For many companies, customer satisfaction (CSAT) scores aren’t just a ‘how’m-I-doin’ metric; they’re an early warning system. When they’re low, it’s a sign that something in the customer experience went wrong, and the company needs to take corrective action. And yet, the most widely-used tool is CSAT surveys. While CSAT surveys can be useful, they have some clear shortcomings:
Surveys occur after a customer conversation or escalation
Response rates for surveys are low (approx. 36%)
Most surveys are filled out by unhappy customers, skewing the results
Survey fatigue is real, impacting volume and quality
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The outcome? By the time you’ve measured CSAT, the damage has been done. And, when you do measure it, the results aren’t necessarily reliable or actionable.
It makes you wonder: how many customer relationships could you salvage if you predicted CSAT earlier?
Artificial intelligence, real-time results
If you’ve ever shown a text conversation to a friend and asked them to decipher its tone, inevitably, the conversation ends with, “It’s hard to tell over text.” The same holds true for customer interactions across your digital customer care channels. But what if AI could make it easier?
The same AI that examines the customer sentiment of millions of conversations on social media can also be used to predict the happiness level of the customer behind the message, known as predictive CSAT.
Predictive CSAT compiles data on customer messages across a number of dimensions, including:
Sentiment: The attitude or opinions of the customer broken down into three basic categories — negative, positive, or neutral.
Intent: What is the intent behind the message that the customer sent? Are they complaining about a broken product? Asking for help? Providing feedback?
Emotion: How is the customer feeling? Are they angry, happy, surprised, disgusted, sad, afraid?
Intensity: How positive or negative is the sentiment? Is this an everyday conversation, or is the customer completely irate?
Time of reply: How long has it taken for the agent to reply to a message? Longer times can impact the CSAT score.
The AI model can then weave these attributes into a predictive CSAT score in real-time. Because agents receive instant performance insight, it creates the opportunity for agents to improve customer satisfaction in the moment or, if needed, escalate to more experienced agents or management.
Sound the alarm, salvage the relationship
So what happens if an interaction is going off the rails? For example, say you have a satisfaction scale of 0 to 100. If a predicted CSAT score drops below a certain threshold, say 30, a supervisor receives an alert. The supervisor can then decide to either coach the agent through the problem or intervene directly.
Purposeful intervention of this type has numerous benefits. Not only do you get a chance to salvage the customer-company relationship, but the agent gets some real-world, one-to-one training. And, because predictive CSAT proactively manages customer care, many cases never reach this level of escalation, which means supervisors only get pulled in when it is essential.
There are a number of other use cases for AI-powered CSAT scores beyond case escalation. For example:
Case prioritization and assignment: Rank incoming cases by importance and urgency, and automatically route sensitive cases to more experienced agents.
Suggestive actions: Set agents up for success with standardized actions that can improve CSAT scores, such as discounts or special offers.
Measuring agent’s performance: Use CSAT as a performance indicator for agents, and intervene early with support and training.
Reducing potential churn: Identify churn risks during customer interactions.
The power of AI is its ability to recognize and identify patterns that feel elusive to the human brain, as well as process a sheer volume of data that humans can’t deal with manually. Predictive CSAT turns that into a brand superpower that’s tied directly to customer happiness.
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