On-device machine learning for real-time
personalization in Gaming

Delight users and boost engagement with real-time personalization

Players today have unlimited contests and sports to choose from, leading to choice paralysis. Re-ranking the choices based on the player's interests reduces the time to discover and delight, leading to revenue uplifts of ~2%.

gaming buildings

Entice users to stay with a personalized leaderboard.

Lags in reflecting global ranks and scores can lead to user churn as it delays the excitement of the users. NimbleEdge can process this in less than 25 milliseconds at 20% of the cost, increasing the session durations and the average transactional value amount by 1%.

Increase revenue with customized cross-selling.

Leverage real-time app engagement data combined with long term user features to cross-sell games/contests and increase lifetime value of players with model improvement of about 5%

Use Case

Contest Selection
Matchmaking and Ranking
Cross-Contests Recommendation
Personalized Leaderboard

gaming challenge

Gaming apps need real-time processing to serve fluctuating players' needs, but cloud is limited in its infrastructure. Furthermore, deploying real-time ML on cloud introduces operational complexity and ends up incurring 5x the operational costs. These limitations also introduce high latencies in the gaming experience, leading to app-switching behaviors from gamers who have short attention spans and little patience.

NimbleEdge Platform

Our platform runs real-time ML Inference & 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 contest selection and contest cross-sells with personalized pricings, eventually increasing the lifetime value of the user while reducing operational complexity and latency to less than 25 ms.

key takeaways

Processing results with granular data, all done in real-time.

20ms - 15-25x better app response times lead to uplifts in CTRs and Session duration. Gamers will stay longer and play more


10-20% improvement in the model’s performances lead to

Uplifts in game retention metrics like gaming duration and completion, game cross-sells and LTV. Huge Topline Growth with better projectability

Money cart

33-80% - massive cloud cost savings from the existing ML infra - significant bottom-line savings


NimbleEdge Platform manages both orchestration and execution capabilities for on-device ML

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Edge pipelines with
on-the-fly updates
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Use Cases

Leverage the Intelligent Edge for Your Industry


Betterment in transaction success rate through hyper-personalized fraud detection
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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Increase in models’ performances lead to a rise in Conversion carts with higher order size
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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Travel & Stay

Increase in average booking value with new and repeat customers with higher NPS & savings in cost of acquisition
Travel & Stay
Search/Service recommendation models  + Personalized offers and pricing
NimbleEdge’s HOME runs real-time ML - Inference & Training - on-device, ensuring performance uplifts in Search/Service recommendation and Personalized offers/pricing models at 1/5th of the cost to run them on the cloud.
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Get in touch to unlock real-time personalization using on-device ML with NimbleEdge
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