4 Ways Analytics Can Transform Your Contact Center



As consumers have become more adept at finding information via search engines and Web sites, there has been a natural evolution by organizations to shift a greater share of customer service to self-service and human-less support. This approach reduces customer support costs and is predicated on a belief that customers will happily trade long waits on hold to speak with an agent for digital channels that can provide real-time responses.

While this approach might be effective in certain scenarios, its applicability as a generic strategy is being tested today by persistent dissatisfaction with the quality of customer service; so much so that customers are expressing a preference for human agent support. Case in point is Accenture's 11th Annual Global Consumer Pulse Research survey of nearly 25,000 consumers worldwide, which found that more than 80 percent of pay TV customers would rather solve a problem with a person than interact over digital channels.

The problem is not that customers have a broader selection of channels to reach the organization; it is that contact centers have historically been run as efficiency machines built to maximize the throughput and volume of calls, email, chats, and Web traffic rather than focusing on resolving customers' problems and delivering superior omnichannel experiences. More than that, it's about data. Organizations are investing in cloud technologies, next-generation tools, and new processes for customer service, but without effective data analytics, it is impossible to measure the impact of these investments on agent performance, the customer experience, and contact center operations.

Here are four ways analytics can transform your contact center:

Gain a unified view of customer interactions

Today's omnichannel contact center provides supervisors with full visibility and control of every incoming and outbound interaction from a central point, regardless of organization, technology, or location. A true unified view of customer interactions, however, occurs across two dimensions. The first encompasses all customer service channels, including voice calls to the contact center, IM, video, and Web. The second dimension is across time, where data is mapped across the customer's journey. For example, a customer hits a Web site, then sends an email, then makes a phone call to get a resolution to an inquiry, and contact center managers must have a view that extends across each of these customer touch points.

The unified view of data not only benefits managers, but agents as well, as customer journey data can be displayed to agents during each interaction to provide the full context of customer actions prior to speaking with the agent.

Unifying data requires that organizations recognize the risk that there could be workflows from which your contact center is not collecting data. It is critical to integrate the entire customer contact process, augment this data with third-party data like demographics when applicable, and then collate this information to easily understand who your customers are and what they need. Data source integration has been historically complex and cumbersome, but new technologies and domain-specific solutions are streamlining these process. Comprehensively linking customer service data is necessary to ensure a unified view of customer interactions.

Anticipate and understand customer needs

Customer behavior is often unpredictable and irrational, which puts companies on the defensive when it comes to anticipating and understanding current and future customer needs. Data analytics— predictive analytics in particular—levels the playing field by taking key aspects of customer information and cycling it through an automatic process of data gathering and workflow planning across key areas such as customer lifetime value, customer satisfaction, and what types of needs they have.

Anticipating customer needs boils down to predicting future performance and the customer's propensity to behave. What is each customer's propensity to churn, abandon a call, or to be upsold or cross-sold? Analytics predicting this behavior allows organizations to eliminate unpredictability by anticipating customer needs and then meeting or exceeding them.

Use analytics-based call routing to maximize performance

Contact center interaction management solutions have historically taken a retroactive approach: examining past interactions to form judgments on the value of customers today. In many contact centers, callers are still routed to the agent who has been available the longest. Advanced contact centers today are going a step further, using intelligent routing to send callers to the agent best able to handle the call. As opposed to availability-based routing, analytics enable organizations to define rules focused on optimizing business outcomes or let predictive analytics route calls in a more dynamic fashion through:

  • Performance routing: Match customers in real time with the agents that will drive the best business outcomes based on past performance.
  • Value-based routing: Route customers with a high propensity to buy or higher lifetime value with priority to higher-skilled agents.
  • Status routing: Route based on customer current status, such as routing delinquent callers to collections or new customers to help services.
  • Demographic routing: Match customers with agents with whom they will most likely have an affinity.
  • Service-level routing: Route to minimize abandons, wait times, and other service level commitments.

Analytics are not only critical to routing calls and messages to the right customer support agent, but also providing that agent with analytics-driven information to improve the close rate. Before the customer speaks a single word, this data can peel back the curtain on how customers might act, so that when they call in again, the system recognizes this and can prioritize their calls and route them appropriately.

Augment agent management with performance metrics

Contact center managers can only optimize what they can measure, and if data is being used to measure the wrong metrics then a huge opportunity is being missed. With the right analytics, managers can extend beyond simply measuring agents against a single, universal target, evolving to performance metrics that match employee goals to their unique strengths.

This approach involves, for example, matching phone agents with the best suited customers, or placing agents at the phone at the best time to reach your highest-paying customers. By doing so, employees have the freedom to develop unique strengths within the team, whether it's a person who excels at letting customers talk about problems, or high-powered sales managers who can upsell every customer they receive. Using analytics to understand the workforce rather than simply mapping all employees to a goal they might not be suited to reach can have a positive impact on employee performance, employee retention, revenue, and customer satisfaction.

Regardless of the complexity of contact center operations, volume of interaction channels, or combinations of in-house and outsourced agents, today's contact center must not only incorporate data analytics but also ensure they have the right tools to analyze the right data across all channels to impact performance.


Arnab Mishra is vice president of business operations, BroadCloud Contact Center Solutions, BroadSoft.