Industries that are highly dependent on customer service like Utilities, Telecom, and Insurance are investing significantly in customer experience (CX) transformation. They are adopting digital best practices to be competitive and explore cost-effective ways to meet the demands of their customers. For example, a Gartner study shows that over 50% of Utility companies are reallocating their investments on CX innovations. Utilities are still behind the curve when it comes to adoption of CX transformation, as compared to other industries like Retail. However, with increased de-regulation, competition, and regulatory mandates; customers are seeking innovation-led customer-service models.

The key ingredients of a successful customer-service model are enhanced end-user experience with personalized elements that can deliver a superior customer experience. Let’s elaborate further:

Need for Experience Engineering - While “Anytime Anywhere” (ATAW) solutions enhance end-user experience, deploying well-trained employees (customer-service agents) and providing “First Contact Resolution” (FCR) consistently have improved customer service. However, organizations still struggle to maintain consistency and contextualize service experience due to high attrition of Customer Service Agents (CSAs) and a lack of personalization, respectively. This in turn leads to increased training cost and overall cost to serve. Any shortcoming in training results in inconsistent customer experience delivered to the customer. Customers are seeking consistent experiences across all channels of their interaction, triggering a need system based foundation to drive improved customer experience.

Personalize, Predict, and Act - Customers expect their service providers to personalize the interactions and propositions offered to them. Over 81% of consumers want their Utilities to know them better and interact accordingly. However, over 60% of companies grapple with providing personalized experiences in real time. Without compromising on customer privacy, leveraging customer data such as past interactions, account history, social media interactions, usage patterns, payment patterns etc. to derive valuable customer insights enable improved customer centricity.

Previse Customer Needs - The insights derived from customer data to predict customer needs, and providing required contextual data at the point of interaction, help CSA to provide personalized experiences with reduced effort. To enable this, SAP Intelligent Technologies like Machine Learning and Artificial Intelligence enable organizations to develop various algorithms and package it as a model. Moreover, automation bots can be developed and integrated to complete system updates for mutually agreed actions between customer-service agents and their customers.

Integrating such capabilities across customer-interaction channels enables a unified customer-service experience, employee experience, better consistency, and in turn leads to customer loyalty.

Wipro’s Cognitive Customer Service

Wipro’s Smart Customer Service Solution is a configurable suite of integrated applications to predict the customer’s needs (like the purpose of customer interaction), provide Next Best Actions (NBA), and automate resolutions that are mutually agreed with the customer. The solution delivers a configurable framework to incorporate each service organization’s Customer experience. The Solution is implemented on SAP Cloud Platform, leveraging SAP Intelligent Technologies like Machine Learning, Business Rules Engine, Intelligent Robotic Process Automation (iRPA) and integrated with SAP S/4HANA Customer Management and IS-Utilities. Various KPIs can be measured leveraging analytics reports, such as measurement of success of Prediction and Automation Usage Analytics. It is already anchored in the Utilities industry; however, the framework-based architecture helps in faster adoption and deployment of the solution for any industry.

Plug-in Past Data to Predict

The Solution can be seamlessly integrated and embedded within the current Customer Interaction Centre functionality, thus complementing --and not competing/replacing-- the existing process. Addressing the dynamic nature of customer interactions, the model comes with continuous learning from each customer interaction, thereby making the model more effective, contextual, and reliable. The customer-service operating model is set to move from an employee-based model and adopt the Uberization model. Such transformations are technology led/driven and solutions like Cognitive Customer Service will expedite setting up such an ecosystem. Ultimately, it leads to increased CSAT, reduced Cost-to-serve, and most importantly, Customer Retention and Employee work satisfaction.