Customer service in the call center is traditionally reactive: customers call in when they have an issue, and agents assist them. Typical call center goals and metrics–including average handle time and first call resolution–revolve around resolving customer issues as efficiently as possible.
However, call centers are increasingly turning to predictive analytics–the practice of using historical data, statistics, and modeling techniques to predict future outcomes–to take a more proactive approach to customer service. In some cases, predictive analytics can help reduce the need for customers to call (e.g., by enabling agents to forward-solve issues that frequently lead to repeat contacts). And when customers do need to call, predictive analytics can help improve their experience (e.g., by reducing hold times, routing them to the agent most experienced with resolving their issue, ensuring the agent shares the offers most likely to appeal to them).
Here are a few of the more well-known use cases for predictive analytics:
- Predicting hold times. This helps set expectations for customers waiting on the phone and helps call center supervisors manage the queue.
- Predicting call volume and peak call times. Forecasting models use historical data to predict changes in call volume over time, helping call center managers schedule the right number of agents to meet demand.
- Routing calls. Predictive call routing uses available customer data to direct calls to the agents best-suited to handle them.
While these are some fairly straightforward examples of predictive analytics centered around operations, call centers can also analyze the past behaviors of customers to predict their future needs or expectations. One of the ways businesses do this is by using conversation intelligence software.
Conversation intelligence software analyzes the language customers and agents use (in phone calls, chats, emails, or any other conversation channel) and extracts insights call centers can leverage to make informed decisions. For example, a financial institution’s call center might discover that customers calling in about a charge dispute have an increased probability of churning. Armed with this information, call center leaders could coach agents on how to handle charge disputes and work with their knowledge management team to create new articles on the company’s process for charge disputes. By taking these steps, the business could increase their transparency, reduce customer frustration, and reduce churn risk.
5 ways predictive analytics benefit call centers
1. Allocate resources efficiently
The workforce is the biggest overhead cost for call centers, so it’s critical that call center leaders hire and schedule the right number of agents to meet demand. If the call center is overstaffed, it’s wasting money (and its people’s time). If it’s understaffed, customers are more likely to get frustrated with long wait times and rushed service, and agents are more likely to burn out and leave. Predictive analytics help call centers anticipate when call volume is likely to increase or decrease, enabling them to schedule the right number of agents for each shift or to staff up or down to meet changing demand.
2. Improve first call resolution
Live service channels, such as phone and chat, cost businesses an average of $8.01 per contact. And any customer service interaction is four times more likely to drive disloyalty than loyalty. Between the cost and the impact on loyalty, call center leaders have a strong incentive to reduce repeat contacts.
Predictive analytics can help call center leaders uncover patterns in issues discussed on initial calls and repeat calls, enabling them to coach agents to forward-solve on the first call so they can reduce the likelihood of the customer calling back.
3. Improve sales offers
Call centers with high volumes of inbound or outbound sales calls need to equip their agents with insights into the offers that are most likely to resonate with buyers. Predictive analytics can help sales leaders understand which offers–and agent behaviors–are most likely to lead to a conversion. Sales leaders can also use predictive analytics to understand which cross-sell and upsell offers are most likely to be successful based on a customer’s previous interactions.
4. Reduce churn
Call centers often rely on survey data to determine which customers are dissatisfied and at risk for churning. However, survey response rates are typically low–often in the single digits–meaning that businesses aren’t getting solicited feedback from a large percentage of dissatisfied customers.
By analyzing customer conversations, call centers can identify churn trends and isolate leading indicators of churn risk, such as customers making remedy demands or agents failing to set realistic expectations. This can help them identify customers to proactively reach out to, agents to coach, or processes to change so they can improve their customer retention.
5. Make strategic CX decisions
Predictive analytics help call centers understand how the interactions a customer has had with a business so far are likely to impact future actions. Using conversation intelligence software, call center leaders can determine whether a customer was satisfied or dissatisfied with their service (even if they don’t complete a survey), what factors impacted their satisfaction, what offers they are most likely to respond positively to, and even how they are likely to engage with the business in the future. These insights help call centers make strategic decisions to improve the customer experience and agent performance.
Even small changes can have a big impact: for example, one of our customers, a leading telecom provider, identified the agent behaviors most likely to improve first-call resolution, launched monthly challenges to encourage those behaviors, and saw a 28% reduction in repeat contacts in 60 days.
How Creovai delivers predictive analytics
As a conversation intelligence platform, Creovai analyzes customer conversation data and delivers insights that call centers can use to anticipate future customer behavior and improve future experiences. Here are a few of the specific ways we use advanced analytics help call centers take a more proactive approach to customer service:
CSATai
Customer satisfaction (CSAT) is one of the most important metrics in the call center–but it usually depends on low-response-rate surveys. To give businesses a more holistic view of customer satisfaction, Creovai created CSATai. We developed machine learning models that can predict a customer satisfaction score (positive, negative, or neutral) for every customer interaction based on the words and phrases used.
When call centers get a predicted CSAT score for every interaction, they are able to mine their conversations for deeper CX insights. For example, they can analyze a sample of negative CSAT calls and look for common trends in issues or products mentioned. They may surface previously unidentified issues and work with the appropriate departments to address them, reducing future pain points for customers.
Customer Effort Index
Customer effort is the biggest predictor of loyalty. According to research shared in The Effortless Experience, 96% of customers who have high-effort experiences report being disloyal, compared to only 9% of customers with low-effort experiences. By tracking customer effort–and identifying the causes of high-effort experiences–call centers can eliminate points of friction and deliver better customer service.
To help call centers track effort without having to rely on surveys alone, we created the Customer Effort Index (CEI). CEI is a machine learning model that predicts the level of effort a customer would say it took them to resolve their issue. Our team developed the model using conversation data and survey responses from tens of thousands of customer interactions across a wide range of industries. Call centers can isolate their high-effort conversations and look for trends, allowing them to identify areas for improvement in their customer service.
ChurnRx dashboards
Conversation intelligence helps call centers determine when customers are getting ready to churn, even when they don’t explicitly say, “I’m getting ready to leave you.”
Creovai has a set of ChurnRx dashboards that are specifically designed to deliver insights into churn risk–and the factors the call center can control. Call center leaders can track the leading indicators and top causes of churn so that they can take action when churn risk factors appear in a customer conversation. For example, if a call center leader determines that remedy demands are one of the most common leading indicators for churn, they might develop a new process of escalating remedy demand calls to the agents who are most experienced at addressing these demands.
By taking a proactive approach to churn risk, call centers can save more at-risk customers and increase their loyalty.
SalesRx dashboards
Creovai’s SalesRx dashboards let call centers track customer behaviors and agent performance in sales conversations, giving them insights to improve their sales processes, offers, and agent coaching–and ultimately increase their revenue. By tracking common objections and the most successful rebuttals, sales leaders can coach their team on how best to address the objections that will inevitably come up on future calls. And by analyzing the behaviors of the agents with the highest conversion rates, sales leaders can give lower performers actionable feedback to increase their own conversion rates.
Final takeaways
Your customers provide a lot of valuable information in conversations with your call center agents–including insights into how they are likely to rate their satisfaction, how likely they are to buy again or churn, and why they may contact your business again. All of these insights can help your business anticipate future customer needs, eliminate unnecessary calls, and improve the overall customer experience.
