Analyzing user preferences and behavior with AI
The analysis of user preferences and behavior helps companies to optimize their products and services. On the one hand, companies can collect data about their customers using the analytics tools on their website or social media channels; on the other hand, various market research tools, such as interviews, can be used to learn more about users. In order to evaluate the growing volumes of data, companies have to make ever greater efforts. We will show you how artificial intelligence can help to collect and evaluate the data.
To collect the collected data, companies can first use data mining approaches. Data mining describes a model that analyzes data and can also evaluate huge amounts of data (such as Big Data). Data mining uses algorithms from statistics and artificial intelligence methods to automatically find patterns, trends or correlations in data sets.
Depending on the type of data, it may still need to be analyzed by Natural Language Processing (NLP) techniques. Natural Language Processing captures natural language and processes it, thanks to certain rules and algorithms. The semantics and grammatical structures of the language are examined.
Evaluate interviews automatically
For the evaluation of interviews already conducted, Speech Recognition helps companies. Automatic speech recognition extracts the necessary information. For the evaluation of ratings or reviews, methods such as Sentence Embeddings should be chosen. With the help of this method, entire sentences and their semantic information can be displayed as vectors.
Finally, companies can evaluate and classify the analyzed data using the Sentiment Analysis method. One advantage: Sentiment Analysis not only recognizes the technological relevance of the assigned data, but can also evaluate the attitudes of specific groups of people. Sentiment analysis is to be located as a subarea of Text Mining. In this analysis, texts are evaluated with the aim of classifying statements in the texts as positive or negative.
This will benefit companies
With the help of evaluated customer data, valid statements can be made about the preferences and behaviors of the users. Based on this, customers can be assigned to clearly definable user groups. Customer analysis also helps to optimize products and services. In this way, the offer for existing customers can be individualized and over-engineering can be prevented. The collected customer feedback also helps in the further development of the product range, including the integration of customers in the formulation of product requirements.