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In today’s intensely competitive markets, companies must strive to meet customer expectations during every interaction, and interactions occur through many channels. Our benchmark research into next-generation customer engagement vr_NGCE_Research_12_all_current_channels_for_customer_engagementfinds that customers use up to 17 channels of engagement. Some channels involve assisted service from employees of the company, and some use self-service technologies such as interactive voice response (IVR), websites, mobile apps and social media, also known as digital service. Although the use of self-service is increasing, the research finds that organizations still expect volumes of assisted interactions to grow, albeit more slowly. The research also shows that the employees customers interact with may work in almost any line of business, including marketing, sales, the contact center, finance and human resources. These challenges require organizations to focus on people, processes, information and technology to optimize the performance of the workforce.

To meet customer expectations during assisted interactions, companies must have the right number of skilled employees available, including cases in which the customer begins in a digital channel but switches to assisted service, and handle a number of functions:

  • Route interactions to the employee most likely to satisfy a particular customer and situation.
  • Build work schedules that match agents’ numbers and skills to expected volumes and types of interactions. To do that requires systems that are responsive and flexible enough to handle unexpected events such as sudden surges of interactions or employee illness and absences.
  • Assess the performance of agents and other employees handing interactions, and determine future training and coaching for individual needs.
  • Motivate employees to improve their performance.
  • Give employees access to all information and systems that can help resolve issues, as often as possible at the first attempt.
  • Enable employees to find and collaborate with others who can help resolve issues without having to call the customer back.
  • Assess the overall performance of interaction handling to ensure it remains within operational guidelines while meeting business objectives, and plan process improvements to optimize the customer journey and the agent experience.

To accomplish all these tasks, companies need to use a combination of systems, which vr_NGWO2_06_use_of_agent_workforce_applicationscollectively are known as workforce optimization. Our research into next-generation workforce optimization finds that the three types of systems most commonly in use are call recording (78%), quality management (70%) and workforce management (45%), although coaching (17%) and e-learning (15%) are the most likely to be deployed over the next two years. In each of these categories both conventional systems and newer, more capable ones are available.

Regarding the most popular kind we note that companies have been recording calls for many years but that few use them to full advantage. Typically, companies listen to a small percentage of calls and use this to manually complete agent performance scorecards. More innovative organizations use speech analytics systems that enable them to utilize all calls, automate much of the agent performance assessment process and most importantly link this information with customer feedback. Such companies record all types of interactions and use multiple forms of analytics systems to create an even broader picture of agent performance and customer experience.

Quality management systems are also mature but typically support a manual process of creating and filling out scorecards for different types of interactions. Here again more advanced systems use analytics to automate much of the process, including calculation of agent quality scores.

Workforce management systems typically use historical data about interaction patterns to produce work schedules that optimize available resources and meet operational targets. More advanced systems monitor employee performance against those schedules and include capabilities to optimize short-term agent utilization, for example by filling idle time with training and coaching.

Coaching and e-learning is a less mature category. Conventional products use the output from quality monitoring and analytics to identify coaching and training tasks, which can then be scheduled using workforce management. The most advanced systems can extract data from competed interactions to illustrate areas in need of improvement, or identify the performance of other employees who handle interactions using best practices.

As well as these advances in core workforce optimization applications, there are a number of potential game changers. The first is analytics. As I have already highlighted, analytics is increasingly important  to workforce optimization. Participants in our next-generation customer engagement research said it will have the greatest impact of any application on customer and employee satisfaction. Advanced speech and text analytics, used in combination with structured data analytics, can produce a comprehensive view of interaction handling, employee performance and customer satisfaction. It can spot trends and issues, and predict likely outcomes of future interactions. This information can be used to forecast future resource requirements, suggest process changes, identify training and coaching needs, and automate calculation of metrics focused on both customers and employees, such as first-contact-resolution rates across channels, customer effort scores, customer lifetime value and agent performance. These metrics can become an integral part of gamification techniques that track and reward agents for performance in day-to-day operations and for taking part in training, coaching and game-playing sessions to help hone skills.

Technology integration is having an impact of workforce optimization. Nearly half (48%) of participants in our next-generation workforce optimization research said that it is important for these applications to be integrated. If they are, what have previously been stand-alone processes can flow across application boundaries: For example, customer feedback can link to quality monitoring, analytics can support information-driven processes in multiple applications, and e-learning sessions can be automatically inserted into agent schedules. Integration of applications also supports a closed-loop approach to workforce optimization that uses analytics to assess past performance, identify areas for improvement and monitor the impact of changes.

Other innovations also are having impacts. Many vendors now support access to their systems from mobile devices so that employees can work away from their desk – for instance, supervisors walking the contact center floor – or home. Cloud computing opens up the opportunity for small and midsize companies to access capabilities similar to those designed for large enterprises.

As I said, our research shows that assisted service has and will continue to play a key role in interacting with customers, and these people and processes must be managed. Increasing demands from customers, multiple channels of engagement, greater volumes of interactions and more complex interactions all increase the urgency of deploying the right number of skilled employees to deal with customers. I therefore recommend to organizations that rely on outdated systems, particularly spreadsheets, to manage those tasks evaluate how more advanced, analytics-driven systems can improve the performance of employees and consequently the customer experience.


Richard J. Snow

VP & Research Director, Customer

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My colleague David Menninger recently wrote about the SAS Analyst Summit, concluding that “the SAS analytics juggernaut keeps on truckin’.” He observed, as I have done in the past, that SAS has a vast array of products that it regularly updates to keep up with market demand, ensuring it remains one of the premier vendors of data management and analytics systems. Dave’s perspectives provide in-depth insights into what these products do, while I focus on how they help with business outcomes around customer experience. I was therefore intrigued to hear at SAS’s European analyst event that its products support four types of user – data scientist, business analyst, intelligence analyst and IT analyst. The presenter used simple quotes to illustrate the differing priorities of these groups: For the data scientist, the one that caught my eye was “I need the latest algorithms to solve the latest problems”; for the business analyst I picked “I need to get my report done quickly and easily”; the information analyst is about “identifying patterns of interest that can prompt active decision-making”; and the IT analyst is about “issue resolution and redemption” (mainly operational analysis). In short each type of user needs different products and capabilities, hence the array of products. Nearest to my research practice is the business analyst, who wants easy access to reports and analysis to resolve business issues, and this is where the company’s Customer Intelligence product plays a part.

As I previously wrote this system has evolved into what SAS calls its Customer Decision Hub. It brings together a number of products so organizations can capture and synchronize all forms of customer data to “deliver the best customer experience.” The Decision Hub can gather customer data from a variety of sources and synchronize it for a customer. It provides rules to govern what happens to the data and how it is used, and reports, analysis and prompts so that employees can deliver information to users and customers in the most appropriate manner. It also includes an array of other capabilities such as information to drive marketing campaigns, data to support event-based interactions, customer journey maps and a 360-degree view of the customer. The latest version of the Decision Hub improves support and capabilities to better manage Web-based interactions, email, mobile and social media channels of interaction.

The next step in its development is SAS Customer Intelligence 360. It is an enhanced version that has a single HTML5 user interface, additional public and RESTful APIs so that data can be collected from third parties, a single data and decision hub for all things related to customer experience and support for both inbound and outbound interactions across all channels. It is available as a multitenant cloud-based service, but data can reside on the user’s premises. Customer Intelligence 360 includes four components. Master Audience Profile supports collection and synchronization of all sources of customer data, both internal and third-party, to build customer profiles. Workflow and Collaboration support creation and development of marketing content across multiple groups. Intelligent Orchestration manages engagement across channels to ensure that customers receive consistent information and to harmonize marketing programs. Unified Measurement and Optimization helps analyze the outcomes of engagement and marketing programs to optimize them in the future. Together these components enable organizations to build complete pictures of their customers, ensure that business groups coordinate how they engage with customers regardless of purpose or channel, and analyze the outcomes to improve them.

Some of these messages obscure what for mevr_Customer_Analytics_08_time_spent_in_customer_analytics is an important feature – the single customer data hub. Our benchmark research consistently shows that organizations have a diverse set of customer-related data source: business applications such as billing, CRM, and ERP; communications systems such as voice, email, text, Web and chat scripts and social media; and operational systems such as network control that provide event-based data such as calls made, films downloaded or energy used. Managing all this data creates issues for organizations. Indeed, nearly two-thirds of organizations participating in our research into next-generation customer analytics said that the data they need as input into customer-related analytics is not readily available. The research also finds that users spend more of their time preparing and reviewing data than they do analyzing the outputs, which undermines productivity and impedes getting actionable information to decision-makers.

SAS offers a combination of data management and analytics to overcome these issues. Buried inside the data management tools is another key capability – identity management. Our research into next-generation customer engagement shows that companies support an average of nearly seven communication channels, and each of these is likely supported by different systems. In such cases, each interaction record has its own unique customer identifier or combination of identifiers, which are difficult to standardize and use as one. To get close to producing a 360-degree view of a customer or a journey map of channels used, organizations need systems that link all these identifiers so the data can be associated with a single customer. The tools in SAS Customer Intelligence are among the few I have come across that do this; I recommend that companies looking for such analysis should carefully evaluate this product.

I support Dave’s view that the SAS juggernaut is rolling on, and systems such as Customer Intelligence can help companies improve customer engagement. However, as organizations evaluate such products, I caution them not to get bogged down in all the components but to look at the overall system and how it can ingest and manage all the organization’s data and any from third-party sources; scrutinize how easy it is to use for all the different potential users. It is also worth remembering that the early focus for Customer Intelligence was to support marketing, and many of its messages are still colored by such thinking. Everything I have seen and heard in recent briefings shows it is applicable across all customer-facing business groups, including the contact center, so I recommend organizations looking to improve enterprise-wide customer engagement evaluate how SAS can help.


Richard J. Snow

VP & Research Director, Customer

Follow Me on Twitter and Connect with me on LinkedIn

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