The Travel Edge

Make personalization truly personal with the Intelligent Edge.

Delight your users with real-time personalized recommendations and rankings on homepage.

Grab your travelers' attention with real-time personalized recommendations based on their preferences revealed by clicks, scrolling patterns, time of day, and search history, while taking care of dynamic feature values important to the traveler, such as price, potentially increasing bookings by up to 8%.

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travel description

Help them decide with customized Search Results.

Leverage real-time engagement data to show tailored search results to your users based on session behavior and preferences, including clicks on destination guides, reviews of hotels, searches for flights, and exploration of local attractions - with less than 30 millisecond latency and autocomplete search at lightning speed.

Reduce app-switching with hyper-personalized 'See Similar Properties' suggestions

The opportunity cost of users leaving the travelling app is one of the highest for the travel industry due to the high perishability of their services. With hyper-personalized session-awareness, use short term search history and real-time in app behavior to rank and show similar properties as per the user’s intent.

Use Case
Challenges
Solution
Use Case

Homepage recommendations
Search recommendations
Thumbnail personalization

challenges
Challenges


- Batch processing is not able to keep pace with the personalization required by real-time expectations of the user.
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- Implementing near real-time ML in the cloud is 5X more costly than deploying it at the edge.

- Cloud-based near real-time ML struggles to scale efficiently due to unpredictable traffic spikes.

- Delay in quick processing of suggestions due to high latency.

Solution
NimbleEdge Platform

Our platform runs real-time ML Inference and training on-device, resulting in improved performance of ‘Recommendations’ and ‘Re-ranking’ models at 20% of the cost needed to run them in real-time on the cloud.This boosts property recommendations according to traveller’s preferences during the session, eventually increasing the lifetime value of the user while reducing operational complexity and latency to less than 30 ms.

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NimbleEdge’s Platform provides both orchestration and execution capabilities for Hyper-Personalization.

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Edge pipelines with
on-the-fly updates
Cloud-to-Edge
Orchestration
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Fully-Managed
PaaS
Learn About the NimbleEdge Platform
Use Cases

Leverage the Intelligent Edge for Your Industry

Fintech

Betterment in transaction success rate through hyper-personalized fraud detection
Fintech
Fraud detection models that try to flag fraudulent transactions (applies to all the FinTech apps)
Speed & Reliability issues with transactions in non-real time ML systems on the cloud limit personalization levels, as it operates with Huge Costs of running Real-Time ML systems on the Cloud
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E-Commerce

Increase in models’ performances lead to a rise in Conversion carts with higher order size
E-Commerce
Search & Display recommendation models for product discovery for new and repeat orders Personalized offers and pricing
The non Real-time/Batch ML processing doesn't serve highly fluctuating or impulsive customer interests. Organizations need real-time ML systems but it is impossible to implement and scale them on the cloud with even five times the average cloud cost.
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Gaming

See uplifts in game retention metrics like gaming duration, completion, game cross-sells and LTV
Gaming
Contest SelectionMatchmaking and Ranking Cross-contests recommendationPersonalized offers and pricing
As a result of cloud’s limited infrastructure in providing scalability with respect to ML model deployments and processing in real-time, gaming apps adopt non real-time/batch processing that negatively affects click-through rates, game duration, completion, cross-sells, and lifetime value of players.
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Healthcare

Savings in the privacy budget with privacy preserving encryption algorithms
Healthcare
Personalized Search recommendations (Exercises, Nutrition, Services, Products)
User engagement metrics, customer acquisition and retention, NPS, and other business app metrics suffer. On-device/Edge processing can be a great solution but the data processing capacity is inherently limited due to resource constraints of edge devices.
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Fintech
Gaming
E-Commerce
Healthcare

Want to
learn more?

Get in touch to learn more about real-time personalization in mobile apps using on-device ML with NimbleEdge
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