Marry internal enterprise data with external data
Our patented AI supports real-time personalization and recommendation using a blend of internal and external data
The maya.ai difference
Map customer behavior according to their tastes, affinities, and preferences.
Simplify their choices with relevant recommendations
Tastegraph™
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 result: 70% more effective than the top end offering in the market. At a fraction of the cost
The TasteGraph™ has over 6.5Mn merchants analyzed and mapped, including
hotels
attractions
restaurants
other merchants
That’s not all
maya.ai comes with multiple features that make it a one-of-a-kind revenue acceleration engine
Entities
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
Taste Attributes
maya.ai understands how customers make decisions in each lifestyle category. It identifies the attributes that define tastes in these categories. 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
Affinities
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
TasteMatch
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
Choice AI
The math behind simple choice
Consumer choice relies on four components: taste, influence, context and behavior
Taste
Aggregated preferences of customers, like ratings and reviews
Context
Other factors such as devices, time, location, weather and more
Influence
External social interactions that affect a customer’s decisions, such as ‘likes’ and ‘shares’
Behavior
Online and offline customer activity such as past transactions and interactions