Moving toward Predictive Customer Analytics for Excellent Customer Support

Customer Support Agent

Historically, customer support teams have taken a reactive approach to customer issues.

Improving customer experience was more about finding solutions to customer-reported issues than proactively taking steps to improve the value and quality of the company’s products and services.

Reactive customer support is time-consuming and “behind the curve”; at its worst, it prevents support agents from owning and resolving tickets end-to-end, causing a spike in engineering escalations and further delays and disruption to internal teams and customers alike. The end result is frustration and dissatisfaction on all sides.

Whether you’re in B2B SaaS, e-commerce, or another industry, customer and employee satisfaction are likely to be key to success, making new approaches more attractive. Yesterday, reactive customer support was status quo. Today, with predictive, proactive capabilities, it doesn’t need to be.

Predictive customer support means that support teams resolve issues before they occur and sometimes even before customers are aware of them. Predictive customer analytics applies AI to observe customer activity and find patterns in the data. The patterns and data uncovered can then understand how a customer is using the product and detect potential issues a customer may face.

Modern customer support teams use historical customer data in combination with real-time data to understand customers’ behaviors, needs, and pain points. Technological advances in AI enable this data to solve problems as and when they occur (and sometimes before!), reducing delays and boosting support agent productivity by providing front-line support staff with real-time suggestions on how best to solve potential issues, as well as routes to resolution. The data insights can help prevent customer escalations and churn.

To get predictive customer support right, companies need to have a technology stack that enables support agents to take effective action to resolve customer issues. What’s needed is the ability to collect customer data and share insights with the support agents in real-time. It empowers support agents with accurate and timely customer knowledge.

Some ways predictive customer support can help companies include:

  • Increasing both customer and agent satisfaction: Predictive customer support leads to increased customer satisfaction by reducing resolution times — and sometimes instantly. Meanwhile, proactive support is likely to see support agents experience greater work satisfaction because they are dealing with fewer unhappy customers and can focus on impactful work. The engineering team is also likely to see fewer tickets being escalated to them, ultimately reducing business costs for companies.
  • Improving customer loyalty: One of the most powerful ways to drive customer loyalty is by providing delightful customer support. Customer support is about providing positive customer experiences by quickly resolving any issues that the customer may face or is already facing. Doing this effectively will improve customer satisfaction and keep the customer happy, reducing the risk of future churn.
  • Reducing customer churn: By gathering customer data with predictive analytics, customer support leaders can identify customers with a high churn risk and quickly take action to improve customer experience. With the help of AI, companies can detect where these customers are having difficulties and offer targeted solutions.
  • Increasing productivity: More companies than ever are increasing productivity through proactive support. One way to achieve this is by using predictive customer analytics to stay ahead of their customers. By detecting potential customer issues, companies can reach out to the customer with a satisfying solution. This can even be a “wow” experience for the customer.

Predictive customer support enables companies to be proactive, providing value to customers, support agents, and the business as a whole. IrisAgent uses predictive analytics to identify potential customer issues and provide suggestions on how to solve the issues. To learn more, visit

About the author

Palak Dalal Bhatia is founder and CEO of IrisAgent. Previously, she led product management for stateful container applications at Google. She also invested in early stage technology startups while working in the venture capital industry. She began her career as an engineer at Microsoft in their search engine team. She completed her MBA from Harvard Business School and bachelors from IIT Bombay.

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