The world of Customer Experience has evolved tremendously in recent years. In the past, creating a journey map was the holy grail for gaining insight into what matters to customers and the journey they take to reach their goal.Today, there are all kinds of alternatives available for making customer experience tangible for organizations, so that they can respond to it and improve their services.
The customer journey is becoming increasingly digital. As customer contact volumes are rapidly increasing across many channels, innovation with Artificial Intelligence (AI) is increasingly being used to provide more efficient methods of delivering an optimal experience.
Supervised Machine Learning
Although much is written about AI, 90% of the publications don’t really have that much to do with Artificial Intelligence. One of the key principles of AI is that the algorithm must be self-learning. And this is crucial, because the majority of current technologies do not learn from new data, but rather repeat the tricks they already know.
In order to update the algorithm with new knowledge, it is re-trained by manually annotating the new data with the correct result and offering this to the machine learning algorithm. In text mining, this principle is widely used for, for example, sentiment detection and topic recognition, where a person regularly provides a dataset with the right sentiments and topics and offers this to the algorithm for further learning.
Unsupervised Machine Learning
In true AI applications, the algorithm learns by itself based on incoming data. Humans only intervene to inform the algorithm of its intended purpose or errors made. Suppose you want an AI algorithm to recognize pears in photos. You do this by offering the algorithm being developed lots and lots of photos of pears. The machine then sets to work to create an algorithm that recognizes pears. When this algorithm mistakes an apple for a pear, you point out its errors. The algorithm then continues to learn and will, over time, recognize more than 98% of all pears in any photo.
Artificial Intelligence applications within CX
The emergence of Artificial Intelligence in the world of CX has opened up new opportunities – just think of the real-time optimization of touchpoints through personalized and automated communication on all digital channels (e.g. chatbots, DMPs, and journey orchestration).
Artificial Intelligence also makes it possible to understand customers ever better. Emotion Analytics (EA) is an AI application that collects and analyses data about how a person communicates verbally and non-verbally so as to understand their state of mind or attitude. Underlying techniques, such as conversational AI, process mining, and NLP (Natural Language Processing) are rapidly improving in this respect.
Communication signals such as open texts, speech, and spoken language are important tools for measuring and modelling human behaviour. When it comes to speech, it is not only important what someone says, but also how they say it. An element of that ‘how’ also touches upon emotions: was a customer angry, happy, or neutral? And how does this affect customer behaviour?
Although it is still in the experimental phase, AI technology is increasingly able to outperform humans when it comes to understanding and predicting human emotions. Huge amounts of data are being analysed and information is being tapped into that humans are unable to perceive, such as biometrics, brain waves, and minuscule indicators in body language and facial expressions. All of this will help us to understand the customer better and better, which will improve the customer experience.