Improve CX and Loyalty with Predictive and Proactive Service Strategies

Most people don't enjoy spending time on the phone with customer support, and forward-thinking organizations are changing their service strategies to reflect that. Even before the pandemic, some organizations were moving to a proactive and predictive service model across industries and product types. The shift accelerated during the pandemic, driven in part by temporary restrictions on face-to-face channels and social distancing requirements. Now, higher customer expectations for service appear to be permanent.

Salesforce's most recent State of the Connected Customer Survey found that 83 percent of consumers and business customers expect to solve complex problems by speaking to one person, but only 13 percent report that it takes little effort to get cases resolved. That discrepancy between service expectations and the customer service reality has serious implications for businesses. Nearly half (48 percent) of consumers in the survey reported switching brands in the past year based on customer service, while 89 percent of business decision makers said they're more likely to buy if companies demonstrate an understanding of their goals.

Practical Applications for Predictive Service Technologies

The ability to anticipate customers' service needs is increasingly possible with new technologies that can improve customer experience by minimizing or even preventing downtime while also maintaining customer loyalty and opportunities for positive engagement. This is true for both B2C and B2B organizations.

One of the clearest examples is happening in the auto industry, where smart, connected new cars can resolve issues behind the scenes with automatic software updates as the manufacturer releases them. These updates can prevent issues that might otherwise require a service appointment. Data from connected cars can also allow manufacturers to notify vehicle owners when their vehicles are due for routine service and can generate codes that technicians can use to quickly diagnose issues. At a wider level, customer service notifications and vehicle data can help dealerships optimize staffing levels and assign the optimal technicians to repairs that require their specific expertise or experience.

Technology improvements to more basic tools are helping companies of all kinds deliver better service experiences. For example, when chatbots first came into wide use, they weren't very intelligent and sometimes created more frustration for users. But as companies prioritize customer data and analytics and leverage it for their chatbots, we're seeing chatbot capabilities improve to the point where they can provide predictive or proactive service. They can greet customers by name, ask if they're still looking for a specific product, or offer help with a past purchase. Customers are responding positively. Fifty-eight percent now say they've used chatbots for self-service, up from 43 percent in 2020.

Better customer Service Starts with Unified, Accessible Data

Personalized chatbot support, proactive vehicle maintenance planning, and other predictive service options require AI-enabled predictive models that draw and analyze data from an integrated customer interaction history or customer data hub. However, many organizations still have siloed customer data, and they might not yet have good ongoing communication between their product, commerce, and service teams. As a result, customers might feel like they're dealing with different organizations at each step in their journeys, and predictive, proactive service isn't possible. Unifying customer data, making it available across the organization, and aligning internal stakeholder priorities around a customer-centric strategy are key elements of successful predictive and proactive service offerings.

What does this unification look like? One example is the cloud-based contact center, which uses customer analytics and a data-driven strategy to enable AI-based customer interactions. These interactions can be chatbots helping customers with specific products based on their purchase histories or real-time next-best-action recommendations for service agents as they engage with customers. These tools deliver a better customer experience. They can also reduce operational costs. One organization realized a 20 percent year-over-year cost savings on their call center operations through a combination of increased call deflection, higher first-call resolution rates, and handle-time reduction.

More Benefits of Predictive and Proactive Service Programs

While predictive and proactive service is customer-centric, it delivers other benefits to the organization and its employees, in addition to potential cost reduction. Data and analytics can also help companies add recurring revenue through proactive maintenance packages that can reduce customers' total cost of ownership and reduce unplanned downtime.

A proactive approach has employee experience benefits, too. If a service provider has an outage, quickly notifying affected customers can save the service team from fielding a wave of calls from frustrated customers reporting their outage. That frees contact center service agents to handle more complex customer issues. AI-enabled chatbots and other self-service options can also leave service agents available to address the kinds of customer requests that need direct engagement, and AI-enabled real-time recommendations can guide agents through the process of retaining those customers by meeting their specific needs in the moment.

This shift in the service agent's role, from rote tasks to solving more complex problems with better support, positively impacts the employee experience in a couple of ways. The technology that makes real-time recommendations effectively extends employee training time by helping newly trained agents through their beginning period, so they can have more confidence while they're helping customers. That technology also increases the likelihood of solving customers' problems and also gives agents the visibility into customer activity they need to upsell and add value. These benefits can reduce employee stress, increase satisfaction, and potentially reduce contact center agent turnover, which reached an average of 42 percent in 2021.

Predictive service options and proactive service strategies start from a place of customer-centricity. With that commitment and mind set in place, an organization can start by identifying the customer issues that could benefit most from a new approach. Starting with one issue and learning from customers' responses and feedback to the new approach can help organizations learn and refine their data, strategies, and deployments before scaling the proactive, predictive model to more customer interactions. Embracing this strategy might take time and require change management, but it's necessary for organizations that want to meet customer expectations for convenience, problem-solving, and personalized service.

Jaime Lightfoot is principal analyst for digital customer experience at Capgemini Americas. She is an experienced consultant with a demonstrated history of working in the information technology and services industry. She is skilled in requirements analysis, IT strategy, business intelligence, solutions architecture, and business analysis. She is based in Atlanta.