Big data, the ability to collect and analyze enterprise and customer level data, is a compelling and elusive concept rather than a specific technology.

For decades, call centers have worked with big data, tracking every second of every call and every minute of agent activity through telephony, desktop, and workforce management applications.

The integration of these platforms and databases coupled with emerging speech analytics platforms enables actionable insights and allows us to address common call center challenges and deploy effective solutions:

• Improved workforce recruiting supporting high quality interactions.
• Enhanced employee retention tools and tactics capable of reducing operating costs and improving the customer experience.
• Elimination of customer churn drivers and implementation of processes that enhance customer loyalty and retention.
• Insights into transaction time drivers that unnecessarily extend handle times and damage customer loyalty.
• Improved workload and resource planning enabling improved service levels, improved speed of answer.
• More robust and customer friendly self-service applications.
• Earlier identification and correction of product quality defects reducing the scale and impact on customers.
• Identification of contact volume drivers related to scope of support issues for technologyfirms.

While not a complete list, these are examples of the operational and customer experience improvements that will be enabled by Big Data.

While big data is conceptually appealing at the enterprise level, the allure of actionable analytics is even greater for call center operators.

On a daily basis call centers organize massive data streams into useable information, creating knowledge bases that inform decisions and enable action. The pragmatic appeal of actionable analytics is our ability to use data to transform operations and differentiate service.

Sources of actionable analytics include but are not limited to:

• Social Analytics
• Speech Analytics
• Text Analytics
• Predictive Analytics

Social Analytics….the newest “bright shiny object” in the call center arsenal.

Social analytics was first recognized as an emerging technology by the Gartner Group in 2007. Based in part on its rapid and continuing evolution, the hype surrounding social borders on hysteria with firms eager to mine, exploit, and monetize social platforms.

Call center staffing levels supporting social media have increased over the past six years from a handful, 2 to 3 agents, tripling to 5 to 10 or more agents. These resources staff listening posts, conduct service interventions on a selective basis, and provide valuable organizational insight on evolving social opportunities. More important than the scale of today’s social operations is the value social media may deliver in the future.

Can it be monetized and become a differentiated sales and service channel?  While many take the position that the verdict remains out, there is data indicating that customers who engage on a social level generate higher revenues and are more loyal than those who do not participate in social channels.

Given the velocity of change within the social media landscape, organizations must deploy nimble call center operations with rapid response capabilities.

Speech Analytics…the most compelling operational technology available today.

While deploying speech as a self-service channel has been challenging, the application of speech analytics to recorded customer interactions is powerful. The analysis of recorded calls allows us to correlate agent behaviors and call outcomes to customer preference, generating actionable and compelling insights.

Beyond customer and potential competitive insights, speech analytics plays a significant role in performance management enabling precision call monitoring, supporting root cause analysis of agent performance failures, and providing specific coaching insights. These benefits do not accrue magically; front-line management will need to be reskilled to leverage speech data for agent performance improvement.

Like big data, speech analytics requires analytic skills and business savvy when mining for customer or operational insights.  This is not plug and play technology with standard queries and reporting; it requires a baseline understanding of the customers’ mandatory requirements and service preferences to effectively leverage the tool and structure queries. Organizational biases and prejudices can in fact limit effectiveness. McIntosh supports clients and helps them replace organizational and personal bias with data driven insights accessible through speech analytics.

Text Analytics

In the call center, text analytics enables the analysis of electronic communications including content from customer emails, online or email survey responses, CRM system transaction documentation, chat logs or transcripts, transcribed speech files, and website feedback.  Like speech analytics, text analytics has the ability, when leveraged skillfully, to deliver operational, customer experience, and competitive insights.

Predictive analytics

The ability to analyze, model, and mine current and historical data to predict future behavior or events, is not a new concept.  Credit scoring is a prime example of past efforts to predict future outcomes and minimize risk.  Today’s call centers certainly are closer to anticipating and proactively engaging with customers, but few firms have chosen to leverage the insights that enable proactive service. The barrier is cultural—call center leadership must break away from the day-to-day, reactive tasks and crisis management in order to leverage predictive analytics.  That cannot happen if the primary concern remains today’s service level.  Call center predictive analytics can address a wide range of issues with the examples provided below only a limited sample of the value predictive analytics can deliver:

• Improved workload and workforce forecasts.
• Newly acquired customer contact rates and specialized handling.
• Identification of proactive opportunities based on open issues or pending activity.
• Customer churn prevention by recognizing and intervening to interrupt churn linked activities.
• Improved employee recruiting and retention by identifying pragmatic, actionable insights rather than increasing recruiting complexity.

Consistent for all analytics, the skills to effectively model the volume and variety of data, to mine historical and transactional data, are in limited supply.  Long-term talent must be developed, but in the short-term, acquiring the critical industry savvy, business acumen, and analytic skills to support predictive modeling will remain a challenge.

Cloud computing, the ability to store and process data virtually across a network, has enabled productivity and flexibility in call center IT operations.

Call center migration from premise-based solutions to centralized, virtual operations began early as network providers delivered voice and data through hosted cloud environments.

This migration continued to expand with the emergence of network management tools for cloud management and the migration of specialized applications to hosted environments through application service providers (ASPs). Cloud computing within the call center continues to evolve, with many premise based applications once believed to be limited to site based technologies are no longer required as telephony continues to migrate to the cloud.

Whether managed by the IT organization or hosted by an ASP, cloud based applications enable nimble call center operations and support significant operational trends such as home agent deployments. M&A has the expertise and the experience to provide guidance when selecting or deploying these technologies and in developing the processes that will yield optimal operational results.