From ATL to predictive marketing
Marco Wyler, Director at gateB, started his presentation with a short history of marketing, which showed how the efficiency of marketing measures is continuously increased by technological innovations. Traditional mass communication was replaced by customer segmentation and target group marketing. Similarly, trigger-based marketing used behavioral patterns and was able to react in real time to relevant events. Today, we are in the predictive marketing phase. By using real-time data and including all channels and customer journeys, more and more accurate forecasts can be created, which enable early (re-)action and offer customers personalized support.
From conventional programming to machine learning
In the past, computers were fed data and a program and then spat out a result. Today, in machine learning, they are fed data and results and develop the appropriate program on their own, virtually self-learning. Alternately, they can make appropriate optimizations to an existing program.
Thanks to machine learning, the automation of customer journeys is progressing rapidly and their design is becoming more and more sophisticated. The traditional customer lifecycle with its various successive phases is giving way to micro-journeys, which enable much more agile and precise customer communication.