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Fact Combiner – algorithm-based sales booster
MongoDB
Java
JavaScript
Node.js
Glassfish
MongoDB
Java
JavaScript
Node.js
Glassfish
Let’s see our results
of the users choose a recommended search
Key features
of mobile app for Itaka
Recommending products to users
The tool automatically recommends products to users thanks to the Machine Learning mechanism, which learns from users’ behaviors, predicts future customer actions, and chooses the products of their preferences. How does it work? If enough data is collected, the association rule gets activated, and the algorithm compares the frequently browsed products and places similar offers next to each other. If the mechanism knows nothing about the user yet, it recommends the TOP options. The client can view the results as a “recommended offers” bar on the main product page. The system generates recommendations for nearly 60k users in real time.
Improving marketing campaigns
The algorithm displays the suggestions for the client and gathers data for automation marketing purposes. What kind of data does it collect? Everything that may matter in the marketing context: how many people were looking at the particular trip or their geolocation data, the frequency of entries, the browsed and booked offers, and many more. Based on this, the algorithm creates a marketing profile of the client that can be later used for optimizing marketing campaigns. The client can also access statistics anytime they need it to analyze them.
Results
Next steps
Fact Combiner is dedicated not only to the travel industry but can be easily adapted to any business where online consumer decisions are being made. What’s essential Fact Combiner includes offline bookings, too, and can analyze data and generate real-time stats and reports. So, we expect it to grow in the upcoming years, and we will keep implementing the solution for the next clients according to their needs and business models.