Leveraging AI to Predict and Prevent Customer Dissatisfaction

AI for Customer Experience

Many researchers have spent about two years building a robust ecosystem of tools for managing customers' tech support cases.

Data science solutions enable agent workflows in this ecosystem, including a machine learning model for predicting which situations are most probable to occur in disgruntled customers. By enhancing awareness of open investigations that could benefit from extra involvement by an agent, "DSAT Predictor" has assisted in optimizing the customer experience and has become a much more proactive measure, frequently before an urgent problem has emerged.

The "DSAT Predictor" flags open tech support cases that most likely lead to unhappy customer comments on a user experience survey utilizing data about the issue, operator, product and client. Operators can see cases detected by the machine learning model as a signal in a dashboard for case management and tracking. This enables tech support workers or supervisors to intervene in case of a possible problem, enhancing clients' technical support experience.

The advantages of the project go further than early intervention. It has also resulted in process standardization while preserving regional differences in customer demand and expectations. Individual company segments were the catalysts for the initial attempts at DSAT prediction. Over time, six alternative predictive tools emerged, each with its own set of input data, algorithm, assessment measures, infrastructure and deployment environments. Alignment and uniformity across the firm were less than excellent in those early days.

The mirrored integration of case management procedures was accomplished by integrating the DSAT Predictor into a single, reliable model. Management can more readily influence the organization's course when the company uses the same statistics and procedures. On the other hand, standardization can be emphasized, leaving the company vulnerable to regional variances. The case management firm prevents this by customizing the DSAT Predictor for each region, enabling the conventional prediction model and case business strategy to adapt to regional cultural values and business objectives.

Standardized procedures and data science, when combined, enable the incorporation of new ideas and technology to optimize agent efficiency and customer satisfaction. The business has become more agile due to the community of machine learning models, which allows procedures to be applied across the business while keeping regional nuance by modifying the models to reflect provincial, regional, or country-level distinctions.

How Can AI Help Customer Service?

It's simple to see why survey methods have become so popular: they allow you to ask many people how they feel about something. Focus groups and personal reading and evaluating client comments, for example, were too time-consuming to expand. Now that technology has altered what is possible, tactics must adapt.

The first and most important shift that businesses should make is to refocus their user sentiment analysis. They should begin with qualitative feedback before moving on to the quantitative survey data. Companies could even contemplate abandoning quantitative surveys entirely if they have the necessary tools to analyze qualitative data, e.g., customer service management solutions, social media, user reviews, mails, contact center notes, chatbots, etc.

Benefits of Using AI

The DSAT Predictor eliminates uncertainty and experience-based decisions by calculating the likelihood of a DSAT based on years of historical data and a thorough knowledge of the industry. Because there is less guesswork, operators may aim to resolve situations faster and intervene immediately when necessary.

While data science and machine learning are frequently lauded as tools for mechanization, delighting consumers requires providing both helped and unassisted choices for diagnosing and resolving tech support issues. Rather than cutting staff, the goal is to give agents the tools they need to do their jobs better. Debugging optimized flows, next-best-action suggestions and data-driven analytics all play a part.

Any extra time agents acquire due to their increased efficiency can be spent on situations where they can have a significant effect on the outcome, such as when we know the consumer is unhappy. While agents are working carefully to manage several cases, often with extraordinary attention to detail, the DSAT Predictor allows them to raise their heads and alerts them when past trends suggest we may have overlooked something.

Recursive advancements have fine-tuned and strengthened the DSAT Predictor since its original unification. Compared to the six precursor models, model data have been harmonized and unified across the organization, culminating in an 85 percent data consolidation. The deployment architecture and algorithms have improved model performance by more than 20%, with some business segments seeing even more significant gains. When matters are handled properly, there is a 15% drop in consumer dissatisfaction. The DSAT Predictor now processes over 1.5 million customer inquiries per year across all geographies and business sectors in Technical Support Operations' Consumer division.

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