By Ilan Kor, Senior Product Manager, Interaction Analytics, Nice Systems Ltd.

Introduction
There are many tools available today that use analytics to help organizations gather intelligence from customer transactions. Most of these tools, however, do not have the capability to extract meaningful and strategic insights from customer interactions and combine them with insights gathered from transactional systems. This capability is the key to improving customer analytics and reporting. This is interaction analytics.

Interaction analytics is driven by qualifying principles.  In the past, contact center systems have focused solely on quantifiable customer metrics, for example: average handling time, service levels (what percentage of calls were answered within a certain time frame), and so on.  Interaction analytics merge this data with qualifying metrics, for example: why did a customer become irate, and how did the agent handle objections? How and why is an agent successful at identifying and closing sales opportunities? How well did he or she fare on their customer feedback surveys?

The kind of input that can be derived from answering such questions provides a strategic benefit throughout the enterprise. The contact center management can better understand the performance of its agents, supervisors, and of the contact center overall; the organization’s marketing department can gain critical competitive insights and input regarding customer “wish lists”; the business development department can better identify and leverage new business opportunities; and so on.

Interaction Analytics
The goal of improved interaction analytics is to gain a deep understanding of customer behavior, and to address a broad spectrum of key strategic issues both in the contact center and in the enterprise. These issues could include improving agent performance, streamlining coaching packages, increasing customer satisfaction, decreasing customer defection, upsell/crosssell, ensuring compliance, preventing identity fraud, and many more.

By using speech analytics on one unified platform, contact center supervisors and decision makers can cross-reference data to gain insights into what is truly going on with their customers, their agents, and in the contact center.

Ideally, the interactions platform, which captures and analyzes data from customer interactions (via voice channels, traditional and IP telephony; CTI; and agent computer screen activity), should apply multidimensional analytics, which include a variety of methodologies and technologies.

The Importance of Multi-dimensional Interaction Analytics
The multi-dimensional approach to interaction analytics entails compiling results from monitoring key words and phrases, detecting the customer’s emotional level, gathering input from agent’s screen activity and application events, as well as gathering inputs from various business systems.

This kind of approach can quickly alert managers to customers at risk. For example, multi-dimensional analytics can combine input from the CRM system – when, say, the customers are identified as buying significantly less each month, with key phrases occurring during calls to the contact center – such as “not satisfied” or “didn’t work”, along with poor customer feedback results for the handling agent. All of these can alert management to a customer about to defect, so that the situation can be handled and corrected before the customer actually decides to go with the competition.

When combining this with emotion detection, the benefits of interaction analytics are all the more powerful. Emotion detection can be critical in identifying true customer satisfaction or dissatisfaction, allowing flagging of problem calls, rapid review and immediate call back. This means unprecedented responsiveness and increased customer loyalty.

The traditional approach to spotting key words and phrases also needs to be revised to improve customer analytics. Ad-hoc search capabilities can transform saved calls into a searchable database by creating an index file for every recorded customer interaction. Once calls are indexed, ad-hoc queries can be performed to retrieve calls that contain key words or phrases that were spoken at any point in time, without having to pre-define the words in advance.

Reporting
Improved reporting means centralizing the results of multi-dimensional interaction analytics into unified dashboards so that managers can review all the relevant performance parameters of the agent, the team, the supervisor, and the contact center. Unified dashboards support – Key Performance Indicator (KPI)-based management – is an extremely effective management tool, and agents can receive the feedback that is required for increasing motivation, and understanding what underlies success or deficiencies in their performance.

Conclusion
Consolidating customer insights generated from a range of contact center data systems, and applying a broad variety of interactions analytics, enables contact center and enterprise decision makers to improve customer analytics and reporting. They can now address key business issues including protecting revenues, ensuring compliance, nurturing customer loyalty, increasing agent productivity and negotiating skills, improving upsell/ cross-sell, compiling critical business intelligence and much more. 

Ultimately they can improve operational efficiency and strategic effectiveness, and understand what is actually going on during customer interactions, and most importantly – why.