The customer’s demands and expectations of contact centers are changing, but not as quickly as a company’s ability to efficiently respond to the impact of these changes within its operations.

Contact centers typically provide service to customers during both the sales and service phases of the relationship. These operations are tasked with helping customers resolve questions or issues with their products, services, billing and other activities. This model is often a reactive one and is designed to minimize service costs while maintaining a certain level of customer satisfaction; however, these often can be opposing strategies.

Service is emerging as a more consistent differentiator. Organizations that typically competed on product features and functions are now edging ahead of competitors with premium offerings and other value-added services. The difference between a good contact center operation and a great one typically lies in a company’s ability to efficiently monitor and respond to key activities within its operations, predict future demand, and optimize service elements to achieve the best outcomes. Small improvements will have material impact on costs, as well as revenue.

In the context of that overarching environment, analytics technology can drive value by optimizing a variety of business process, from better staffing management to more efficient contact distribution to more effective upselling. And it can provide insight into questions that other technologies can’t.

TODAY’S CHALLENGES

In today’s highly competitive business environment, where a company’s reputation can be dramatically affected by a few keystrokes anywhere in the world, recognizing the value of providing superior customer service is critical to a company’s profitability and long-term viability. Adding to these challenges, contact center operations rely on a multitude of transactional systems, each generating large volumes of disparate data, usually stored independently.

We’re collecting more and more information everyday about our customers, both personally and in aggregate. Analytics offers the ability to perform behavioural analysis of customers based on that data. Consider a mobile phone carrier. There’s a wealth of data to draw from: billing information, applications, patterns of use, etc. When do our customers primarily use SMS? When do they use social media? When do they use their phones as phones? Analytics provide insights in near-real-time that can help solve the problems of proper staffing, queuing and contact distribution by helping understand why a customer is making contact.

At the same time customers are demanding new channels for support, including social media, short messaging service, and e-mail. The contact center operations rate of investment in information technology (IT) to drive operational efficiencies and effectiveness are much lower than demand growth rates, so managing the volume of customer contact is a challenge.

This is where an integrated Contact Center Analytics can offer high value to contact center operations. To ensure the customer experience is positive, leading organizations are moving from a reactive contact center operation to one that is proactive, transitioning from a simple receive-and-respond mindset to one designed to monitor, alert, predict and optimize customer interactions. Top-performing contact centers typically have more satisfied customers, lower operating costs and higher revenue per customer.

Strategically, analytics can offer value in three areas: Demand planning and Resource Optimization, Performance Assessment, and Enriching the Customer Experience.

DEMAND PLANNING AND RESOURCE OPTIMIZATION

The most pressing challenge for contact center management today—and the one with the highest potential savings—is staffing and resource management. Most staffing management technology is spreadsheet based—a valuable technology, but one that isn’t predictive. Thus, we often end up with contact center staff that is idle, expensive (for obvious reasons), or swamped, with delays to resolution that frustrate customers. Complex queuing and contact distribution processes can also add to the time to resolution, and in a contact center environment, time literally is money.

Analytics offers a more granular approach to the volume of data collected, and in near-real-time. Transaction-based, spreadsheet-driven tools can’t manage the same levels of granularity—the specificity of queries. More granular analysis means better demand prediction, which in turn leads to more efficient staffing decisions.

Accurately predicting service volume provides contact center executives and managers with the critical information needed to effectively support strategic and tactical planning activities. One of the most elusive targets in any contact center operation is the ability to accurately predict demand. With the erratic nature of consumer demand and the ever-growing list of internal and external influencers, it is a daily struggle for contact center operations to accurately identify the service in order to schedule staffing.

Another elusive target for contact centers is the ability to identify the right amount of staffing needed to meet predicted demand. A combination of limitations in forecasting accuracy, disparate data sources, antiquated methodologies and cumbersome manual processes result in inefficient, inaccurate staffing decisions being made. Poor staffing decisions have a direct impact on operational costs (whether by overscheduling or under scheduling) and an indirect impact on customer satisfaction. With staff costs being the largest operations expense in any organization, it is critical to optimize staff supply to meet service volume demand.

Ultimately, contact center leaders want to be able to see where they are headed and then contemplate various scenarios of what could happen in order to determine the best course of action to take. They will have to make decisions that will impact the business, which requires fact-based information, not hunches or guesswork. Contact center operations need a powerful forecasting engine combined with robust optimization technology that moves far beyond current Erlang-C driven models. This enables organizations to harness impacts of changes in customer demand to ensure the right mix of skilled resources that are needed at the lowest cost all while exceeding service level expectations.

These technologies enable contact center operations to significantly improve strategic and tactical planning, dramatically reduce or eliminate manual data gathering processes, lower operational costs, and improve operational efficiency.

Also, using analytics to measure the impact of variances enables contact center management to estimate the impact on operations instantaneously, increasing the speed- to- value with significantly less effort. Analysts and planners can finally shift from spending 80 percent of their time on data gathering and manipulation to spending 80 percent of their time on adding value to the business.

Performance Assessment

Performance assessment has two major areas of focus: root cause analysis and impact evaluation.

ROOT CAUSE ANALYSIS

Why do support contacts fluctuate? What events lead to spikes in contact volume? Analytics platforms—especially visual analytics platforms with an interface aimed at the business user rather than the IT department—allow management to explore the relationships among these variables and, again, improve the ability to predict demand

Improved staff planning from the insights of root cause analysis reduces overtime and over-staffing—as well as understaffing, which leads to longer handling times. Consider that a poorly planned staffing environment can have a variance of 200 per cent full-time equivalents (FTEs) at an average of $50,000 in wages, benefits and overhead. The impact is material.

First Contact Resolution rate has a huge impact on the bottom line. Considering the cost of each customer contact (for example, in many contact centers, this ranges between $5 and $6 per contact), contact centers strive to achieve a 70% to 75% FCR rate. Analytics can provide rich data insight that helps to automate the routing of a customer contact to the resource most likely to provide that resolution. By better aligning resources to customer needs, significant improvements in FCR rate can be achieved. It can also provide an analysis of where training for specific customer needs is required. Analytics can also help strip the complexity out of queuing system design by predicting the likely paths of IVR contacts.

For an enterprise contact center, an average reduction of one second in handle time over the volume it receives in a year could save tens of millions of dollars. Having a backend analytical platform that has a context to map contact routing more efficiently can reduce time on virtually every contact, saving millions of dollars.

IMPACT EVALUATION
Analytics can be applied to isolate the impact of changes to the contact handling process, discovering what works and what doesn’t for the customer. Impact evaluation can improve contact center performance in a number of ways:

Improving self-serve capabilities

Assessing the drivers of net promoter scoring, customer churn and upselling opportunities.

Improving next best action recommendation by customer segment.

Isolating efficiencies, incremental revenue opportunities, and cost saving opportunities.

ENRICHING CUSTOMER EXPERIENCE

The contact center is a component within a customer experience ecosystem, and analytics can help solidify an organization’s ability to enrich the customer experience. The vast volumes of customer, transaction, and unstructured data can help an enterprise better understand demand, and leverage the contact center to execute more effectively.

Analytics in any customer contact situation, whether it’s point of sale, online shopping or in a contact center environment, is about delivering the right offer to the right customer at the right time. In a contact center, that’s also about directing the contact to the right resource who has the right information. Analytics can match the client to the right resource. And, since, as we noted above, the data can predict the need for certain resources, we can plan training to make sure we have the right mix of expertise.

This enriched customer service environment can encourage clients to cross-sell or upsell themselves. By delivering a next best offer to the contact center agent, the agent has a better opportunity to drive incremental revenue and improve customer retention. Next best offer takes into account multiple factors and predicts the basis for interaction, based on customer interests, behaviour, previous interactions, and product holdings.

FINAL THOUGHTS
Analytics’ role in staff planning, performance assessment, and enriching the customer experience staffing and execution, compound: one plus one plus one equals five, not three.

• Reduction in cost of service
  
• Increased revenue
  
• Service level improvements
  
• Planning processes improvement
  
• Staff performance Improvement

Most importantly, analytics can help enrich the customer experience and increase long-term customer value by driving efficiencies and enhancing strategy to have an impact on the bottom line.

Originally published in Contact Management