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Recommendation engines

can help marketers and organizations increase the likelihood of arriving at recommendations tailored to a user’s past online activity or behavior using in-depth knowledge based on big data analysis.


is a powerful product recommendations engine that uses machine-learning to select the most relevant, data-driven personalized product recommendations for each customer interaction.

Homa uses machine learning to understand and predict human behavior in a helpful way. recommendation engine is used in real-time to deliver highly relevant suggestion to micro-segments of consumers.

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User behavior analytics

Gleaning and analyzing user behavior patterns from “high-information users” (highly engaged users), such as the number of times the user clicks the picture of a particular make of a vehicle, the amount of time the user spends on the product information page, etc.

Web Crawling

Graphbeen will can crawl websites while actively looking for a set of keywords that would trigger a specified action. This keywords can determine the category, specs, facts, tags or etc.

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web crawling setup can do the job if it’s programmed to fetch content from a website that has a set of keywords which will be analyzed and matched in tree of knowledge


Machine Learning Based Elimination of the guesswork of product recommendations
Real-time Recommendations Relevant recommendations since the consumer will still be in the market for the product or service
Continuous Machine Learning A smarter system that updates and improves recommendations increasingly over time
Scalability to millions of products and consumers Increased efficiency of marketing campaigns and cost savings