Enriching Customer Experience (CX) with Data Science-backed Customer Solution

Posted on : August 16th 2022

Contact centers need advanced technologies and methodologies such as data science to get actionable insights into their everyday operations to efficiently deliver excellent customer service. Data science has opened up a vast realm of opportunities for contact centers. For example, by transforming the forecasting and contact delivery process, data science can bring about a paradigm shift in how contact centers deliver customer support today. Simply put, a data science-backed Customer Experience (CX) solution will help contact centers enhance customer service.

Challenges in Delivering Customer Service Efficiently & Effectively

The contact center industry is at a crossroads. Advances in technology and evolving customer demand require businesses to enhance the way they effectively and economically deliver customer service without sacrificing quality. The need for skilled workforces to address complex issues, especially when customer demand surges, means workforce management is integral to the contact center industry. However, it is challenging to create a reliable staffing model without an effectively predicted contact volume arrival pattern.

Furthermore, contact centers invest so much time and money looking at historical call arrival patterns, understanding handle time, and factoring in shrinkage assumptions to meet Service Level Agreements (SLAs). To meet their SLAs, contact centers need to take a proactive and stronger stance by leveraging advanced techniques such as data science. Data science will transform the forecasting and contact delivery process in ways that will dynamically change the customer journey.

Transforming Forecasting and Contact Delivery Process with Data Science

Contact centers can leverage data science to go beyond understanding what is happening within their centers. It will help them generate actionable insights about what will happen next, resulting in reduced costs, increased revenue, and higher customer satisfaction scores.

Data science will help predict when a customer is likely to call and why. For instance, in the case of a credit card customer inbound contact, the reason for the call can be predicted by analyzing the most recent activities and behaviors leading up to the call. Furthermore, 30% to 50% of the call volume can be handled through automation by providing a simple list of options to a customer the moment they call or ping a contact center. This simplifies the operational challenges and reduces call handle time, as now the contact center has to execute only the plan for handling those calls beyond the automated environment's scope.

Improving the Quality of Service by Putting the Customer First

Contact centers are continuously seeking to identify customer pain points, yet most customers interact with them with dread. The best contact centers recognize this as a chance to proactively differentiate themselves from the competition by leveraging advanced analytics. However, there is a fundamental difference between the legacy data and analytics at many contact centers and the advanced analytics techniques and methodologies such as data science that are now available. By implementing these new techniques, contact centers can accurately predict and literally control their own future. Therefore, the time has come for contact centers to shift focus from purely answering calls, chats, and emails to predictive volume handling, thus effectively improving the quality of their service.

If contact centers are still struggling to get all their volume handled, overwhelmed by the cost and resources required to continue to recruit talent, or worried whether their brand would suffer from the inconsistent service, they now have an option. Instead of switching or adding vendors or continuing down the endless path of the staffing carousel, contact centers should leverage data science to help with a flexible and evolving solution that can put their customers at the center by enhancing customer service, meeting SLAs, and reducing costs.