The future customer contact center is here. Learn how businesses of all sizes can get out in front of issues and truly delight customers leveraging predictive customer service analytics.
Predictive customer service analytics make the impossible possible.
Imagine being able to reach out to a customer to let them know there’s an issue and you’ve already found a solution that’s underway. All before she is even aware there was a problem in the first place. Or being able to anticipate how a person can have a better experience with a product and let them know in the exact moment of relevance.
Customer service analytics is quickly maturing, paving the way for proactive and predictive care. For generations, consumers contacted a company when they had an issue. It was very disruptive to a person’s life: when you had a problem, you stopped doing whatever else you were doing to search for the contact information and reach out, only to be placed on hold or otherwise have to wait for a response. This led to widespread customer frustration.
As our society becomes more customer-centric, we’re seeing companies of all sizes start to leverage customer service analytics to get out in front of issues and anticipate problems before they exist, delighting their customers with pertinent information at the exact moment of relevance or identifying opportunities to surprise a customer with a hyper-relevant experience.
Acting on predictive customer analytics opens the door to build brand love as we’ve never seen before – with customers truly believing that a company is looking out for them and has their best interests at heart.
Dream Big: A Glimpse at What’s Possible with Customer Service Analytics
When thinking about how predictive customer service plays out in real life, it’s important to pinpoint various issues that occur based on your customer lifecycle, product usage and maturity, and situational-context.
Here are a few examples:
- A person is waiting for a ride-sharing car service. The company lets them know that if they walk 4 blocks, they can shave 10 minutes off of their total travel time based on real-time traffic patterns.
- An airline recognizes that based on current traffic conditions and security line congestion, a person is going to miss their flight and sends rebooking options.
- A company that makes vacuums lets a person know that they need to replace their filters after they have owned it for two months and not repurchased replacement filters. The company provides a seamless way to place an order (i.e. a one-click check-out in a social message feed) and a step-by-step tutorial once their order arrives.
- An electricity company lets customers know that a team has been dispatched to fix a power outage in their area, and the estimated restoration time is 2 hours.
- A company anticipates a customer is about to churn, or jump the ship, so it reaches out with a personalized incentive to extend a contract.
This looks worlds apart from stories like Frontier Airlines failing to have any communication with the parents of two young children who were traveling solo and diverted to another airport due to weather. The poor parents never received an update until their children borrowed someone else’s phone. Had the airline had an AI-powered travel chatbot in place, a message could have been automated to the parents as soon as the decision was made to land at a different airport.
The Nitty-Gritty: How to Implement Predictive Customer Service Analytics
This all sounds truly magical, but is it actually possible?
If companies start putting their data into action and leveraging AI to scale efforts, predictive customer service is achievable. Companies need to leverage predictive algorithms to identify patterns from a sea of historic data and empower AI to act on signals from various internal systems (CRM, order management, etc) as well as contextual triggers within the burgeoning Internet of Things. Companies can then automatically reach out to the right customer with the right information at the right time. Here’s how to get started:
Analyze past data to find patterns based on product maturity and consumer behavior. Understand when people are reaching out and why, and leverage machine intelligence to determine how this correlates between various audience segments and within different contextual circumstances. This will help you pinpoint various opportunities to solve an issue on an individual level (i.e. You might find that first-time customers reach out for product support after 12 months facing this specific issue. You can proactively intervene and provide steps to maintain top performance).
Tap Into Context
Whenever possible, act on signals from the physical world to anticipate issues before they happen. This can be from third-party data like weather and news, from location and other personal data that a customer has shared with you, as well as from a growing number of “things” in our lives – i.e. smart refrigerators, TVs, and even crockpots. With more products becoming connected, it’s now possible for companies to anticipate when a product may need support or is at risk of malfunctioning, enabling you to reach out with the right advice or even schedule an in-home repair. Volvo, for example, uses signals from its Early Warning System to predict when parts might break down and under which weather conditions. The company could use this to alert drivers in a specific area who have not had service in over one year to make sure to rotate their tires as a snowstorm develops on the radar.
Personalize to the Individual
Identify when – and with what – to surprise and delight a customer with a special offer or upgrade. Predictive support is really offering personalization on overdrive. Caesar’s Palace, for instance, leverages troves of historic data to anticipate which upgrades to offer to specific guests, understanding that free meals would make some guests salivate while others prefer entertainment options. Netflix, on the other hand, sends personalized emails of things to watch this weekend based on your viewing habits.
Empower AI to Act
When things change within your CRM (i.e. your customer has owned a printer for 3 months and is most likely going to run out of toner), inventory management (i.e. a sweater is back in stock), or logistics (i.e. an order is delayed), you can automatically reach out with relevant information in the moment of relevance. It’s not possible to scale predictive customer support without the help of AI. Leveraging deep learning, you can fine-tune your modeling over time based on how customers behave and how CSAT changes. You might find, for instance, that reaching out 10 weeks instead of 12 to prompt someone to repurchase toner results in more revenue and happier customers.
Predictive customer care is the future of the contact center. Solving issues before they exist and individualizing a moment to enhance the customer experience will define the companies that are truly customer-centric.
Are you ready to get out in front of issues and be the ultimate hero in your customer’s eyes? Let’s chat about how we can enable your company to act on predictive customer analytics today.
For more information on AI and customer service, visit:
- 7 Ways to Provide Personalized Customer Service (Plus Benchmarks and Stats)
- How a SaaS Chatbot Can Reduce Churn Rates
- Best Help Desk Software Platforms In 2022 [Features, Pricing, and More]
- Best Chatbot for a Website in 2022 and Beyond [Review and Key Features]
- Zendesk vs. Freshdesk: Which Is Better?
- Chatbot Evaluation Metrics: Measures of Success
- Customer Service Tools: The Pros and Cons of AI in Email Support
- Customer Experience Tools to Elevate CX