One unified platform for behavioral data capture, predictive modeling, and real-time recommendation delivery across every customer touchpoint.
The Madewithinter behavioral data engine captures every meaningful interaction a shopper has with your store — page views, hover events, search queries, category navigation, add-to-cart actions, and purchase completions. Unlike legacy analytics platforms that batch-process data overnight, our engine operates in real time, building a continuously updated shopper profile with millisecond latency.
Each behavioral event is enriched with contextual metadata: device type, session duration, referral source, time of day, and geographic region. These signals are fed into our intent scoring model, which assigns a probability score to each product category and SKU for every active session. The result is a live, dynamic picture of what each shopper wants right now — not what they bought six months ago.
Our SDK is available as a lightweight JavaScript snippet (under 8KB) for web storefronts, and as native SDKs for iOS and Android mobile apps. Data is processed in our SOC 2 Type II certified infrastructure with full GDPR and CCPA compliance built in.
Behavioral events are processed and acted upon in under 100 milliseconds, enabling true real-time personalization without page latency.
Cross-device identity resolution stitches together anonymous sessions and known customers into a single behavioral profile per shopper.
First-party data only. No third-party tracking pixels. GDPR Article 6 compliant consent management built into the SDK.
The Madewithinter recommendation engine is a multi-model ensemble system that combines collaborative filtering, content-based similarity, and real-time behavioral signals to generate product recommendations that are genuinely relevant to each individual shopper. Unlike rule-based systems that require manual curation, our engine learns and adapts automatically from purchase patterns and behavioral data.
Recommendations are deployable across every customer touchpoint: homepage hero carousels, product detail page cross-sells and upsells, cart page bundles, post-purchase email sequences, and abandoned cart recovery campaigns. Each placement has its own optimization objective — clicks, add-to-cart, or purchase — and the system continuously A/B tests variants to maximize the metric that matters most to your business.
Retailers can customize recommendation logic through our no-code rules engine: boost new arrivals, exclude out-of-stock items, apply margin thresholds, and surface seasonal collections — all without touching a line of code. Advanced users can access the full API to build custom recommendation surfaces in any frontend framework.
Deploy recommendations across web, mobile app, email, and push notifications from a single API with consistent product data and pricing.
Merchandising teams can override, boost, and filter recommendations using an intuitive drag-and-drop interface — no engineering required.
Product catalog changes — new SKUs, price updates, inventory levels — are reflected in recommendations within seconds via our webhook-based sync system.
Madewithinter's predictive analytics suite transforms raw behavioral data into actionable foresight. Our models forecast purchase intent, churn probability, lifetime value, and next-category affinity for every shopper in your audience — enabling proactive personalization strategies that drive revenue before it would otherwise be lost.
The conversion optimization module includes a built-in A/B and multivariate testing framework that allows retailers to experiment with different personalization strategies, recommendation placements, and content variants. Tests are automatically powered by our traffic allocation algorithm, which directs shoppers to the winning variant at statistically significant confidence levels — typically reaching significance in 7 to 14 days depending on traffic volume.
Our analytics dashboard provides a real-time view of key performance indicators: revenue attributed to personalization, recommendation click-through rates, uplift over control groups, and experiment results. All metrics are exportable to your data warehouse or BI tools via our data connector integrations for Snowflake, BigQuery, and Redshift.
Each session receives a real-time purchase intent score that triggers personalized urgency messaging, targeted offers, and priority inventory display.
Run statistically rigorous experiments on recommendation strategies, page layouts, and offer mechanics without third-party testing tools.
Sync all behavioral data and analytics results to Snowflake, BigQuery, or Redshift for integration with your existing BI stack.
Madewithinter is designed to work with the e-commerce stack you already have. Our pre-built integrations cover the leading commerce platforms, marketing automation tools, and customer data platforms, so you can go live in days, not months.
Native integrations with Shopify, Shopify Plus, Magento 2, WooCommerce, BigCommerce, and Salesforce Commerce Cloud. Custom REST API for headless commerce architectures.
Sync personalized product recommendations to Klaviyo, Mailchimp, HubSpot, Salesforce Marketing Cloud, and Braze for triggered email and push campaigns.
Bi-directional integrations with Google Analytics 4, Segment, Rudderstack, Amplitude, and major CDPs for a unified customer data ecosystem.