AI x CX = CSAT
Albert Einstein’s mathematical formula of mass–energy equivalence is the world’s most famous equation. It is a formula discovered by man, which proves our incredible potential and capacity for intelligence. Many formulas have been discovered since and all have helped inform and shape our efforts to create and iterate a version of ‘the next big thing.’
Formulas cannot be patented, but effective ones catch on. One elegant equation that may prove to be a game changer and the next big thing in technology is AI x CX = CSAT (Artificial Intelligence x Customer Experience = Customer Satisfaction), or the inclusion and deployment of AI in the delivery of customer experience solutions to achieve customer satisfaction. AI and machine learning can leverage CSAT by providing us with the tools and techniques to identify problem areas, analyse consumer feedback, and personalise the customer experience. Satisfaction ratings are an important measure of success. A satisfied customer (internally and externally) is a happy customer and the values associated with what makes a customer happy to return are often qualitative.
Qualitative measurements of success are easier to measure in smaller businesses since most of these businesses can depend on surface-level observations and make quick changes on the fly i.e. the food at a restaurant whether cooked or undercooked can be changed before a plate goes out; a long queue at a grocery checkout can be resolved by having more cashiers occupy checkout aisles; or a handyman applying a second coat of paint to walls that are still muted. These are quick fixes that do not require tabulation of customer satisfaction scores. The same cannot be said however for bigger businesses where metrics are needed by management to receive an overall view, and a review, of how well their products or support teams are meeting customer satisfaction.
There are several ways AI can manage qualitative measurements of success in small and medium-sized, or large enterprises. Here are some of the ways AI is used to boost CX to arrive at CSAT:
1. Chatbot Customer Support: Being placed on hold is instant dissatisfaction. AI-powered chatbots can provide ongoing support and can be deployed 24/7 to capture engagement and opportunities. CSAT is instantaneous when a customer looking for a solution receives help without picking up a phone or launching the product’s prescribed desktop communicator.
2. Bespoke Recommendations: A general recommendation is appreciated, but a tailored one is preferred. A white-glove experience can come from AI since it uses machine learning to detect patterns in our repeated consumer behaviours. It can even pull from algorithms to personalise product recommendations for returning customers. Customers who are looked after are more inclined to return to a service that knows who they are and what they want.
3. Sentiment Analysis: AI is not fully sentient, but it works well enough to discern emotional cues from our written or verbal communication. AI can analyse text or voice from customer comments or reviews to determine insights into business areas that can be improved.
4. Real-time Analytics: The same technology that brought speech to text is now improved with AI and can track communication in real time. Language and speech patterns are components that can be used by AI to create ‘predictive text’ whereby future consumer needs are predicted and recorded. These predictions, whilst not perfect, are good benchmarks that anticipate future needs, and when needs are anticipated and met, satisfaction follows.
Implementing AI in any customer service environment brings many benefits, but there are important caveats to remember. One challenge is balancing the participation of AI in industries that are best served by humans. AI-driven interactions are efficient but can lack that distinct ‘human touch’ and AI may sometimes skimp on the empathy required to create a satisfying and memorable customer experience.
Another challenge is making AI sound natural instead of artificial. Human language is fluid, and we have a unique combination of words that AI’s artificial filter can miss. This is most evident in the use of ChatGPT in thought pieces and articles. AI-written texts are obvious because they often return numbered results to a query, as seen in listicles that would use numbered items when explaining discussion points. This can be remedied by properly training the AI to ensure its responses are appropriate to context and do not sound like a search result.
Additionally, AI implementation within a customer service framework can be costly. Specialised software can take months to train, develop & implement, and will require ongoing maintenance whenever new updates are introduced to keep the system software robust.
There is also the obligation to ethics. AI has the power to manipulate customers into making abrupt decisions or make decisions to their disadvantage. This is a point of contention that will be a topic of discussion in talks concerning AI’s use in Customer Service.
AI x CX = CSAT may be an intimidating formula, but its applications have seen much success in the customer experience landscape. The formula is already being applied in chatbots, and in 24/7 self-service kiosks that remain online during hours that are inconvenient for humans to occupy.
As of this writing, AI is not without its flaws, but its positives outweigh its negatives. Innovation and iteration go hand in hand, and the disadvantages of AI may no longer exist by the time you read this article. AI x CX = CSAT may not be the formula Albert wanted us to continue and lead with, but it is catching on to become ‘the next big thing.’ If only Einstein could see us now.
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