With the power of predictive contact center data, support agents can anticipate outcomes and what information customers need before they realize it themselves—and respond quickly, guiding them to the best answers and highest-quality resolutions. Contact centers have always had to deal with the challenge of engaging with customers.
Traditionally, we’ve done this by picking up the phone when a customer calls in or using email and web chat to answer customer questions. Both of these methods are that they only allow for reactive responses. Customer service agents can only answer customer concerns after they’ve been raised, and people often prefer to use multiple channels at once. For example, they might call a company’s support line and tweet at them simultaneously.
Increasingly, companies are turning to predictive data models to help them engage with their customers more effectively. By building machine learning models that can analyze historical data and predict when a customer might need help, companies can proactively provide customers assistance through the preferred channel.
This article will introduce you to the history and current state of contact center data offerings and the best ways to implement this technology into your business.
A Brief History of Predictive Contact Center Data in Customer Service.
In 1967, Roy Weber, a scientist for AT&T, invented the 1-800 number to help people identify a specific business or service without memorizing a phone number. This feature was handy for businesses and consumers who no longer had to decipher an unfamiliar area code and exchange.
1-800 numbers were already widely used when AT&T introduced toll-free calling to customers across the country with Touch-Tone dialing. However, the rollout of Touch-Tone technology was significant in that it made long-distance calling a more convenient option for end-users than ever before. In addition, its widespread use laid the groundwork for later innovations like predictive dialing and text messaging (SMS).
Also, the development of 1-800 numbers and auto-attendance powered the IVR revolution. However, while IVR technology certainly wasn’t new by the 1990s, its use in the contact center was relatively uncommon. Many of today’s leading contact center solutions were still in development and not yet being used in the contact center. As an example, one of AT&T’s pioneering developments was the introduction of predictive dialing to call centers in the early 1990s. Predictive dialing integrated with existing call center technologies at that time, such as automated attendants, improved efficiency, and streamlined processes.
Contact center data was born with the growth and implementation of these technologies combined with cloud abilities in the 2000s. While predictive analytics technology is used in many industries, it’s particularly well suited for contact center data. This is because it enhances the customer experience by enabling agents to anticipate customer needs proactively. By leveraging historical data on customers’ behaviors and preferences, you can train these systems to provide an optimal mix of information that drives customer satisfaction and reduces time spent on hold and in the queue. This means contact centers don’t have to react to customer requests. Instead, they can proactively engage with your callers using the correct information at the right time through the right channel.
The Problem With Reactive Customer Service.
Reactive customer service is costly and inefficient, but that’s what businesses still rely on. However, it confuses callers employees, and it is tough to measure and track. Answering the same questions, again and again, costs a lot of time and money. In addition, reactive customer service is scalable but not sustainable. When you use contact center data, you know the following best action to take during a call center interaction with a customer. You can predict future events based on past behaviors and understand why customers behave in specific ways. This helps you drive customer loyalty, increase sales, reduce churn, and save money.
The problem with reactive customer service is that while it can help your business address specific customer issues, it is not scalable. Customer and employee frustration grows, as the same customer has to repeat their story repeatedly. At the same time, customer service costs rise as more staff members become involved in each case.
Businesses of all sizes can benefit from predictive contact center data capabilities. However, for smaller companies, the value of such a system is even more significant, and it’s relatively easier to implement. There are obvious benefits to having a platform that will predict every customer’s following best action, whether moving them from a chat application to a phone call or suggesting the best time for a follow-up call. In addition, these systems can help your business build trust and loyalty with your customers when used correctly.
Genuinely personalized service requires advanced contact center data that blends data from different channels and touchpoints. Using contact center data, your organization can gain deeper insights into their customers’ needs based on how they interact with products and services across channels.
A Better Way to Engage Customers.
The future of proactive customer engagement is rapidly approaching. Start preparing today so that you’re engaging with your customers more effectively than ever before. With the growth of predictive customer service, it’s essential to plan for a future in which this technology becomes a standard component of every contact center. But how can companies prepare for what’s next?
These are some key questions to ask:
What will my team look like in the near future? Of course, many contact centers already rely heavily on automation. Still, as self-service channels grow in popularity, you will pay more attention to the people who interact with customers at critical touchpoints. What does this mean for customer service teams? Will a new role emerge that combines human interaction with automated data analysis? How will this role differ from those we have today?
To what extent can I rely on machine learning technology to take care of customer engagement tasks? The answer to this question will depend on your business and the types of tasks you handle. There may be no need for a human agent ever to see customer concerns in some cases. In others, you might want to leverage machine learning technology while still having agents screen out and escalate issues that fall outside of common parameters. As contact centers continue to evolve, it will be essential to know where your priorities and limitations lie.
How can I ensure that customer engagement is optimized for maximum efficiency? As we move forward into an era, you must develop relationships with trusted partners who utilize time-tested and award-winning solutions. With the experience and technology to support your needs, Call Experts is the best contact center partner for your business.
Customer service is a notoriously tricky area to get right. It’s all too easy to have a bad experience when seeking help and advice, but that’s increasingly the result of growing customer expectations, which are often shaped by interactions with other businesses. Contact center data can help contact centers anticipate customer needs and enhance the customer service experience.