In the world of Customer Service, Artificial Intelligence (AI) is one of the hottest buzzwords of 2019, followed closely by Robotic Process Automation (RPA). By definition, RPA uses software to perform repeatable tasks. AI “observes” and “learns” trends and behaviors to optimize current processes. They can work together to automate tasks that better learn the customer, predict their needs and provide a more satisfying experience.
But moving to AI can be challenging, not only technologically, but also from the business perspective because the benefits are often not quantifiable and have yet to be proven. While we know that AI and RPA software can identify relevance and correlation of factors a human analyst might miss, and analysis-driven approach can build confidence, show ROI, and lay the foundation for a more autonomous future.
In the customer service space, AI and RPA technologies can be part of an intentionally designed solution to provide an effortless experience for your end users. AI and RPA can be used to identify customer situations and determine how to best handle them, including directing them to the best agent or function to support their situation. Focusing on a few initial use cases can make the process more transparent, show ROI, and generate buy-in. Let’s take the case of “retention” as an initial example. Identifying key customer attributes that can predict that a customer contact is retention-driven can improve customer service operations (less agent transfers) and business metrics (better retention rates). (Take this one step further into the future, and you can proactively engage potential churn customers before they even get a chance to leave.)
Start With a Core Use Case and Human Analysis
Start with some basic data analytics and examine key customer attributes, including customer contacts, and the contact results. As an example, look at how many days from the contact until the customer’s subscription expires and compare that to call reason. You may find that if a customer contacts you within X days of their expiration, then Y% of the time it is regarding cancelling or renegotiating. There may be one or two other key factors to examine. Keep the list small, you can expand later. The more complicated the analysis rules, the harder it will be to get others on board.
Use the Data and Results to Build the Business Cases and Confidence
Use the data to make the business case. If those contacts were now automatically routed to retention agents, how much would be saved in agent time and/or improved in retention rate? This is the business case that starts to show the value of customer information driven handling. And implementation of AI and RPA can provide the results.
Build a Foundational Architecture for Future Complexities
Implementation architecture also becomes key to laying a future foundation. Separate the analysis engine from the channels. Modify the channels to reach out to the analysis component, allowing the analysis engine to make the recommendation to the channel. The channel just needs to know how to implement the recommendation, not how to determine it.
As you implement a few use cases, you will be gaining acceptance that attribute-driven customer service is a benefit to your organization. You can then start expanding the models and start looking at more automated, self-learning models and tools. Because you have architected the analysis engine component as a separate engine, it can become more and more sophisticated with minimal impact to the individual customer service channel technologies. This approach not only builds the capabilities iteratively, it also builds the business buy-in.