Interaction Analytics: Five Reasons to Implement It Now
Call centers provide a trove of useful information on customers, but too few companies make use of this data. The maturation of artificial intelligence (AI) and natural language processing (NLP) has unlocked new possibilities, however — and our clients have the results to prove it.
Every day, enterprise call centers have hundreds of customer service agents handling inquiries from thousands of customers. These interactions offer a valuable opportunity to gather insightful customer information that can enhance products and drive improved services. Customers share information not just about their product interests but also about life events, service issues and even their emotional mindset. For example, a bank customer asking a service representative about a fee on her statement might casually mention that her child is preparing to go to college — an indication that the family may soon need a home equity loan, a student loan or a student checking account.
Such insight can help companies identify opportunities early in a customer’s decision cycle, making it possible to cross-sell additional products and avoid negative experiences that might cost them a customer. Today, with consumers enjoying 24x7 ability to research and transact, opportunities to meet customer needs come and go in a flash.
Yet many organizations lack the capability to capture and analyze call-center interactions. We believe that it’s time for forward-looking businesses to address this gap.
Interaction analytics: Ready for the enterprise
In the past few years, advances in natural language processing (NLP) have enabled enterprise-grade solutions that offer interaction analytics. These solutions can elevate contact centers to a new level in generating customer insights. Moreover, they improve customer experience, contact center operations and quality.
Interaction analytics platforms ingest call audio recordings, then use NLP and linguistic technologies to translate audio to text and identify topics or concepts. Interaction analytics can also capture text-based communications from email, chat and social media. The technology helps organizations automatically analyze all interactions, instead of manually listening to and analyzing only 1% to 2% — typical for many call centers.
These platforms leverage artificial intelligence (AI) to generate highly accurate and relevant insight across many languages and dialects, including scanning all interactions to derive meaning from “non-talk” and “cross-talk” with unprecedented accuracy. For example, the AI capabilities in a speech analytics platform can identify topics that most often precede periods of extended silence, uncovering potential issues related to agent training or the knowledge base. Rectifying these issues can reduce handle time and result in cost efficiencies and improved customer experience.
Interaction platforms also can detect customer sentiment, and how that sentiment changes throughout a call, by evaluating word choice, rate of speech, tone and context. By associating sentiment measures with topics and agents, companies can develop and track improvement strategies.
Successfully implementing interaction analytics
We’ve worked with large organizations to implement interaction analytics, and the results are impressive. For example, one leading fast food chain wanted to transform its call center by focusing on four key challenges:
Improve the customer experience.
Reduce repeat calls.
Understand key drivers of calls.
Reduce call handling time.
We helped the company deploy an interaction analytics solution that analyzes 100% of all contact center interactions, including voice, email and text. As a result, customer satisfaction improved from 92% to 99%.
To achieve this, we leveraged interaction analytics to identify call drivers (reasons for the call) that had high negative sentiment and identify approaches to address the underlying need for the call. In this case, calls to resolve malfunctioning point of sale (POS) devices were resulting in high negative sentiment. By identifying a need for procedures to prevent POS malfunctions in store locations, we helped alleviate the need for the calls and reduced negative sentiment for this driver by 50%.
Non-talk time (long pauses on a call during which an agent may be researching a resolution) was reduced by 10%, which also shortened call handling time.
The persuasive benefits of adoption
In our experience helping companies adopt interaction analytics platforms, we’ve seen them benefit in the following ways:
Reduced handle time.
Businesses can identify topics that drive the longest call times. They can then develop strategies to improve training or increase access to online information to reduce handle times — and thus costs. Some call topics, such as address changes or statement inquires, are usually handled quickly. Other topics may not be adequately addressed in the knowledge base, so less experienced agents spend more time researching and consulting with colleagues to understand proper resolutions. By using an interaction analytics platform to systematically link call topics with handle time, gaps in knowledge resources or training can be identified and corrected.
Our clients may deflect calls by driving them through such self-service mechanisms as interactive voice recognition (IVR), which also reduces costs. In addition, companies can use IVR to post messages on topics that they expect to drive high call volume. With one client, we observed a large volume of calls for resetting an account password despite having a self-service channel. We identified that 8% of associates were not educating customers about the self-service option. Based on this insight, a targeted agent coaching plan led to a 39% reduction in password-related calls.
Reduced repeat calls.
Adopters of interaction analytics reduce repeat calls by identifying the topics or agents associated with greater repeat call volume. By identifying and analyzing these topics and agents, they can develop strategies to resolve issues on the first call.
Improved customer experience.
Businesses improve customer experience and satisfaction by getting more information to callers faster and reducing call time. Companies provide a better experience by handling calls faster and resolving inquiries the first time. With interaction analytics, they can go a step further: by tracking customer sentiment by topic and agent, they can diagnose root causes of negative sentiment and implement corrective approaches.
Companies also improve compliance by using interaction analytics to monitor when required disclosures are read and flagging instances in which they were not. This is particularly critical in industries like financial services that have strict compliance mandates.
With such compelling benefits, we encourage contact center leaders to explore how interaction analytics can contribute to their business strategy and technology roadmap. Because call center activities engage various functions and roles, from customer service associates to quality managers to business unit stakeholders, it’s important to establish a change management agenda to ensure that all stakeholders are on board and understand the benefits and opportunities.