Technology

How Artificial Intelligence Changes Marketing

Many companies have already recognized that integrating artificial intelligence into the value chain has great potential. According to a study on artificial intelligence by the technology market research institute Vanson Bourne on behalf of Teradata, 80% of the companies are already investing in Artificial Intelligence and hope, among other things, for increased sales and competitive advantages.

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In addition to the areas of research, development or production, the use of intelligent systems offers great advantages, especially in marketing. Marketing managers today have access to large amounts of data that can not be adequately interpreted without the help of computers. This is one of the reasons why Artificial Intelligence is being used more and more often because it enables an analysis of arbitrarily large amounts of data. This can be very helpful for marketers in planning and implementing effective marketing activities.

Technological background

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Artificial intelligence
Artificial intelligence (AI) is a branch of computer science that deals with simulating intelligent human behavior with computer programs. The AI, in turn, are subordinate to various sub-areas such as neural networks or natural language processing.

Machine Learning
Machine Learning is also a branch of AI. We humans learn from experiences. Machine learning describes a state in which this human learning behavior of programs is simulated by algorithms. Algorithms can recognize patterns in records, make predictions, or classify data.

Supervised and Unsupervised Machine Learning
Within Machine Learning, there are two basic techniques:

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  • Supervised Learning: An algorithm is trained to make plausible predictions for new input datasets based on known input and output datasets. For example, classification techniques can be used to classify input data into categories, or regression techniques to forecast continuous outputs.
  • Unsupervised Learning: Algorithms automatically recognize hidden patterns and structures in input datasets and derive results from them.

Use Cases of Machine Learning in Customer Communication

1. Intelligent Recommendation Engines
Interested parties and customers leave data when browsing online shops. On the basis of this data, machine learning algorithms can conclude on the preferences of the respective visitor and propose suitable offers and recommendations for the individual visitor. The use of intelligent recommendation engines enables online retailers to exploit cross-selling and up-selling potential and increase their sales.

2. Customer churn forecast
Machine Learning makes it possible to predict which customers are likely to leave in the near future. Here, algorithms are able to recognize features that already match the data of the customers who have already emigrated. If these characteristics are applied to the current customer base, it is possible to filter out the customers currently at risk of emigration. In this way, these customers can be specifically addressed – tied with the help of individually tailored incentives.

3. Sentiment analysis
So-called sentiment analyzers are instruments of social media monitoring. Algorithms use this method to automatically evaluate posts such as comments, postings or reviews on their tonality. By comparing the contributions with a database, opinions about products, services or brands can be assigned to one of the categories positive, negative or irrelevant. This allows companies to take specific measures to influence the mood accordingly.

4. Chatbots
With the launch of a chatbot platform for Facebook in April 2016, the hype about so-called intelligent dialog systems began. These are cloud-based robots that simulate human communication. Originally, messaging services such as Facebook Messenger or WhatsApp were used for private-sector communication, such as friends and family. Meanwhile, more and more companies use these services and integrate their chatbots. This allows companies to get closer to the customer and strengthen customer loyalty. Many companies are already using this technology in customer service in order to be able to respond to customer inquiries in large numbers and around the clock in a personalized way.

Conclusion

In addition to the automated processing of routine tasks, such as in customer service, AI and especially machine learning enable the acquisition of new knowledge about the preferences, the buying behavior or the opinion of prospects and customers. This knowledge allows more effective planning and implementation of marketing strategies and measures. The application examples show that the use of AI already proven in some areas of application and in the future, a modern marketing can hardly do without intelligent, self-learning programs.

About the author

Vikram

Vikram is a Digital Media Strategy Consultant who helps small business owners grow their
business. He is passionate about blogging, digital marketing, and emerging technologies.

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