Machine learning: deciphering consumer emotions

5 de December de 2023

Emotions play an essential role in consumers’ purchasing decisions. Brands have long sought ways to understand, anticipate and respond to these emotions to optimise their marketing strategy and customer experience. Thanks to machine learning, companies now have tools at their disposal to decipher this complex emotional puzzle.

Consumer emotions are essential

Consideration of consumer emotions has become critical to business success. This need stems from the recognition that emotions play a crucial role in consumer decision-making.

Therefore, when a company understands and responds appropriately to the emotional states of its customers, it can create more satisfying and memorable experiences, which in turn builds brand loyalty and trust. Furthermore, by focusing on emotions, companies can differentiate themselves from their competitors by offering not only products or services, but also emotional value.

A great example is the Apple brand. This leading company not only focuses on the quality and innovation of its products, but also pays special attention to how these products make its users feel. Apple creates user experiences that go beyond functionality, appealing to emotions such as a sense of belonging, innovation and status.

What is machine learning

Machine learning is a subcategory of artificial intelligence. It allows companies to process and analyse large amounts of data, including reviews, social media comments and surveys, to gain a deeper understanding of consumer opinions and emotions.

One of the key techniques used in sentiment analysis is natural language processing (NLP), which allows machines to read, understand and interpret human language.

  • NLP uses algorithms that can identify patterns and trends in text, such as keywords, tone used or grammatical structure, classifying opinions into categories such as positive, negative or neutral.
  • Machine learning then identifies nuances in user opinions, such as intensity of sentiment or changes in perception over time, and can classify these texts according to their emotional tone. This is especially useful for companies seeking to better understand reactions to their products or advertising campaigns.

One of the advantages of this technology is its ability to continuously adapt and learn. As more data is processed, the system becomes more accurate in its analysis, allowing companies to keep up with consumer opinions.

By applying this technology, companies can:

  • Identify emotional patterns: algorithms can analyse large amounts of data, such as social media comments, product reviews or customer service interactions, to identify patterns that indicate positive, negative or neutral sentiment.
  • Predict emotional responses: With sufficient historical data, models can anticipate how a segment of consumers will react emotionally to an advertising campaign, new product or brand event.
  • Personalise the customer experience: by understanding individual emotions and needs, brands can tailor their strategies to deliver more personalised and meaningful experiences.

Applying machine learning

The application of this technology can be implemented in numerous areas of the business, some of them may be:

  • Marketing and advertising: brands use these models to measure the emotional response to their advertising campaigns, adjusting them to better identify with their audience.
  • Customer experience: real-time analysis of customer feedback allows companies to respond quickly to concerns or problems, improving customer satisfaction and loyalty.
  • Product Development: Understanding consumer emotions helps companies design products that generate a positive emotional response.

To understand in a more practical way how machine learning can help brands understand consumer sentiment, here are 3 cases in which incorporating this technology will considerably improve results.

  • Sentiment analysis: brands use machine learning models to automatically analyse sentiment in social networks or online reviews, being able to know users’ feelings about a brand or product. For example, in 2023, IAB Spain launched a study on user sentiment on social networks towards brands in the food sector in Spain. In it, users’ most loved brands can be found, as well as the most friendly and the most hostile channels.
  • Facial recognition: in physical environments, some brands are experimenting with technologies that read customers’ facial expressions to assess their reactions to products or advertisements in real time.
  • Chatbots and virtual assistants: by understanding the tone and emotions behind customer queries, these bots can provide more empathetic and appropriate responses.

Shall we talk?

At Cognodata we have built CognoIA, an artificial intelligence accelerator platform based on advanced analytics, machine learning and generative AI that allows us to streamline execution and inject our expertise into the projects we undertake. Specifically, we have developed CognoGPT as a module within this platform for the application of capabilities to see, hear, speak, search, understand and accelerate advanced decision making in new use cases. Do you want to know it?