Aesthetic Quotient™, or AQ, is a measure of what our psychologists have identified as “fluid and crystallized taste level.” Put simply, an AQ test measures your shopping and style preferences, unique to your lifestyle.
AQ utilizes an AI/ML scoring system developed to quantify an individual's aesthetic preferences. Unlike traditional search and analytics, AQ takes into account a comprehensive range of characteristics, allowing for a more accurate and nuanced evaluation of an individual's aesthetic, or style profile.
AQ Platform for Retailers
Our AQ SAAS platform allows you to harness the power of AQ to tailor your assortment, drive customer engagement, and optimize profitability.
Effectively address all internal aspects related to the company. By doing so, the company can streamline communication and provide quick, efficient solutions to employees' queries.
Enabling productive conversations with customers. Through this initiative, customers can be guided through their customer journey in a personalized manner, resulting in enhanced customer satisfaction and loyalty.
Gain valuable insights into the needs and preferences of both individual and collective customers. This will help the company to optimize its offerings and services, leading to increased customer satisfaction and retention.
Leverage the chatbot tool as a personalized shopping assistant for customers. This approach will not only increase customer satisfaction but also lead to increased conversation and sales for the company.
the steps to BUILD your own AQ DRIVEN MODEL
Input your data set
Deliver a solution
Access your data set
Choose the ideal combination for your use case, featuring user-level recommendations, intelligent user segmentation powered by AQ, personalized rankings, and accurate outfit predictions.
Capture user events and interactions such as views, signups, and purchases. Augment with item and user meta data to enhance advanced AQ modeling.
Acquire a comprehensive understanding of your customers. Gain deep insights into latent characteristics and propensities to inform effective business development strategies.
Fine-tune the recommendation engine
Tailor the model to your unique needs
Maximize performance based on key business metrics and requirements, such as revenue or trend bias.
Select the optimal ML algorithm for your distinctive data and train your model incorporating item discovery and new product cycle launches.
Contextualize recommendations including location, device, user segment, AQ cohort.
Ready to Revolutionize Your Retail Strategy?
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