Understanding exactly how, when, and where products are being consumed has long been challenging for brands operating in retail spaces. Historically, consumer analysis has involved vast networks of on-the-ground reporters, internet surveys, and broad-brush modeling. 

Computer vision operates in traditional brick-and-mortar spaces, measuring footfall, finding store hotspots, and estimating the number of customers in real-time. 

Amazon’s Just Walk Out technology now enables shops to determine what people pick up from a shop, eradicating the need for point-of-sale devices. Instead, computer vision automatically analyzes what the customer has picked up and bills them.

Consumer behavior analysis and intelligence now extend into social media, where social media listening is used to derive insights from a brand’s products, and their competitor’s products. 

Computer Vision and NLP For Consumer Behavior Analysis

Machine learning has simplified the process of extracting insights from structured and unstructured consumer data. 

Organizations can analyze unstructured images, videos, and text to reveal customer and market behavior and other insights for business intelligence. Social media listening enables brands to better understand their customers, respond to their painpoints and discover market niches for further research and exploration. 

• Computer vision can analyze in-store data while maintaining privacy. 
• NLP models can extract consumer insights from social media 

Data Labeling For Consumer Behavior Analysis

Labeling image, video, and text data for consumer analysis involve retrieving key information from unstructured data and labeling it accordingly. This enables brands to identify key actions and behaviors inside or near their brick-and-mortar stores and their online channels. 

Aya Data helps companies train models to interpret consumer behavior by producing high-quality labeled data sets.

Typical Services: Polygon, Bounding Box, Semantic Segmentation, Key Points

Domain Expertise: FMCG and Retail