In theory, a business’s top concern is their customer’s happiness. After all, a happy customer means a loyal customer, and a customer who is unsatisfied is generally bad for business. Therefore, businesses today should do well to track their customer’s satisfaction, and then attempt to make their customers even happier. This is easier said than done, as happiness and its metrics are more complex than meets the eye.
Happiness can be understood both quantitatively and qualitatively, yet several businesses either view it under a limited scope or do not consider it at all. Several businesses, with the intent of “interacting” with the customer, may send out surveys for customers to fill out about their satisfaction. While these methods of tracking satisfaction may tell a business if customers are happy or not, it does not explain the real reason behind a customer’s emotions.
Customer satisfaction is dependent on several factors that exist completely outside the realm of your product, such as politics and mental health. For example, a customer may be dissatisfied with a product not because there is anything wrong with it, but because the business supports a politician or movement that the customer disagrees with politically. This would make the customer unhappy, but a survey about a product would not be able to account for this.
Additionally, classifying satisfaction by emotion misses a key ingredient in the formula of detecting happiness: human behavior.
Additionally, humans become unintentionally biased when asked about their emotions. By narrowing a range of emotions to a few check-boxes, surveys prime the human brain to start thinking about their emotions in a certain way, making the response unreliable. Emotions exist subconsciously and passively, and are vulnerable to sudden change.
How to measure happiness
Machines, on a commercial scale, don’t have the ability to pick up on a sad face, a heaving sigh or lethargic walk to signal discontentment. Instead, we look to the digital traces of emotion customers leave behind across a business’s interaction interfaces.
ecosystem.Ai’s behavioral algorithms measure happiness not by tracking inward-facing emotion, which is hidden and reliant on self-reporting, but by analysing outwardly visible behavior. Our sociological and psychological backing means we consider aspects like the importance of relationships in human contentment, as well as periodic novelty for dopamine boosts. For example, our sentimental equilibrium algorithm, would pick up on customer dormancy and introduce new content to entice the human craving for novelty. Tracking responses, the algorithm can pick up when the engagement is positive or negative, adapting as needed and stopping before engagement becomes invasive.
The answer lies in combining social sciences and machine learning, where both quantitative and qualitative aspects of happiness are considered. At ecosystem.Ai, we believe that customer happiness can be found in user data. This data can be mapped onto and associated with a personality, which can give insight into how different customers experience and express various types of emotion.
By using real-time feedback and engagement to actively track patterns of behavior in order to deduce satisfaction and dissatisfaction in-the-moment, businesses can actively measure and respond to emotion.
Happiness is key for a business’s success, as it is impossible in the current age for a business to thrive with unsatisfied customers. Despite the importance of happiness, it is inherently misunderstood among businesses as a simple quantitative emotion instead of a complex human behavior. However, if a business were to apply a sociological approach to happiness in conjunction with machine learning, they would be able to accurately track customer satisfaction in real time. By being able to accurately measure and analyze customer emotions, businesses can truly put their customer’s happiness first.
