More data means more opportunity for value creation. It’s really that simple. In any industry where data is created in massive volumes, there is always the possibility of unearthing new insights, and within the contact center environment the value that can be derived from such data is enormous.
One good trend is that almost every contact center today has basic data and analytics tools in place. However, a McKinsey study reports that only 37% of percent of organizations feel that they are using advanced analytics to create value (Source) and that needs to change.
Here are some advanced analytics approaches that contact centers can implement to attain actionable insights.
Analytics is a broad practice. To get the right insight that you want to act upon you must drill down to the right approach that will give the right insight. For contact centers, the following approaches can help.
Although a new entrant in the analytics space, speech analytics banks on Artificial Intelligence and Machine Learning to determine the sentiment of the caller. Alternatively, supervisors can also call monitoring tools to oversee how agents are conversing with calls. This analysis can be done on a real-time basis or with historical call recordings.
All the text that a contact center receives and reverts through social media, text messaging, email, etc. can be subject to text analytics. It can help in identifying potential issues in the messaging tone, the promptness of reply and several other aspects of customer service.
Predictive analytics in contact center can help answer several questions. Some of which include, ‘How many agents would the contact center need on Black Friday?’, ‘What part of the day has maximum call density?’, etc. With predictive analytics, contact center supervisors can analyze historical data to plan for the future.
If a customer can find the solution to their query on their own without having to be routed to an agent, it is a win-win situation. The key to maximizing self-service adoption lies in figuring what kind of queries customers want self-service options for, the technological infrastructure required for it, and the ease-of-use.
The primary purpose of applying analytics is to optimize KPIs. If done right, analytics can produce significant results, some of which are described below:
Why do some calls take forever to resolve, while some other calls are resolved quicker? Calls could be quickly resolved if the customer gets the information or the resolution that they are looking for immediately. To provide that information or resolution, the contact center agent must have the information readily available.
Analytics can help a contact center to look at the caller journey from beginning to end and identify potential areas of improvement. For example, low-value queries like account balance check can be solved with an IVR menu instead of routing it to an agent.
FCR (First Contact Resolution) plays a major role in helping a contact center achieve its service level target. The industry average of 80/20 is achievable is FCR can be maximized. And, analytics can help with that. A combination of analytical approaches that we discussed earlier, like speech and text, can help point out what information customers are seeking for. Agents can be armed with the required data for such calls thus maximizing the FCR.
For any contact center supervisor, reducing call volume would rank somewhere in the top five priorities. To reduce call volume without compromising on customer service, an efficient self-service system should in place.
In contact center domain, it comes in the form of IVR (Interactive Voice Response) system. When IVR containment increases, the call volume will naturally drop leading to higher contact center productivity. This can be achieved by analyzing the most frequently asked queries by customers and integrating them into the IVR menu.
CSAT (Customer Satisfaction) score is the ultimate yardstick of a contact center’s success. Predictive analytics is a surefire approach to maximize the CSAT score of a contact center. It can help narrow down on factors that heighten customer satisfaction and factors that drive them away. By identifying the key drivers of customer satisfaction, a contact center can get to work improving them.
Contact centers have to tackle customer queries and complaints fired from multiple channels. While there are omni-channel customer tools available to simplify everyday operations, there is a surefire way to fix contact center efficiency for the long term.
It is with data. By taking the right analytics approach, contact centers can unearth ample data about the most recurring customer queries, opportunities for automating responses simplifying call flows and much more. When such a data-driven approach is implemented properly it can yield benefits like reduced average handling time, maximized first contact resolution, maximized use of self-service options and also an overall improvement in customer satisfaction score.
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