Artificial intelligence (AI) in Marketing: what does it mean for the future?

Artificial intelligence (AI) has already had a long history on its way into marketing. Intelligent computers have been part of popular culture and serious research since the 1950s.

Today it is hardly any different. AI is a trend topic and is euphorically acclaimed by some, critically or even apocalyptically seen by others.

There are several areas in which AI has made great progress in recent years. Especially through machine learning the whole field has made a big step forward. On their own, such systems are getting better and better — which is why many marketers already dream of AI colleagues with great skills, especially in combining AI and marketing automation. AI’s most impressive successes in marketing come from chat-bots/ text comprehension, voice control, search engines, image recognition, autonomous driving etc.

More and more companies are offering chat bots on their websites, some of which already use artificial intelligence to interact. These generally answer standard questions from visitors. This seems obvious and logical to implement because a large proportion of the questions asked by users/customers are the same.

Artificial Intelligence in Marketing — Future Potential?

But what do these many impressive advances actually bring us in marketing? What can we expect in the next few years when, as expected, the use of AI in marketing increases?

1. Content Creation

Computers are already writing texts today, and so far it’s not so bad. It writes mainly in those areas where there are many recurring patterns and where one does not need so many different formulations.

For reference, stock market reports, sports reports or weather forecasts. And there are already systems that take care of image selection for articles — especially for content marketing and process automation in marketing, AI offers great potential.

This also applies to social media marketing — here, for instance, AI systems can perfectly adapt content to the expectations and interests of the users depending on the platform. The system learns from the success of the respective contributions what works and thus becomes better and better.

2. Automatic Analysis

Real communication is not a one-way street, so for marketing automation, we also want to understand what feedback comes from the recipient of our messages. Or what is being discussed about us in forums or social media.

This is where a system like Stanford CoreNLP — a natural language software — could help as it identifies sentence components, recognizes their context and also evaluates the “sentiment,” i.e. positive or negative moods expressed in a text.

3. Intelligent Segmentation: AI in marketing automation

It will be really exciting when we can use AI for Marketing Automation to further improve our segmentation. A starting point for this would the analysis of the content coming from the user/customer/lead, and a second starting point were the analysis of user behavior. In other words, what we are already doing in this area without AI.

However, with the existing systems we are only able to proceed according to the rules today. It will be really exciting in the future when the system is self-learning as AI. So when it evaluates, based on the KPIs, which of our actions have brought what. And optimizes future measures accordingly.

4. Smart Personalization

It goes one step further when we have not only segments, but real personalization of content and measures. So the AI Marketing Automation system not only optimizes content based on target groups, but also based on the interests of each individual user or lead.

For example, we could finally put an end to the endless discussions about the optimal time to send e-mails, because every recipient could receive the newsletter exactly when it is best for him personally.

The system would see who responds to what content, who needs to be provided with how detailed information, when, in what order and how.

5. Optimal Connection

Great potential also lies in intelligent connection of systems. On the one hand, this has technical hurdles that we have to overcome. On the other hand, it is only possible for us humans to record a limited amount of data at all.

This is where an artificial intelligence that has an overview of the entire marketing process could do a lot. For example, it could segment & analyze mini target groups: About 18–24 year old sports fans. Or 33–42 year-old IT decision-makers. Who do they follow on Facebook? On LinkedIn? Which posts do they like? Which ones do they share? What do they discuss? From this data, the system can deduce what their exact interests are, what they spend money on and what they respond to; and then display ads that are perfectly tailored to this target group as well as adjust the landing pages, optimise the approach in follow-up e-mails and perfectly time all further communication.

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