Take Actionable Steps Based on...

What Your Customers Are Saying

Yanay uses artificial intelligence to make internal and external sentiment data blind to stereotypes and human bias. Our mission is to deliver fact-based and proactive decision making for happier customers and increased engagement.

SMALL DATA
We use hyper localized insights and find YOUR customers and focus on what they are saying about you.

Our unique technology structures and consolidates all your internal and external data from several sources in real time into one unique, unified and enriched data model.
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Our natural language processing algorithms detect new insights into what customers are saying about their experience with your services, products or organization.

BIG DATA
​We look at the trends and sentiment over time, and compare the best and the worst moments.

We collect, normalize and analyze 50M data points across 50 social platforms as a learning base for our recommendation algorithms.
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Every single profile is analyzed using more than 100 different characteristics (sentiment, localization, images, keywords, surveys, news media, social and professional ranking etc.).

MACHINE LEARNING + ALGORITHMS
​We turn trends and spikes into actionable relevant recommendations

Our recommendation algorithms use internal and external customer created content as a learning base and leverage machine learning to deliver relevant and actionable things you can do to improve and engage happier customers.

Our recommendations address several customer perception issues such as competitive insights, conversions, recruitment, or new product and service development.

WHITE BOX JUSTIFICATION
​We provide evidenced-based justifications relevant to your decision making, for actionable and measurable results.

Every recommendation is justified with qualified and quantified facts across different dimensions - location, sentiment, products and scope - time, frequency, event.

Each interaction you have with recommendations is analyzed to make the solution evolve taking into consideration your feedbacks on recommendation relevance.