machine learning

Using Call Center Analytics to Improve Customer Satisfaction and Revenue

Call centers act as another important touchpoint where consumers interact with the company. Traditionally the customers come in contact are served over the phone after they are directed through IVR to the right executive. Now, customers are also being served through other platforms and ways such as SMS, in-app chat, or Facebook messenger. This complete operation produces crucial data about customers. Leaders can analyze this data and bring out insights to improve Customer Satisfaction and enhance employee performance and efficiency and overall revenue. This enables the company to understand the customer and make futuristic strategies to improve company performance. Using Debt Collection Analyticscall centers can also improve Debt Collection, predict collection and enhance complete portfolio performance. 

Root Cause Analytics 

Many times, a customer makes a call for small issues and reasons, usually call centers to try to deflect such calls to a low-cost channel rather than understanding the root cause for such calls. Using Root cause analysis, leaders can identify the issues and strategize to reduce such kind of calls and improve customer experience. It can also lead to the identification of issues lying outside the center and can be rectified by the company such as choosing the right marketing channel, improving the self-service, raising alerts, and enhancing bill layout. 

Intelligent Self-service 

With frequent and small issues coming each day, both customers and companies need self-service tools such as FAQs, help assistants, and more. But most of these tools end up being useless as either they do not answer a specific question or give too many possible answers, both of which don’t solve the consumer’s issue and leave them unsatisfied. The modern-day call center analytics tools can use machine learning and text and speech analytics to generate a specific answer to these frequent yet important queries. These systems learn and improve with time and give more and more accurate solutions. Companies can use these self-learning tools to reduce the number of calls and improve productivity. 

Empower call center representatives 

The call-center associates work in a high-stress environment with low salaries and usually thought of as cost by companies. However, these are the people representing the companies at a crucial touchpoint and hence much be thought of as an integral part of the organization, who can become customer’s trusted partners and help earn their loyalty. Positive interactions and success during these conversations by these associates can cement the customer’s relationship with the organization. 

These critical situations cannot just be solved with technology, it also needs emotional intelligence. Call center employees can use technology, data, and this intelligence to help the customer and create a bond. Use data and identify such employees and groom others to improve performance and satisfaction. 

Customer segmentation and right channel allocation 

With more and more calls coming to the call centers, the interactions have to be segmented and distributed to the right channels to improve productivity and efficiency. Call centers can use analytics to make decisions regarding routing the calls by analyzing important metrics such as customer profile, customer preference, customer behavior, the complexity of the interaction, and more. The queries with high complexity should be given to the representatives and others can be distributed to low-cost services such as chat or self-service. 

Improve customer interactions with predictive analytics 

Most of the call centers experience calls initiated by the customers due to some issues. Call centers can use predictive analytics to identify the potential issues before they arise and rectify them before the customer tries to reach support. This proactive approach can lead to a significant reduction in call volumes, improved efficiency, and can exceed customer expectations, strengthening the bond between the company and consumer. 

Call center analytics can identify the correlation between multiple problems and help management make data-driven decisions. For example, if a customer facing Problem A who also tends to have Problem B, as suggested by analytics can be satisfied with just one call. When the customer makes the first call to rectify Problem A, the representatives can proactively ask and suggest solutions regarding Problem Y in the same call. This reduces repeat calls, customer satisfaction and improve self-service tools. 

Follow customer journey across channels and making better predictions 

With so many channels serving multiple customers, the data can become siloed and create an inconsistent and unsatisfactory customer experience. Companies can use a scalable analytics solution to convert this multichannel experience to an omnichannel one. Leaders can use cloud-based analytics solutions to analyze real-time data from all the channels and can create live customer profiles and understand their journey better and offer better solutions. 

Call centers can use predictive analytics to predict various KPIs and help in finding answers to questions like what marketing channels will be better and more. It will take into account the historic and real-time data to make the right forecasts and help in making futuristic strategies. 

With more and more call centers adopting the analytics tools, the industry is set for a paradigm shift. This digital transformation will enable better customer experience, increased productivity, and enhanced revenue. 

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