Machine learning already has important functions for marketing. At the moment, however, it is primarily large companies that use the functions internally, such as Google. Self-learning systems are still so new that they cannot simply be purchased as an out-of-the-box solution. Instead, popular internet providers develop their own systems and are therefore the driving force in this sector. However, as some are open source and work with independent research despite commercial interest, developments in the field are progressing even faster.
In addition to its creative side, marketing has always had an analytical aspect: Statistics on customer behavior (purchase behavior, number of visitors to a website, app usage, etc.) play a major role in deciding which specific advertising measures to use. The more data you have, the more information can usually be drawn from it. Intelligent programs are needed to process such a large amount of features. This is where self-learning systems come into play: Computer programs have been taught to recognize patterns and can make well-founded predictions, which is otherwise quite limiting for people who tend to be biased when it comes to data.
An analyst usually approaches measured data with certain expectations. These biases are difficult for people to not have beforehand and can often lead to disappointment with the results. The greater the amount of data an analyst processes, the greater the deviation is likely to be. Although intelligent machines can also be biased.
Self-learning systems also improve and facilitate the way that analysis results are presented: Automated Data Visualization is a technique in which the computer automatically selects the best way of presenting the data and information. This is particularly important so that people can understand what the machine has found out and predicted. With so much data, it becomes difficult to display the results yourself. Therefore, it makes sense for the computer to present the results.
Machine learning can also have an influence on how content is created – the keyword here is generative design. Instead of designing the same customer journey for all users (i.e. the steps the customer takes to purchase a product or service), dynamic systems can create individual experiences based on machine learning. The content displayed to the user on a website is still provided by copywriters and designers, but the system integrates the components specifically for the user. In the meantime, self-learning systems are also being used to design by themselves: with the project Dreamcatcher, it’s possible to have components designed by a machine.
Machine Learning can also be used to improve chatbots, for example. Many companies already use programs that handle part of the customer support using a chatbot. But in many cases, users get quickly annoyed by the automatic operators: The capabilities of current chatbots are usually very limited and the response options are based on databases that are manually maintained. A chatbot based on a self-learning system with good speech recognition (NLP) can give customers the feeling that they are communicating with a real person – and therefore pass the Turing test.
Amazon or Netflix have made another important development when it comes to machine learning for marketers: recommendations. A major factor for the success of these providers is to predict what the user wants next. Depending on the data collected, the self-learning systems can recommend additional products to the user. What was previously only possibly on a large and not-so-personal scale ('Our customers like product A, which means they will like product B'), is now also possible on a small scale thanks to modern programs ('Customer X has liked products A, B, and C, which is why they will probably like product D').
In summary, self-learning systems will influence online marketing in four important ways:
- Quantity: Programs that work with machine learning and have been well trained can process large amounts of data and make predictions for the future. Marketing experts draw conclusions from the success or failure of campaigns this way.
- Speed: Analyses take time – if you have to do them by hand. Self-learning systems increase the working speed and allow you to react more quickly to changes.
- Automation: Machine learning makes it easier to automate operations. Since modern systems can independently adapt to new conditions with the help of machine learning, complex automations processes are also possible.
- Individuality: Computer programs can serve countless customers. Since self-learning systems collect and process data from individual users, they can also provide comprehensive support to these customers. Individual recommendations and specially-developed customer journeys help marketing measures to be more effective.