Moving towards predictive customer analytics for excellent customer support

Blog - predictive customer analytics

Historically, customer support methodologies have seen companies take a reactive approach to customer issues. Improving customer experience was more about finding solutions to self-diagnosed customer issues than companies proactively taking steps to improve the value and quality of their products and services. 

At best, reactive customer support is time-consuming and “behind the curve”; at worst, it sees support agents being unable to own and resolve tickets end-to-end, causing a spike in engineering escalations and further delays and disruption to internal teams and customers alike. The end result? Frustration and dissatisfaction on all sides.

Whether you’re in B2B SaaS, e-commerce, or another industry, customer happiness, and employee satisfaction are likely to be foundational for success, making alternative approaches increasingly attractive. Yesterday, reactive customer support was status quo. Today, with the advent of predictive, proactive capabilities, it doesn’t need to be. 

“Designing great customer experiences is getting easier with the rise of predictive analytics” - McKinsey & Company 

What is predictive customer support? Predictive customer support means that companies can help customers to resolve issues before they occur and sometimes even before they are even aware of them!  Predictive customer analytics leverages AI to observe customer activity and find patterns in the data. The patterns and data uncovered can then be used to understand how a customer is using the products and detect potential issues that a customer may face. 

Salesforce found that 66% of customers expect companies to understand their unique needs and expectations. How do you measure up? 

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 the field of Artificial Intelligence (AI) enable this data to be leveraged 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. With this, support agents are empowered with accurate and timely customer knowledge. 

Why are companies moving towards predictive customer support? How does it help companies?

Some of the ways predictive customer support can help companies include: 

  • A win-win situation for everyone: Predictive customer support leads to increased customer satisfaction as it reduces the time taken to resolve issues -- and sometimes means that there’s no delay at all! Meanwhile, proactive support is likely to see support agents experience greater work satisfaction because they are dealing with fewer unhappy customers and able to focus on impactful work. The engineering team is also likely to see fewer tickets being escalated to them. This would ultimately reduce 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. According to research by McKinsey, due to the COVID19 pandemic, 36 percent of US consumers reported trying a new product brand in the previous three months. Thus, more than ever, companies need to adopt predictive customer support to improve customer loyalty. 

  • Reduce customer churn: By gathering customer data with predictive customer analytics, customer support management 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. 

  • Proactive Support: More companies than ever are finding ways to increase productivity proactive support. One way to achieve this is by using predictive customer analytics to be 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. 

An energy company can use historical data and real-time data to determine why a customer’s energy units run out faster than usual. The customer support agent or customer success team can contact the customer and suggest alternative options or solutions to avoid running out of energy units quickly. 

Predictive customer support enables companies to be proactive, providing value to customers, support agents, and the business as a whole. IrisAgent uses predictive customer analytics to identify potential customer issues and provide suggestions on how to solve the issues. Schedule a demo today to see for yourself.