Contact Center Predictive Analytics
Contact center predictive analytics can help your organization find ways to improve your call center. Predictive analytics uses data science to make predictions so businesses can make better decisions. In call centers, data analytics centers on the customer experience and ensuring that agents are covering the right information and respecting compliance issues.
To add predictive analytics to your phone service, you should find a Contact Center as a Service (CCaaS) vendor and let them know you're interested in predictive analytics and reporting. Many call center software platforms have analytics functions as a built-in feature.
What Are Contact Center Predictive Analytics?
Predictive analytics can be used in a variety of different contexts, but contact center predictive analytics specifically focuses on contact center business operations. Analytics is used to draw conclusions about and use data more effectively. Call centers and contact centers rely on analytics to improve their business operations.
Understanding Predictive Analytics
Using a variety of predictive methods, predictive analytics can help your organization determine trends statistically and can help you identify patterns of behavior.
Here are a few predictive analytics methods:
- Data mining
- Machine learning
- Deep learning
- Statistical models
Predictive analytics techniques can guide call centers through how to interpret the data they're gathering on a regular basis. It isn't good enough to just gather information, you have to know how to use it and have a methodology in place to use it effectively. That's how predictive analytics can help.
Once you have a data set to work with, you'll need to determine how you'll use predictive analytics to get the most from what you know. Your contact center predictive analytics software vendor may recommend specific methods, but your software platform may be able to conduct some analysis automatically.
How Can Predictive Analytics Be Used in a Call Center Setting?
In a call center, predictive analytics has a variety of uses:
- Boost the customer experience: Thanks to predictive analytics, you can identify the causes of customer churn and improve the customer experience in order to reduce the percentage of customers you lose over time.
- Measure customer satisfaction: You really need to know how your customers see your organization and the service you provide if you are going to make any changes. Analytics allow you to put this to a quantitative number measurement.
- Find agents who need additional training: Analytics can help you pinpoint individuals and teams who have room for improvement and need further coaching.
- Tag non-compliant language and topics: Using analytics, you can tag non-compliant language and behaviors from agents.
- Ensure the right topics are covered: Analytics can help you determine if your organization's agents are following scripts, covering the right topics with customers, and reviewing the right information.
- Reduce the need for finding new customers: Customer retention is important for businesses because acquiring new customers can be costly. Improving customer retention can really make a difference, then, for your entire organization.
- Find dissatisfied customers first: Analytics can help you detect dissatisfaction earlier, so you don't have to worry as much about dissatisfied customers becoming increasingly frustrated with your business and missing out completely on a resolution for any problems.
- Notice behavior changes: If something is changing in the business environment, analytics can help you notice it fairly quickly so you can respond intelligently as a company.
With the right information about your customers, you can make important changes to your business and prepare your organization. Predictive analytics make more of that possible.
Getting Started with Contact Center Analytics
Analytics allows contact centers to determine whether customer experience campaigns are succeeding or failing. Call center management can readily check the contact center's performance by looking at customer experience, customer satisfaction, customer retention, and other important metrics. Thanks to analytics, management can also interpret data as it's collected. You can notice trends in the industry, measure the impact of marketing campaigns, and more.
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