Resolving customer problems or complaints may sometimes feel like a race to the finish line, but it’s up to the agent and the business that employs them to ensure it’s not the customer who feels exhausted by the time they reach it.
Over the past few days we’ve been featuring a series of posts on this blog that look at some of the most common metrics in customer service management and where artificial intelligence technologies such as Salesforce Einstein, which is now part of Service Cloud, could help. The first, customer satisfaction or CSAT scores, is one of the best known. The second, Net Promoter Score (NPS) is arguably one of the most popular based on how it ties into brand loyalty. Compared to those, customer effort score (CES) may be the new kid on the block, but it could also be the one where AI becomes particularly advantageous.
As its name suggests, customer effort scores are less inwardly-focused but really attempt to measure how much work the customer had to put into identifying the right point of contact for a service issue, what the process put them through and any additional heavy lifting they had to do to reach a point where they’re satisfied. After all, if your customer service program deals with troubleshooting but does so in a way that’s painful to endure, who’s going to come back, let alone buy from you again?
CES evaluations can be deployed in myriad ways, but the most common has been to ask a question such as “How much effort did you personally have to put forth to handle your request on a 5-point scale from very low effort (1) to very high effort (5)?”
Others might choose to serve up a statement on their site or through their contact centre which says, “XYZ Corp. make it easy for me to handle my issue,” followed by a seven-point scale of “strongly agree” to “strongly disagree.” The evaluation could cover the entirety of a service interaction, or it could be broken up and asked in multiple ways during different stages of a service engagement.
CES has become increasingly attractive to companies of all sizes, but it may be of great interest to small and medium-sized businesses who are competing with larger entities and who need to ensure that they retain the customers they win through each deal.
Of course, the ideal plateau in terms of customer effort is not having to deal with any kind of troubleshooting at all, but that’s probably not realistic. Instead, think about where AI could bring new value (and at least a reduction in customer effort) in the following areas:
The Effort Of Seeking Help: Talk to almost anyone and they can probably tell you a story of looking in vain on a company’s web site for the right phone number, e-mail address or other channel to contact a support agent. AI is able to seamlessly work across social media, e-mail, or route calls more effortlessly to the best possible resource, allowing customers to stay within their preferred method of interaction.
The Effort Of Waiting: It can take a lot of patience (and mental energy) to sit on hold while service agents work through the queue of customers digitally lined up and seeking assistance. Among its many other advantages, AI nips those wait times in the bud, either offering virtual assistance through chatbots or instantly connecting customers to a live person as required.
The Effort Of Explaining: Customers have grown accustomed to needing all their personal information, their purchase details and other data before someone can help them. They’re also familiar with having to share these details more than once during the course of an interaction. It doesn’t mean they like it, and it’s even worse when agents ask for information the customer has forgotten or is not readily available. In contrast, AI is designed to “learn” from the data about each customer and either work directly with them to solve problems or serve up the necessary details to a live agent.
The Effort Of DIY: In the pre-digital days, “self service” might have meant looking at an owner’s manual or watching an instructional video that came along with the product or service. Like many other areas, AI has improved self-service by an order of magnitude. Besides chatbots that can automate many of the most common back-and-forth that goes on between companies and their customers, the technology can dynamically access the most relevant and specific details to help resolve an issue yourself. Customers won’t feel like they’ve made a lot of effort if it’s easy — especially if it teaches them something valuable.
On a bigger scale, AI also reduces customer effort by anticipating what they might need before they need it, and offering content or tools to proactively mitigate pitfalls they could encounter with a product or service.
AI can also be used to learn about what customer effort looks like across a particular geography, size of the account, customers who purchased a specific product or other segments. By educating the company about the products, processes or other things that force customers to spend time and energy they’d rather devote elsewhere, AI can help develop strategies that could directly lower the overall average of your CES scores.
Finally, AI can do an analysis of how CES scores affect the other metrics we’ve discussed in this series, such as Net Promoter Score or CSAT. In some cases, companies might prefer a blend of these metrics, or focus on something more specific, such as customer churn.
The point is to get started, and be among the first to demonstrate how AI can provide a more effortless, more satisfying customer experience — one that turns more of your customers into true promoters to the rest of your target market.
Today, every company faces an imperative to integrate AI into their business in order to succeed. A cutting edge CRM solution can help. Learn more in our ebook, “AI for CRM: Everything You Need to Know.”