When you’re hiring a new member of your customer service team, there are plenty of personality traits that may be important, but perhaps nothing is as vital as their ability to learn quickly. The faster they can understand your products and services and what’s most likely to go awry, the faster they’ll be able to help customers when they reach out.
This is the exact same reason why AI is transforming customer service. Technologies such as Salesforce Einstein, when integrated into Service Cloud, allow the system to learn even more quickly than an agent can, by pulling from diverse sources of data and finding useful insights that can accelerate positive outcomes for customers. Agents can still be a vital part of this process, particularly where a human touch is most needed or appreciated, and AI can allow them to boost their productivity, efficiency and impact.
Still skeptical? That’s okay — most businesses can only get on board with a strategy if they have an effective way to measure their results. In customer service, there are three common ways to do this: customer satisfaction scores, or CSAT, Net Promoter Scores, or Customer Effort Scores.
In the following three-part series of blog posts we’re going to explore how AI could improve the scores across each of these metrics by looking at some of the ways the technology could contribute to the service experience. First up: CSAT.
Usually by the end of an interaction with a contact centre or other touchpoint, a service agent will ask the customer to offer a quick evaluation of how the experience played out.
“How would you rate your experience with us today?” the agent might ask, followed by a five-point ranking of
Your CSAT score is calculated by the total or average of customers who answer the question. Of course, it also requires customers to answer honestly, but the higher the result, the better indicator you’re creating a strong bond with your target audience.
Though CSAT is a tried-and-true approach for many organizations’ service teams, it has its limitations. One problem is that the feedback may lack a certain degree of context, especially when all the responses are added up together. What if the service experience involved a relatively easy product versus a more complex one, where resolving problems takes longer? What if the agent in question was fairly new versus a highly experienced expert? CSAT scores that mix everything together as though all service interactions are the same will never provide the clearest possible picture.
The other main criticism of CSAT is that the score doesn’t necessarily cover the customer’s overall relationship to a brand. They may love how your products and services work, for instance, but not how long it takes the to ship. They may have made one purchase they’re happy with and it will keep them loyal but offer negative CSAT feedback related on an isolated incident involving a different product.
Unlike individual agents who are gathering CSAT scores in a fairly one-one-one fashion, AI has the potential to provide more granularity into how small and medium-sized businesses look at customer satisfaction. These are just some of the possibilities:
Instead of just an overall number, AI means companies can make customer satisfaction more of an ongoing strategy and empower their agents to contribute to the bigger picture.
Of course, as mentioned earlier, CSAT scores are only one way to look at the results of your customer service capabilities. In our next post, we’ll look at Net Promoter Scores, and what AI can do there.
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