Walk into a contact center today and it may look deceptively similar to a decade ago. In many centers, the phone continues to be at the core of all customer interactions, with most of the agent’s time spent on handling calls. The ACD reporting system is based on call metrics around handle times, service levels and first call resolution rates, but does not provide insights into underlying causes.
So far contact center analytical tools have primarily focused on Agent performance Analytics.
But a silent transformation is also unfolding. Big Data and advanced analytics have opened up opportunities to dig deep into structured and unstructured data and provide deeper insights to enable enterprises to serve customers better.
Predictive Analytics: The modern predictive analysis engine uses past performance data pertaining to call volume, service level, handle time, and customer satisfaction, to predict future volumes in specific situations like festivals, new product layouts, price changes thereby enabling organisations to plan ahead and staff optimally to drive superior customer experience.
Call Center Desktop Analytics Some companies have deployed desktop application programs that do real time call monitoring to capture inefficiencies, and identify potential coaching opportunities for phone agents. By not only viewing the phone agent’s activity during the call, but also capturing all activity on the agent’s desktop, companies can propel operational efficiencies through lower handling times and better utilization rates.
Speech Analytics: Speech analytics combined with predictive analytics, can analyze the caller’s tone, vocabulary, sentiment, and even silences to gauge emotion and satisfaction, using keyword identifications, data mining, call recording and highly advanced sentiment analysis. It can also detect an angry caller or even predict when a caller is lying or trying to commit fraud. Armed with this information, calls can be routed to a more experienced agent or to one possessing the requisite skills to address the situation.
With the advent of social media, companies are able to get clues about service disruptions in advance from complaints in Twitter, Facebook and so on, pointing to potential spikes in call volumes. Companies can then quickly provide alerts and even change recorded messages to include information about the outage.
Source: Dimension Data Benchmarking report
The Contact Center of the Future will move away from the phone as the core of the center, transitioning to a more omnichannel platform that includes phone calls, but is also comprised of chat, email, instant message, social media, video chat and other forms of communication that continue to emerge. Challenges exist with converting data across numerous, disjointed systems due to complex architectures.
In fact AI is already beginning to dramatically change the way contact centers operate. It’s making contact centers more efficient with bots that can quickly answer the questions most commonly asked by customers. AI is even helping to predict customer behavior, providing advice to customer service reps on how best to solve a particular issue depending on need and complexity.
A benchmarking report by Dimension Data says that 71.5% respondents believe analytics enables better agent performance and 69.1% say it drives better CX but just 36.4% can track a customer journey that spans multiple channels and only 17.4% can locate problem hot spots that impact CX.
Ominchannel analytics: Customers rarely use just one channel to complete an interaction, and many end up in the contact center. Yet few companies integrate that information to create a superior customer experience. Even those with “multichannel” operations rarely connect customer data and trace the customer journey across channels. Customer experience breakdowns are commonplace as customers move from one channel to the next, resulting in poor customer experience and churn. Omnichannel approach and analytics can integrate customer data across Voice, mobile, web, social, chat, email etc and combined with transactional, sentiment, demographic, and other data to arm contact center agents with appropriate information. Text analytics can review and monitor messages sent to customers, and vice versa. This is critical to understanding any potential issues through the customer lens.
Self Service Analytics: Some companies are thinking further ahead and are finding ways to encourage customers to use self service channels. Instead of having a customer call a contact center to update their address, why not have them do it online on your website? This reduces opportunity for error, incoming call volume, and cost. By some estimates, more than 70% of buyers engage in online research before buying a product. Customers in turn have turned to online communities because they offer more real-time means to engage with a company, as opposed to more traditional communication channels such as the phone or email. Gartner has predicted that companies could gain between 10% to 50% cost reduction by integrating customer communities into their support offerings. Satisfactions surveys can be used post a self service transaction and analytics around this metric can be used to determine efficacy of self service tools.
Clearly, while Agent performance analytics continues to be relevant and dominant, the ever growing reach of Analytics is closing the chasm between the telephone primed customer experience of the present and the digital primed customer experience of the future.