Vindow harnesses machine learning algorithms used for analyzing price patterns and predicting hotel rates trends, ADR, and occupancy forecasting. Vindow accesses relevant data sets that ultimately are utilized to give users enhanced visibility into key market insights. Vindow is the only solution where you can find industry relevant rate trends allowing decision-makers to predict market performance with high levels of accuracy and precision.

Scoring methodology developed to help users select a hotel based on a comprehensive rating generated by combining scores on industry-relevant hotel variables. Each score is generated by applying AI to aggregate reviews using sentiment analysis, topic extraction and natural language process techniques.

Vindow understands that every client is unique and ensures that it will match the right client with the right hotel. Vindow can enhance your hotel search experience by using machine learning techniques within the platform, detecting user preferences, guiding the buyer to the supplier that best fits their criteria.

By using intuitive analytic dashboards, Vindow delivers answers other platforms cannot either because it is too time consuming or simply because they don’t know how. The anlaytics section provides factual information accelerating and improving the selection of hotel options. By reducing the time needed for data preparation through automation and built-in intelligence, Vindow removes subjectivity and human error from the process and allows procurement professionals to take action based on data-driven analytics.

Vindow’s platform provides a centralized, searchable repository with exclusive features such as integrated workflows, e-mail alerts, customizable templates, contract generation, and in-app redlining.

Artificial intelligence is not a feature of Vindow but rather its core foundation. Vindow’s brain is based on two fundamental layers of intelligence that work together to provide a smarter sourcing experience. The first layer identifies patterns in hundreds of millions of travel and economic datasets relevant to different markets. Typical examples of knowledge created by this layer include sentiment or topic analysis or travel reviews or analysis of economic development or crime-safety reports. Using that knowledge as a foundation, the second layer provides actionable insights relevant to specific customers. This layer is able to predict prices on different markets.