What is AI-based marketing

AI in marketing - three practical examples

Artificial intelligence is not a short-term trend, but will change our private and professional lives significantly. AI is by no means new - in fact, science has been dealing with it since the 1950s. But it is often a long way from innovative research to practical relevance - usually only concrete solutions emerge from them when they become economically relevant; In other words, money can be earned with it and a real consumer benefit emerges from it. This is exactly where we are today in the field of artificial intelligence. But what specific benefits does AI have for marketing?

3 examples: Interaction between data and technology in the age of artificial intelligence

The most interesting field of application of artificial intelligence for brands is machine learning. This gives marketing decision-makers the opportunity to use automated data analysis to identify and develop new sales potential, for example. In times when many brands are in a growth crisis, this is a real advantage. Because marketers these days are almost drowning in data. You have invested large budgets in recent years to set up your own systems such as data management platforms. Unfortunately, the data alone does not provide any knowledge, as a current study by the Wunderman agency group shows: According to this, 62 percent of marketing decision-makers state that they are not able to derive knowledge or specific measures from the data. At the same time, however, 99 percent say that data will determine marketing and sales success.

And this is exactly where artificial intelligence via machine learning comes into play. It's not about having a lot or most of the data. It's about the right data (first party) and about recognizing patterns in them and making them usable for marketing. What this interaction of data and technology looks like in the age of artificial intelligence in marketing is shown by the following examples, which are based on three central methods of AI: pattern recognition, inherence and classification.

Method: pattern recognition. Application: Insights

With the help of pattern recognition, marketing insights on target groups can be generated. The British hotel chain Jurys Inn provides an example of how this looks in practice. She was able to get detailed information about her converting users through a direct measurement of the digital media usage behavior on her website. In addition to demographic data, these also included information on the interests of the target group, which was uncovered using pattern recognition in the online behavior of converting users. This enabled the hotel chain to recognize that there was a connection between a conversion and sports-related issues. A closer look at the data showed that many users were looking for hotels in the vicinity of sports facilities where, for example, Premier League football matches were held. Based on these insights, Jurys Inn created a specific sports site which, in addition to the option to book hotel rooms in the vicinity of sports facilities and stadiums, also contained information about the respective sport and sports events. Thanks to this added value in terms of content and a direct booking option, the booking process has been simplified for users and the number of conversions has increased significantly.

Method: inference. Application: prospecting

A second common AI method used in marketing is inference, also known as logical inference. This method is used in prospecting - the targeted acquisition of new customers.

The delivery service foodora used this method as part of its programmatic advertising campaign. It was important for the company to reach and retain new users in defined growth markets not only in the short term, but also permanently. This is why foodora's marketing team has chosen an innovative marketing approach: In addition to the ongoing display campaign, the company invested additional budget in order to expand its reach in these markets at very short notice and to win new customers. Using the data of the converting users in the respective markets and an AI-based look-alike modeling, the delivery service was able to identify new users who were not part of the company's core target group. As a result, foodora has succeeded in winning over 3.7 million highly relevant new customers in the three markets in just a few days - and retaining them. Even after the short-term campaign boost, an increase in daily conversions of 69 percent was observed in the three countries.

Method: classification. Application: Brand Safety

Classification is another important AI method in digital marketing. It is used for a topic that is important for brands, the brand-safe delivery of digital advertising. In this way, the technology can learn not to deliver advertising material on certain pages. To this end, information and data are provided at the beginning as to which websites should not be used for advertising. On the basis of the available data, the artificial intelligence then assesses whether an environment can be qualified as brand-safe and, if necessary, prevents the delivery of advertising on this website. The classification as not brand-safe can be made, for example, via features such as content on the website. Let's take the example of an airline: They do not want to display advertising in environments that involve plane crashes or delays. Based on the input, the AI ‚Äč‚Äčtechnology is able to understand that it should not only display advertising on certain pages, but also, for example, on an otherwise secure news page on which news about a plane crash is being picked up.

Conclusion

The three use cases show how artificial intelligence is already part of the practice in marketing today. It helps us to analyze data and interpret it meaningfully - at a speed and mass that humans cannot achieve. We have to understand the possibilities of AI and learn to use them. If we succeed in doing this, we will have the opportunity to simplify and accelerate processes and invest more time in more creative and strategic marketing tasks.

https://www.quantcast.com/de/

As Managing Director DACH & Nordics, Volker Helm is responsible for the business of the technology company Quantcast in German-speaking countries and Scandinavia. Before joining Quantcast, he supported Kinetic, the WPP Group's global market leader for out-of-home media, with its digitization and internationalization strategy. Previously, as CEO of Mediaplus, the media division of the Serviceplan Group, he was responsible for internationalization.