Produktdetails werden auf Englisch angezeigt. Übersetzungen folgen in Kürze.
Kurze Fakten
- Preis
- $399.00 USD
- magento_2-Versionen
- —
- Rüstzeit
- 90 Min
- Lizenz
- Einmalig und unbefristet
- Sprachen
- 11 Sprachen
Einmalkauf
AI Product Recommendation Engine
A custom-built Magento 2 / Adobe Commerce extension that tracks behavioral signals and serves personalized cross-sell, up-sell and "frequently bought together" blocks on homepage, PDP and cart — built, installed and supported by ECOSIRE.
$399.00USD
Keine Zahlung jetzt. Dies sendet eine Angebotsanfrage an unser Team – wir melden uns per E-Mail mit Preisen und nächsten Schritten.
Hauptfunktionen
Behavioral signal tracking — product views, dwell time, add-to-cart and purchase events captured via frontend observers and a dedicated REST tracking endpoint, stored in custom tables
Personalized recommendation blocks for homepage, PDP and cart, delivered as themeable layout XML / block widgets placed via admin layout or templates
Frequently-bought-together logic built from real order line-item co-occurrence, recomputed on a Magento cron schedule
Seasonal and trending models that surface rising products by time window, category and store view
A/B testing of placements and strategies with conversion and AOV attribution reporting in the admin
Service contracts (PHP interfaces under api/) and a DI-based scoring engine so strategies are swappable and unit-testable
GraphQL resolvers and REST endpoints exposing recommendations for PWA Studio and headless storefronts
Full-page-cache safe rendering via a private-content / customer-data path so personalization never poisons the FPC
Multi-store-view, multi-website and customer-group aware — respects scope, segment and group pricing
Admin configuration UI under a dedicated ACL resource: placement rules, manual pins, exclusions and per-strategy weighting
Adobe Commerce extras — optional integration with customer segments and B2B company accounts where the Commerce edition is licensed
Cron-driven model rebuilds with logging plus a bin/magento CLI command to force a recompute on demand
Über dieses Produkt
The AI Product Recommendation Engine is a Magento 2 / Adobe Commerce extension that ECOSIRE builds to order for your catalog, then installs and supports on your store. It is not an instant Adobe Commerce Marketplace download — we engineer it around your taxonomy, themes and traffic, deliver it as a versioned module under app/code/Ecosire/ProductRecommendations, and hand over a tested, production-ready build.
Warum AI Product Recommendation Engine wählen
Behavioral signal tracking — product views, dwell time, add-to-cart and purchase events captured via frontend observers and a dedicated REST tracking endpoint, stored in custom tables
Personalized recommendation blocks for homepage, PDP and cart, delivered as themeable layout XML / block widgets placed via admin layout or templates
Frequently-bought-together logic built from real order line-item co-occurrence, recomputed on a Magento cron schedule
Seasonal and trending models that surface rising products by time window, category and store view
A/B testing of placements and strategies with conversion and AOV attribution reporting in the admin
Service contracts (PHP interfaces under api/) and a DI-based scoring engine so strategies are swappable and unit-testable
Behaviorally, the module captures real signals — product views, dwell time, add-to-cart and purchase events — through frontend observers and a lightweight tracking endpoint, persisting them to dedicated tables so recommendations reflect what shoppers actually do rather than static "related products" lists. A scoring service (exposed via a service contract / DI interface) blends collaborative signals, frequently-bought-together affinity, and seasonal/trending models computed on a cron schedule.
Storefront output is delivered as themeable UI components (layout XML blocks + KnockoutJS for cart/checkout) that drop into homepage, PDP and cart, plus GraphQL and REST resolvers so PWA Studio / headless storefronts consume the same recommendations. An admin section under a dedicated ACL lets merchandisers configure placements, pin or exclude products, and run A/B tests of recommendation strategies with conversion and AOV reporting.
Everything respects multi-store-view scope, customer-group pricing and Magento's full-page cache (recommendations render via a private-content/ESI-friendly path so caching stays intact). On Adobe Commerce we can integrate with B2B and customer segments; on Open Source we deliver the equivalent natively. ECOSIRE handles installation, setup:upgrade, theme integration, and post-launch support so the engine ships clean and keeps performing.
Was Sie bekommen
- Custom Magento 2 module (Ecosire_ProductRecommendations) under app/code, versioned and composer-installable, with registration.php, module.xml, di.xml and declarative schema (db_schema.xml)
- Installation and configuration on your environment — setup:upgrade, di:compile, theme block placement and full smoke test on staging then production
- Admin configuration guide plus a short merchandiser walkthrough of placements, pins/exclusions and A/B test setup
- GraphQL/REST API documentation for headless or PWA Studio consumption of the recommendation endpoints
- Source code handover with PHPCS/Magento coding-standard compliance and inline documentation
- Post-launch support window with bug fixes and Magento minor-version compatibility checks, plus a maintenance option thereafter
Für wen das ist
Ecommerce Manager (mid-to-large catalog)
Owns AOV and conversion targets for a 5,000+ SKU store and wants personalization that goes beyond Magento's static related/up-sell lists without ripping out the existing theme.
Magento Technical Lead / Solution Architect
Needs a clean, service-contract-based module that respects FPC, store-view scope and customer groups, exposes GraphQL for the PWA front end, and won't become unmaintainable bespoke spaghetti.
Merchandiser / CRO Specialist
Wants admin control to pin hero products, exclude clearance items, and A/B test recommendation strategies with real conversion and AOV reporting rather than guessing.
Installationsübersicht
Von ECOSIRE herunterladen
Kaufen Sie die Lizenz auf ecosire.com und laden Sie die ZIP-Datei des AI Product Recommendation Engine-Moduls von Ihrem Konto-Dashboard herunter.
Auf Odoo hochladen
Extrahieren Sie die ZIP-Datei in Ihren Odoo-Ordner für benutzerdefinierte Add-ons auf dem Server (oder laden Sie sie über „Apps“ > „Aus Datei installieren“ auf Odoo.sh/Runbot hoch).
Installieren Sie das Modul
Aktivieren Sie den Entwicklermodus, öffnen Sie „Apps“, klicken Sie auf „Apps-Liste aktualisieren“, suchen Sie nach „AI Product Recommendation Engine“ und klicken Sie auf „Installieren“.
Einstellungen konfigurieren
Öffnen Sie das neue Menü, fügen Sie Ihren ECOSIRE-Lizenzschlüssel ein, verbinden Sie alle externen Anmeldeinformationen (Shopify, Amazon, Stripe usw.) und speichern Sie.
Verifizieren und live gehen
Führen Sie den integrierten Verbindungstest aus, synchronisieren Sie Ihre ersten 10 Datensätze und planen Sie den wiederkehrenden Cron. Wenden Sie sich an den Support, wenn etwas fehlschlägt.
Wie AI Product Recommendation Engine im Vergleich abschneidet
| Kriterium | ECOSIRE | Benutzerdefinierter Build | Konkurrent | Odoo Native |
|---|---|---|---|---|
| Personalized recommendations beyond static related/up-sell lists | ||||
| Behavioral signal tracking (views, dwell, cart, purchase) | ||||
| A/B testing of placements with AOV/conversion reporting | ||||
| Built specifically around your catalog, theme and store-view scope | ||||
| Full-page-cache safe, FPC-compatible rendering | ||||
| GraphQL / REST for headless / PWA Studio | ||||
| Installed, tested and supported by the builder | ||||
| Source code handover with coding-standard compliance |
Häufig gestellte Fragen zu AI Product Recommendation Engine
+Is this an instant download from the Adobe Commerce Marketplace?
No. This is a build-to-order extension. After purchase ECOSIRE engineers the module around your catalog, themes and traffic, then installs and configures it on your store. You receive a versioned, tested module under app/code — not a generic Marketplace package.
+How long until it's live on my store?
Typical delivery is about 2 to 4 weeks from kickoff, depending on catalog size, theme complexity, headless vs. Luma, and how many placements and A/B variants you need. We scope an exact lead time after a short discovery call and build/test on staging before any production deploy.
+What ongoing support and updates do I get?
The build includes a post-launch support window covering bug fixes and Magento minor-version compatibility checks. After that you can take an optional maintenance plan for upgrade compatibility (new Magento / Adobe Commerce releases), model tuning and enhancements. We don't push silent auto-updates — changes are reviewed and deployed with you.
+Will it slow down my store or break full-page cache?
No. Recommendations render through a private-content / customer-data path so the full-page cache stays intact, and heavy model computation runs on cron rather than per-request. We profile the integration on staging and keep storefront rendering lightweight.
+Does it work with a headless / PWA Studio storefront?
Yes. The module exposes GraphQL resolvers and REST endpoints so PWA Studio or any composable front end consumes the same recommendation logic as the Luma storefront. We document the schema and endpoints as part of the handover.
Warum Kunden ECOSIRE vertrauen
SOC 2 ausgerichtet
DSGVO-konform
Sichere Stripe-Zahlung
30 Tage Geld-zurück-Garantie
Lebenslange Updates
LGPL-3.0-Lizenz
Verwandte Module
Angebot anfordern
AI Product Recommendation Engine
A custom-built Magento 2 / Adobe Commerce extension that tracks behavioral signals and serves personalized cross-sell, up-sell and "frequently bought together" blocks on homepage, PDP and cart — built, installed and supported by ECOSIRE.
- Behavioral signal tracking — product views, dwell time, add-to-cart and purchase events captured via frontend observers and a dedicated REST tracking endpoint, stored in custom tables
- Personalized recommendation blocks for homepage, PDP and cart, delivered as themeable layout XML / block widgets placed via admin layout or templates
- Frequently-bought-together logic built from real order line-item co-occurrence, recomputed on a Magento cron schedule
- Seasonal and trending models that surface rising products by time window, category and store view