🛒 Harnessing AI to Better Understand Consumers
AI is revolutionizing marketing by enabling personalized, targeted communications through technologies like neural networks, clustering algorithms, and voice and visual recognition.
Today's Highlights
- How AI is changing computer insights
- This Week On BuzzBelow - a recap on this week's topics.
- In Other News - a few interesting developments we're tracking
AI is playing a significant role in changing how marketing works across various industries. The possibility for artificial intelligence to uncover deep consumer insights, which enables brands to provide appealing and targeted communications, is crucial to this transition. Here is how AI is changing consumer insights:
Neural Networks for Personalization
Deep learning, a kind of machine learning, uses neural networks to evaluate consumer data in order to create personalization. Complex pattern recognition is made possible by neural networks, which are based on the interconnected neuron structure of the human brain. Neural networks are capable of identifying complex patterns and assisting in the development of customized marketing messages and advertising campaigns by processing data through several levels of computation. Netflix utilizes deep learning, employing neural networks to analyze customer data and provide personalized recommendations. The analysis spans multiple layers of data including viewing history, ratings, and other interactions, facilitating highly personalized content suggestions to enhance user engagement.
Clustering for Customer Segmentation
Algorithms such as K-Means or Hierarchical clustering are used to fine-tune customer segmentation. These algorithms enable more focused marketing tactics by classifying consumers according to shared demographics or behavioral traits. Through understanding of distinct consumer base segments, marketers can customize their campaigns to effectively appeal to various target audiences. Adobe's Real-Time Customer Data Platform uses clustering algorithms to segment customers based on various behavioral and demographic characteristics. This segmentation enables more targeted marketing campaigns and personalized customer experiences.
Voice and Visual Recognition
Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used by voice and vision recognition systems to process and evaluate audio and visual data. These networks offer a more comprehensive understanding of customer attitudes and preferences by recognizing and interpreting speech patterns, voice tones, and visual clues. A famous example of this is how Google employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in its voice and visual recognition technologies. These technologies power products like Google Images and Google Voice Search, providing rich insights into user preferences and behaviors based on visual and voice data.
In summary, the usage and market of AI for consumer insights is already widespread. A substantial 90% of marketers across 35 countries have automated customer interactions using AI tools. According to a benchmark report, 54% of marketers plan to use chatbots at scale in 2024 for social customer care along with other resources like FAQs and customer forums. With the rate AI is being adopted in marketing, global market revenue for this technology is expected to grow from $27.4 billion in 2023 to $107.4 billion in 2028. As AI technology continues to evolve, the breadth and depth of customer insights will only expand, further propelling the marketing and advertising industry into a new realm of possibilities.
This Week on BuzzBelow
Sukanya Ganesh
Sukanya Ganesh
In Other News
Michael McWhertor
Ayaan Ahmed
Maria Deutscher