_06_most_important_customer_analyticsin parallel, which enhances performance and delivers results faster. Version 11 has a new user interface and enhanced analytic visualization capabilities, which make it easier for users to interpret the outputs, take appropriate actions and share the information with others. The new architecture also can run analysis in real time, which my research into next-generation customer analytics shows is the number-one capability companies are looking for and first ranked in a fifth (21%) of organizations; doing so can, for example, advise supervisors if agents are saying something wrong or are about to close a call without giving required disclaimers.

Ventana Research believes that to derive full benefit from any application, especially analytics, it should be used within an overall performance management framework. This should include three steps: Understand, Optimize and Align. In this context, Understand uses batch and real-time analytics to show what has happened and what is happening; Optimize uses these outputs to decide which changes are required; Align creates an action plan to ensure the changes are brought to bear. The analytic process uses metrics and is cyclic so that repeating the cycle shows the impacts of changes and other changes needed going forward. Nexidia supports a similar approach, which uses its discovery tools to reveal what has happened and what is happening, root-cause analysis to understand why it happened and a metrics-driven approach to improvement. For example, companies can enhance quality management by analyzing more sources of data (such as surveys, customer feedback and call recordings), understanding drivers of agent and customer satisfaction, and fine-tuning coaching and training to improve the customer experience. My research into the agent desktop and customer service shows that very satisfied agents twice as often as those less satisfied deliver on key customer-related metrics such as customer satisfaction, net promoter scores and first-time interaction resolution.

Nexidia’s products give users insights into the processes connected with improving the customer experience. I recommend that organizations examine how Nexidia can help improve the outcomes of customer interactions using its next generation customer analytics called Interaction Analytics.


Richard J. Snow

VP & Research Director

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