What it is
An engine that learns from each user's behavior to show them what's most relevant, right when it matters.
What's included
- A behavior-based recommendation model.
- Personalizing products, content and messages.
- Integration into your website, store or product.
- Experimentation and A/B testing.
- Conversion and retention metrics.
How we work: 5-phase methodology
- Phase 1 · Catalog and behavior diagnostic — assessing your product/content catalog and the available user-behavior data.
- Phase 2 · Engine design — defining the recommendation logic (behavior-based, product-similarity-based, or hybrid) for your case.
- Phase 3 · Building and testing — developing the engine and validating it against real data before exposing it to users.
- Phase 4 · Deployment — integrating the engine into your website, app or sales channel, with monitoring of its initial performance.
- Phase 5 · Transfer and optimization — a usage manual and defining how to iterate the engine using data generated after launch.
What we need from your team
- A product or content catalog with its attributes (category, price, relevant features).
- User-behavior data if it exists (purchase history, clicks, time on page).
- Access to the system where the engine will be integrated (e-commerce, app, content platform).
- A definition of what outcome you want to optimize: conversion, average order value, time on site.
Who it's for
- E-commerce and marketplaces.
- Content or SaaS platforms.
- Any product with a large catalog.
Signs you need it
- Every user sees exactly the same catalog or content, regardless of their browsing or purchase history.
- The catalog is so large that many users give up before finding what they're really interested in.
- Email or notification campaigns are generic for the whole base, instead of responding to what each user is looking for.
- Average order value isn't growing because there are no relevant suggestions inviting people to buy or consume more.
Expected outcome
- Higher conversion and average order value.
- More retention and repeat visits.
- An experience that feels tailor-made.
Why jsadsAI
We connect behavioral data, AI and your platform so personalization is real and measurable, not just a generic 'you might also like'.
Frequently asked questions about this service
Do I need a lot of user data for it to work?
History helps, but it's not essential from day one: you can start with product-similarity recommendations and evolve toward behavior-based personalization as data accumulates.
How is it measured whether the engine is working?
With the metric you defined as the target (conversion, average order value, time on site), compared against a baseline with no recommendations.
Does it work the same for e-commerce as for content?
The logic adapts to the case: for e-commerce it usually prioritizes conversion and order value; for content, engagement and time on site.
Does it integrate with my current platform?
Yes, integration with your website, app or sales platform is part of the deployment.
What about the privacy of the behavioral data?
It's processed under the same data-sovereignty rules as the rest of our systems: no unauthorized retention and no use to train public models.
Technology stack
- Sistemas de recomendación
- Personalización
- Eventos de comportamiento
- A/B testing
- Integración web/app