The ideal customer service scenario is solving a customer's problem before the customer even knows the problem exists. Predictive customer service driven by artificial intelligence (AI) using product and service data offers companies the tools to work toward this ideal scenario while improving not only the customer experience, but also the employee experience while reducing costs. This is a win-win for the customer, the employee and the company.
Virtually all industries, both B2C and B2B, including manufacturing, media, retail, hospitality, and life sciences, can benefit from predictive service. Predictive service is especially well-suited for industries with complex assets and service requirements. In particular, forward-thinking companies are using predictive service to schedule parts replacement. We're also starting to see predictions related to earlier steps in the parts value chain, such as where parts need to be stored and when they need to reach warehouses so they can be delivered quickly when needed, shortening both the delivery and the service time.
Analytics-driven predictions can also improve customer experience in self-service channels, and that paves the way for major improvements in company performance and customer retention. High-performing companies are 76 percent more likely to offer self-service than lower-performing organizations, and many customers prefer self-service because it saves them time when it works correctly.
All these use cases aim to identify and address customer pain points before problems arise. Getting to that point requires a series of steps to lay the groundwork for a successful predictive service program. Here are the key components of your predictive customer service checklist:
Develop unified customer profiles.
To identify and address customer pain points before problems arise, businesses need a single customer profile. Marketing, commerce, service, and sales organizations must work together to update that single customer profile during and after every touchpoint. When all of the customer data is stored in one place and kept up to date, anyone who engages with a customer can review all of the other interactions to get the full story of the customer journey so far. That enables service reps to provide a seamless comprehensive experience that can result in long-term loyalty and generate more business for the company.
Select predictive-service analytics tools.
There are plenty of tools, including open-source applications and others from large cloud players and commercial off-the-shelf vendors. For companies with major asset maintenance requirements, especially those that offer equipment or maintenance as a service, Internet of Things (IoT) devices are key customer service tools. That's because IoT monitors enable companies to track asset performance and incorporate predictive maintenance to avoid asset downtime. IoT devices also support digital twin applications that allow companies to influence maintenance, manufacturing and even R&D activities through virtual modeling based on real-world data.<
Identify the signals that predict service needs.
The customer-service and asset data logged and analyzed by your predictive service tools' artificial intelligence and machine learning can forecast customers' needs. This can be as simple as an upcoming subscription renewal alert that prompts a service team to reach out to customers before their service expires. It can be as complex as analyzing the amount of wear on an industrial motor based on the equipment's service history, total hours in operation, current operating conditions, and expected life span to determine when the motor needs routine maintenance.
Offer service proactively whenever possible.
As your predictive service applications identify patterns that indicate an upcoming need for service, your service organization can be more proactive in serving customers and in planning longer term. For example, the same analysis that anticipates usage-based parts failure might also apply to other asset scenarios. This way, companies can gain better insight into which products or parts fail first and build in predictive maintenance visits to streamline service and enhance business continuity.
The same patterns of customer behavior might also extrapolate to new market segments and audiences. Good data analysis can also show where bots and other AI-based service options can automate some service processes. All of these applications can improve customer satisfaction and strengthen brand loyalty.
Enable smarter reactive service.
Not every problem can be resolved before the customer has an issue, at least not yet. When customers need human support, AI-driven predictive service can help service reps deliver faster, better personalized responses in real time. For example, in addition to surfacing relevant service information for CSRs, predictive analytics can guide CSRs through the appropriate steps in complex troubleshooting based on the outcome of each step.
With each service interaction, there's more data to analyze and leverage to apply to future service requests. That includes data about the service interactions, too. Businesses can use sentiment analysis to see which service approaches perform best and to find upsell and cross-sell opportunities. AI and machine learning can also evaluate CSRs' performance to match their strengths to specific customer calls and identify areas where CSRs might need more training to deliver better tailored service and a superior customer experience.
Predictive service is enabled by today's data analytics technology, but it's ultimately driven by customer preference. Many customer segments, particularly younger consumers, prefer a one-on-one personalized customer experience but are OK with self-service as long as it's fast and effective, and especially if it helps them avoid a voice call to customer service. With mature and impactful AI, predictive service makes it possible for companies to lower their service costs while improving the customer experience during service interactions and over the longer term.
Bill Donlan is executive vice president and DCX/Salesforce service line leader at Capgemini.