By Kartar Saxena, VP- Strategic Consulting Group, [24]7.ai
In today’s customer service landscape, every interaction counts. Businesses want to scrutinize each interaction for the customer experience (CX) delivered but are often restricted by scalability, leading to a reliance on sample-based approaches. Surveys are also used to obtain direct feedback from customers. However, survey analysis is typically limited to examining low scores to uncover reasons for dissatisfaction. As a result, the customer experience identified from a small portion of interactions is extrapolated to represent the experience for the entire population.
One way to go beyond these limited CX measurement approaches is to gauge customers’ sentiment. Sentiment can act as a proxy for the experience and can be identified in every interaction, broadening the scope of CX optimization activities. However, simply identifying the overall sentiment of an interaction does not fully illuminate how the interaction transpired. It requires a thorough understanding of the customer’s sentiment during the self-serve phase, their interaction with the bot, and then with the agent. This study of ‘sentiment shift’ enables a holistic measurement of the true customer experience rendered.
Advanced AI insights
AI and Machine Learning (ML) technologies have revolutionised sentiment analysis, enabling contact centers to analyze vast datasets from diverse communication channels in real-time. Sophisticated AI models can now identify contextual sentiment, considering previous statements to understand the overall tone and detect nuances like sarcasm. This real-time capability allows contact centers to gain holistic insights into customer emotions expressed across phone calls, emails, chats, and social media platforms. By harnessing these advanced tools, contact centers can promptly identify emerging issues, tailor interactions to individual preferences, and elevate service quality, thereby significantly enhancing the overall customer experience.
Practical applications in contact centers
Coupling the reason for contact, such as intent or issue, with sentiment analysis can add more depth to the insights gathered. For instance, if the initial sentiment is negative for certain intents, it indicates that customers are entering the conversation upset. This highlights upstream issues with that intent—either the process is not customer-friendly, or self-service options are ineffective. Organizations can use this information to fix inefficient processes and agent empowerment gaps, thereby improving the overall customer experience.
Another benefit is that sentiment analysis aids in performance monitoring and coaching by analyzing sentiment trends and agent interactions. This helps identify opportunities for skill development, performance improvement, and adherence to service standards.
Addressing challenges and ethical considerations
Bias in algorithms must be actively countered to prevent unfair outcomes and stereotypes, necessitating diverse datasets and transparent algorithms. Human oversight remains essential for responsible adoption. Ultimately, contact centers must balance technological advancements with ethical considerations, prioritizing accuracy, privacy, fairness, and human intervention.
Embracing the future of customer experience
As the digital age unfolds, contact centers must embrace sentiment analysis as a strategic imperative for driving customer engagement, satisfaction, and loyalty. Looking ahead, advancements in AI and ML technologies will provide contact centers with even more sophisticated tools for analyzing and interpreting customer sentiment. These innovations will unlock new levels of insight, enabling contact centers to preemptively address customer needs, innovate their service delivery, and achieve unparalleled success in delivering exceptional customer experiences. Embracing these cutting-edge tools will be essential for contact centers aiming to stay ahead in a competitive landscape and build lasting customer relationships.
