As I have pointed out in previous blog posts, customer expectations related to the level and speed of service they receive from brands are at an all-time high. Various research reports confirm that the overwhelming majority of Americans expect a far higher level of service and responsiveness than they did just a few years ago.
If these documented expectations alone aren’t enough to get a brand’s attention, consider the ramifications:
- According to Newvoicemedia, U.S. companies lose more than $62 billion a year due to providing poor service.
- A Propel Software survey reveals that nearly 60% of US consumers will leave a brand after just one bad experience.
- American Express reports that customers in the US are willing to spend 17% more on companies they felt provided excellent service.
As consumer expectations and their related ramifications continue to rise, brands have had to adapt. A primary theme in this adaptation is implementing a predictive approach to customer service. In other words, use data and analytics to anticipate customer needs or challenges at an individual level and take the initiative to offer solutions or offers based on this information.
At a high level, predictive customer service provides the following benefits:
- Improved Customer Experience: Predictive customer service allows companies to anticipate customer needs and offer solutions even before the customer contacts support. This results in a more proactive and personalized customer experience. For instance, a customer service agent might recognize that a customer’s account has been temporarily suspended due to suspicious activity and offer to restore the account, guiding the customer through the process before they even realize there was a problem.
- Increased Efficiency: By predicting customer problems and offering solutions in advance, companies can resolve issues faster and more effectively, leading to increased efficiency and reduced response times. For instance, a customer service platform might automatically identify that a customer’s internet connection is down and offer to remotely troubleshoot the issue, or redirect the customer to a self-help resource that can solve the issue quickly.
- Better Resource Utilization: Predictive customer service helps companies allocate their resources more effectively, reducing the need for manual intervention and freeing up support staff to focus on more complex issues. For instance, a company might use customer data to determine that a customer’s issue is a high-priority problem that requires immediate attention from a specialist, and redirect the customer’s request accordingly.
- Increased Customer Satisfaction: A smoother and more personalized customer experience leads to increased customer satisfaction, which translates into increased loyalty and repeat business. For instance, a company might use customer data to recommend a product or service that the customer is likely to be interested in, and proactively offer a discount or special promotion to encourage the customer to make a purchase.
At Servicing Solutions, we utilize powerful Artificial Intelligence and Predictive Modelling tools to learn customer behavior and preferences on an individual level. This allows us to personalize the next appropriate engagement for each individual customer.
- If we know a particular customer consistently makes payments 10 days after a bill is due but within the grace period before it is considered late, we can automatically refrain from sending the customer a reminder or collection notices. However, if their payment is not received consistent with the customer’s normal behavior, we automatically send a reminder for them to pay now to avoid late fees. In both of these examples, we are using powerful modeling tools to understand how to best engage with a particular customer.
- We also use AI tools to understand what the best time or communication channel is to contact a particular customer, which greatly increases our ability to engage and build relationships with customers.
- We utilize precise customer records and profiles to understand what products/services the customer has purchased in the past or issues/challenges they have faced. Having this data available anytime a customer engages allows our agents to provide the most personalized service or advice possible.
The common theme with predictive customer service is to take the sole burden off of the customer by providing them with timely information and problem-solving resolutions before they even have to ask.
Ready to Invest In Real Experience to develop a predictive customer service model that will improve customer satisfaction? Reach out to us at email@example.com.