The maya.ai difference
Real-time personalization and recommendation
A living, breathing map of the world’s tastes
maya.ai acquires essential data on various lifestyle categories, to create a graph-based entity-affinity model.
This model is then mapped to customer behavior data, to reveal a universe of choices and recommendations.
The TasteGraph™ has over 6.5Mn merchants analyzed and mapped, including
With over 6.5Mn merchants and counting, every digitally discoverable merchant is a target entity for the TasteGraph™. It identifies unique merchants globally and establishes relationships with other relevant merchants.
maya.ai understands how customers make decisions in each lifestyle category. It identifies the attributes that define tastes in these categories. For example, to understand the tastes of customers in dining, maya.ai factors restaurant attributes like quality of service, ambiance, food, cuisine and specials. With cutting-edge machine learning and natural language processing (NLP), maya.ai filters through a combination of structured metadata and unstructured data. It then tags
every merchant with hundreds of such taste attributes and assigns scores to each, to create a taste profile of the merchant.
Based on a merchant’s profile, maya.ai provides an affinity score between any two merchants in each category. maya.ai uses anonymized customer preferences and a patented collaborative method to create a cross-category graph.
All in real-time.
Complement your customers’ tastes with the right merchants
The TasteMatch provides an affinity score between a customer’s taste profile and a given merchant.
The TasteMatch score is used to rank recommendations for customers. It considers context for specific use-cases.
The math behind simple choice
Consumer choice relies on four components: taste, influence, context and behavior.
maya.ai uses these components to understand choice. We call it the Choice Equation.
Aggregated preferences of customers, like ratings and reviews
External social interactions that affect a customer’s decisions, such as ‘likes’ and ‘shares’
Other factors such as devices, time,
location, weather and more
Online and offline customer activity such as past transactions and interactions
Combines multiple recommender systems to deliver the top affinities in any category to every customer.
Is underpinned by an extensive set of dimensions that mimic how consumers make choices.
Iterative machine learning
Evolves constantly in a built-in machine learning loop.