Predictive analytics can help evaluate behavioral patterns which can enable call centers to provide better up-sales, improvements, or ticket resolutions by analyzing historical customer behavior and predicting likely future actions. This article will look at the ways predictive analytics and prescriptive analytics are being used in the call center today, and trends to watch for tomorrow.
How Predictive Analytics Increase Customer Satisfaction
The term predictive analytics refers to the use of data, statistical algorithms, and machine learning to determine the likelihood of future occurrences based on historical customer data. According to Dan O’Connell, CSO of Dialpad, a cloud-based contact center solution, the majority of call centers are using predictive analytics to predict hold time, queue time, and length of call. “They are also used to analyze times of high or low call center volume to predict staffing needs, in near real-time, and can therefore help to improve the immediacy of responses from the call center,” he said.
O’Connell said that call centers are increasingly using analytics to identify the behavior and demographics of customers in order to more effectively serve them. “The customer is then matched with an agent who would be the best fit to converse with the person calling in. For example, it may be beneficial to match a caller with an agent who is in a similar geographic location, speaks English as their first language, and is the same age,” explained O’Connell.
Not only will predictive analytics be used in the future to increase conversions, but it will also play a large role in eliminating pain points in the customer journey. “In the future, predictive analytics will be used to forecast customer satisfaction and identify intent,” suggested O’Connell. “By analyzing different factors during a call, analytics will paint a deeper picture of a customer’s happiness with their call; this information can be used to gauge purchase intent, as well. The data will be essential to identify pain points in the call process, why a customer was or was not satisfied, and can create a data-driven road map to constantly improve the call center experience.”
O’Connell’s predictions for the use of predictive analytics are likely to benefit call center employees as well. “In the future, call centers will also use predictive analytics to analyze the satisfaction of not just customers, but of call center agents. Call center agent turnover was already high before the Great Resignation, but especially now, it’s important that company’s use data to gauge agent burnout. The same analytics that measure customer satisfaction based on tone, sentiment, and language, can be used to reduce agent turnover,” he said.
The benefits of predictive analytics today are myriad and can facilitate a more emotionally positive call center experience for customers. “Predictive analytics in the contact center can help customers drive revenue, reduce churn and increase productivity. Essentially, it helps contact centers use time more efficiently by ensuring appropriate staffing and providing direction to ensure the right actions are taken before, during, and after a call,” reflected O’Connell.
Prescriptive Analytics Works in Conjunction With Predictive Analytics
Prescriptive analytics is seen as the next step after predictive analytics and is a branch of data analytics that uses predictive models, AI, and machine learning to recommend the next actions to take for optimal results. By using predictive analytics’ estimation of what is predicted to occur, prescriptive analytics is able to recommend the future course that should be taken.
Using prescriptive analytics, brands are able to simulate the probability of different outcomes and see the probability of each, which helps them to more clearly understand the level of risk and uncertainty they face, rather than relying on averages or “instincts.” Organizations can gain a better understanding of the likelihood of worst-case scenarios and plan accordingly.
Predictive analytics works in conjunction with prescriptive analytics, as the two data analytics technologies work synergistically together. “Predictive analytics can be used to improve prescriptive analytics. While prescriptive analytics look at data and come to a hard, concrete outcome based on a set of rules, predictive analytics can look at data and produce a variety of agile, adaptable options, rather than one concrete solution. Predictive analytics, especially when they are used in real-time, can be used to improve call center functions. For example, with prescriptive analytics, an agent would be told to keep a call to two minutes based on historical data that shows shorter calls make happier customers. But, if a call is running longer than usual or a caller is behaving uniquely, predictive analytics could suggest staying on the line, as it could lead to a sale or a satisfied customer,” said O’Connell.
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Customer Data Platforms Consolidate Data for Predictive Analytics
Chris Bergh, CEO of DataKitchen, Inc., a DataOps consultancy and platform provider, said that more than any other factors, the success of using predictive analytics depends on how well the omnichannel data has been collected, managed, and analyzed to be leveraged. “The challenge for enterprises is that they collect and store vast amounts of customer information in ERP, MRP, CRM, marketing automation, web analytics, call center platforms. and other IT systems. These are all built upon a patchwork of platforms and technologies that don’t easily share information. Putting this information at the fingertips of customer service in the call center — the 360° view of the customer — is mission-critical for customer service, but incredibly challenging from an enterprise IT perspective.”
Like many brands today, Bergh consolidates all the data from the various internal and external sources into a central database using a Customer Data Platform (CDP). “Data engineers use the CDP to further orchestrate the creation of data warehouses (or even flat files) that drive descriptive and predictive analytics creation.”
Using the CDP, customer service call center employees gain a complete picture of all customer interactions with the brand. In this fashion, when a customer calls, all of their information is known by the agent that is assisting them. “This can help enterprises present a unified face to their customers while addressing service issues quickly and efficiently.”
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The Challenges of Predictive Analytics
As Bergh suggested, one of the largest issues that brands face is that there is so much data spread across multiple channels, and analyzing it manually is next to impossible. “The biggest challenge when building predictive analytics models is access to massive amounts of data. You would need to analyze thousands, if not millions, of interactions to build a system that can drive actionable outcomes,” said O’Connell. “At Dialpad, we have access to data that others don’t, so we have the ability to build sophisticated models for a variety of use cases for the myriad of customers we have.”
Getting people to recognize the benefits of predictive analytics while maintaining realistic expectations can be daunting. “It’s also challenging trying to set expectations with technology like this. Predictive analytics can be a great tool in the toolbox, but it’s designed to be directional and should be used as such. Nothing will ever replace the human interactions stemming from call centers, but leveraging predictive analytics can help streamline and optimize agents’ work,” said O’Connell.
Other challenges include a general lack of the high-level skills and expertise that are required for the accurate and effective use of predictive analytic applications. Predictive analytics applications and platforms are generally designed to be used by data scientists who have a deep understanding of AI, statistical modeling, R, and Python. Many brands end up hiring a person or whole team of data scientists in order to obtain the best results from predictive analytic applications. This is beginning to change, however, as new predictive analytical platforms are being designed to be used by practically anyone, and they are being integrated into other solutions such as CDP platforms, creating a unified platform that does not require users to switch from one standalone application to another.
By using predictive analytics along with prescriptive analytics, brands are able to determine the next best action to take during call center interactions with customers. Brands are now able to use consolidated platforms such as CDPs to unify omnichannel data, with predictive analytic functionality built-in, easing many of the challenges that are still faced when complicated data analytics applications are deployed with high expectations and low levels of expertise.