Setting Attainable Goals for AI in Customer Service

"At a time when many executives are looking for new ideas to improve the customer experience, it’s important to think critically about how AI can actually solve your particular challenges."

You may be familiar with the following statistic:

80 percent of firms believe that they are delivering a superior customer experience, while only 8 percent of customers share the same sentiment.

It comes from a 2005 Bain & Company report entitled, “Closing the Delivery Gap,” in which 362 companies were surveyed about their customer service operations.1 Sadly, there’s no evidence that this study is any less relevant today than it was 12 years ago. In fact, a more recent study revealed that only 16 percent of marketing executives thought that their organizations performed well in fulfilling promises made to customers.2 Both studies create a great jumping off point to discuss artificial intelligence (AI) and how it relates to customer service.

It’s hard to find any technology vendor today that is not touting the AI capabilities of its products and services. At a time when many executives are looking for new ideas to improve the customer experience, it’s important to think critically about how AI can actually solve your particular challenges. Before jumping in head first with any vendor, take a step back and evaluate what your vision is for how you engage with your customers, and how you can improve their experience. Here are three steps to setting a vision for AI:

Step 1: Evaluate the metrics you want to improve

Developing a vision first requires you to agree on a multi-level performance dashboard and identify the overall and agent-level needles you want to move. Indicators to consider include:

Overall metrics:

  • Average hold time
  • Employee utilization
  • First call resolution (FCR)
  • Number of cases by case type
  • Average Handle Time (AHT)
  • Interactions per resolution
  • Average resolution time by case type
  • Net promoter score (NPS)
  • Customer retention rate

Agent-level metrics:

  • Number of open issues per designated point in time
  • Resolved cases by case type
  • Average Handle Time (AHT) by case type

Step 2: Develop use cases for AI around weak points

Now think about the life cycle of customer experience, and which performance indicators could be improved with AI. Let’s look at the examples above. If we were to dig deeper into improving customer retention, we might find that customers on the fence are frequently offered the wrong solutions or incentives. This is an area where AI is particularly helpful. It can directly assist customer service representatives (CSRs) in delivering the most relevant actions and offers, resulting in more loyal and satisfied customers.

Let’s also consider employee utilization. When call centers are swamped (because employees are highly utilized), customers go on hold for unreasonable amounts of time. And in some cases, they may balk, never opening a case and just churn. Thus, many of your other case metrics never even register the problem. There’s no reason why automated technologies assisted by AI—like chatbots—can’t relieve CSRs of mundane or repetitive tasks. This brings down the number of customers entering the queue, making CSRs more available to customers who need valuable human support.

With just the two use cases above, you could form a very attainable AI goal. Here’s a sample goal statement: “We want to decrease customer churn by X% using AI. We will do this by: reducing average hold time by deflecting simple cases to chatbots and self-service, and by providing the most relevant next-best-actions and offers to at-risk customers.”

Step 3: Consider the human component of AI

You may see the value in your AI goals, vision and strategy, but do your customers? It’s critical that you communicate how this technology could benefit them.

The last, and possibly most important thing to remember when developing your AI goals, is that your customer-facing employees will be dealing with these changes day in and day out. As the Bain study notes, “while organizations may understand that they are accountable for the full customer experience, they often fail to recognize that the frontline employees who deliver it may be the least respected and empowered group in the company.” Their enthusiasm about this AI vision is key to improving customer experience.

Setting attainable AI goals is just the first step in transforming your customer experience. For a deeper dive, read the full “Artificial Intelligence and Improving the Customer Experience” whitepaper.


1Closing the Delivery Gap, Bain & Company, 2005
2Predicting Routes to Revenue, CMO Council, 2016