Contact centers have been leveraging innovative technologies such as AI, machine learning, and data analytics to provide multichannel customer support, deliver superior customer experience, and boost agent productivity. One such technology that is enabling call centers to make data-driven decisions is predictive analytics. It is a branch of advanced analytics used for creating future predictions.
Predictive analytics in a contact center can go beyond predicting future events. The insights offered can help improve service quality, operational efficiency, and even agent productivity. With endless possibilities of predictive insights, call centers can truly transform how they engage with customers.
Predictive Analytics Definition Explained
Predictive analytics uses current and historical data to produce insights into what might happen in the future. These tools use technologies such as data mining, AI, Machine Learning, sentiment analysis, text analytics, statistical and predictive modeling, and more to analyze current data and make future predictions. Patterns found in the historical and transactional data are used for identifying risks and opportunities.
In simpler words, predictive analytics answers what is most likely to happen based on the current data, and what you can do to change that outcome. In contact center services, these insights can improve everyday business operations and help achieve a competitive edge.
Ways Predictive Analytics Can Improve Customer Success
Predictive analytics has changed the role of contact centers from a basic customer service offering to a more strategic differentiator, making drastic improvements in customer satisfaction and a company’s financial performance.
Below are some ways predictive analytics can maintain customer relationships and improve contact center success in general:
1: Reduce Call Volume
Through advanced analytics, a company can conduct end-to-end analysis on a huge volume of customer data and look at the flow of calls to identify areas for improvement. Based on this output, it can design a solution to improve the customer journey and use tools such as IVR to accelerate and route calls to the right agents without significantly altering the workforce management schedules, especially in seasons of peak call volume.
2: Forecast Customer Needs
Predictive analytics tools are highly efficient in detecting a change in customer behavior, allowing your agents to be more proactive and tailor their messages in anticipation thereby serving them effectively before the customer even knows they have a need.
Analytics help a contact center ensure it has the right amount of support to address the customer’s needs in a timely fashion and exceed their expectations. With this approach, agents can offer superior customer service.
3: Improve Agent Performance
Besides improving customer experience and boosting satisfaction rates, predictive analytics in contact center can also improve agent performance.
Predictive analytics software can monitor each agent’s call and pick up the root causes behind poor customer engagement. These causes could include the agent’s resolution time, slow/fast-talking pace, inappropriate tone, accent issues, inability to engage with the caller, lack of empathy, and so on.
Listening to all agent calls manually and identifying errors based on assumptions can be cumbersome and counterproductive. Advanced analytics shortens the evaluation-feedback cycle and empowers your agents to improve their technique. As agent productivity improves, this exercise can leave an immediate positive impact on customer satisfaction. The performance insights produced can also be used to improve call center training programs as common agent mistakes can be addressed early on.
4: Increase Sales
An outbound call center of an IT support company or any other business with a focus on sales and a poor first-call success rate can significantly leverage the power of analytics. Normally, it takes 2 to 3 follow-up calls to convert. The decision on whether to call a customer or not is mostly based on agent judgment, hence successful conversions are not always guaranteed.
Predictive analytics offers an objective way to make follow-up calls. The software extracts data sets to identify prospects who are likely to make a purchase decision. It also looks into the past sales record for predicting months of peak sales for optimal agent allocation. When agents prioritize callbacks based on such insights, they can maximize conversion.
5: Reduce Customer Churn
Every business at some point struggles with customer churn also called customer attrition. Retaining existing customers, no doubt, is less expensive than acquiring new ones. Using predictive analytics, contact center agents can identify the customers at risk of churn and take proactive measures to enhance their experience. Your agents can lure these high-risk customers by offering an incentive that best matches their needs to ensure they continue doing business.
With ever-growing customer data, now is the right time to use predictive analytics in contact center processes and personalize the customer care in metaverse experience by tailoring conversations based on their interests and unique needs.
Frequently Ask Questions
What is predictive analytics?
Predictive analytics uses data and techniques such as Machine Learning, Artificial Intelligence, and statistical algorithms to identify future outcomes based on historical data.
How to do predictive analytics?
Predictive analytics is performed using ready-to-use software embedded with predictive capabilities. The software uses statistical tools like forecasting, data modeling, and regression analysis alongside technologies. Such as Machine Learning and Artificial Intelligence to answer questions around future possibilities.
Why is predictive analytics important to a contact center?
Predictive analytics can offer a contact center with greater insights into how to better meet its KPIs. To improve agent performance, predict trends, manage a higher call volume, and improve conversions.