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AI Recommendation Engine for Retailers

Turn every interaction into the next best product

Not generic "you may also like" widget. ContactPigeon recommendations are powered by real customer behavior, predictive insights, and AI trained on your catalog.

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Powering Customer Experiences for Leading Retailers Globally

AI powered recommendations for smarter conversion

Every recommendation is powered by live behavior, triggered at the moment of intent, and activated across every channel, so it doesn't just personalize the experience, it drives conversion.

What to Recommend

Recommendations powered by customer insights and deep product understanding

Most recommendation engines rely on outdated signals and surface-level product data. ContactPigeon goes further. Every recommendation is powered by a live, unified customer profile that updates with every click, view, purchase, and interaction, combined with an AI engine trained on the merchant's full product catalog.

The difference?

Recommendations don't just follow behavior, they interpret intent and match it with the right products, turning generic suggestions into precise, high-converting decisions.

When to Engage

Engages at the right moments

Even the best recommendation fails if it shows up at the wrong time. ContactPigeon is built to activate when it matters - during browsing, at add-to-cart, on exit intent, after a price drop, or when a customer is about to disengage.

It reads context, identifies intent, and triggers recommendations exactly when they are most likely to influence action.

Where to Engage

Activated across every revenue-driving channel

Recommendations can be activated across the entire customer journey - website, pop-ups, email, push notifications, SMS, Viber, and beyond. What a customer browses instantly shapes what they see next, everywhere. The result is a consistent, connected experience where product discovery doesn't stop at a page. It follows the customer across channels and drives conversion at every touchpoint.

Personalized Recommendations

Personalization that learns and adapts

Recommendations are continuously updates based on real-time behavior, historical activity, predictive insights, and campaign engagement.

If a shopper browses a new category, abandons a cart, clicks a campaign, returns after inactivity, or purchases in-store, their recommendations can adapt immediately.

Real-time Intent

Recommend products based on what shoppers are browsing right now.

Historical Behavior

Use past purchases, views, and engagement history to personalize future suggestions.

Business Rules

Prioritize high-margin products, overstock items, or specific product categories.

Predictive Insights

Use churn risk, expected next purchase, and next-best-action predictions to shape targeting.

Customer Segment

Adapt recommendations for VIPs, first-time visitors, inactives, and other segments.

Population Patterns

Use patterns from similar segments to predict what a shopper's next likely purchase.

16% sales growth using customer insights and recommendations

At Calvin Klein, unifying customer data across channels allowed us to deeply understand our audience. Through automation and personalization, we didn't just increase revenue, we elevated the entire customer experience.

Giorgos Betchavas, Managing Director, SARKK S.A. Tommy Hilfiger & Calvin Klein

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Use Cases

From first click to repeat purchase, recommendations that move customers forward

Guide shoppers with personalized recommendations that adapt to every stage of the journey, from discovery and consideration to conversion and retention.

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New visitor recommendation

Help first-time shoppers discover relevant products faster.

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Value-based recommendations

Maximize revenue by adapting product recommendations to each shopper's perceived propensity to spend.

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Replenishment recommendations

Drive repeat purchases based on predictions on when customers will likely replenish their products.

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Cross-sell & upsell recommendations

Increase order value with complementary products.

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Recovery recommendations

Bring shoppers back to the products or categories they explored.

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Win-back suggestions

Win back inactive shoppers with relevant product suggestions and offers.

Stop guessing what customers want

Use real-time customer insights, predictive intent, and AI-powered product understanding to recommend what shoppers are actually most likely to buy.

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Trusted by 500+ brands globally. Rated Top Customer Satisfaction on G2.

Turn every channel touchpoint into a product discovery opportunity.

ContactPigeon lets retailers dynamically inject product recommendation blocks across owned channels, not just on the website.

On-site content

Display personalized product blocks on homepage, product pages, checkout flows, and landing pages.

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Chatbot

Guide product discovery with AI powered recommendations directly inside chat experiences.

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Email

Automatically include product suggestions inside campaigns and automated flows.

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Mobile

Send timely product nudges through direct channels - SMS, Push, WhatsApp when speed matters.

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FAQ

Frequently asked questions on Recommendation Engine

What data is used to power recommendations?

Recommendations can be powered by browsing behavior, purchase history, cart activity, product views, wishlist interactions, campaign engagement, loyalty data, customer segments, predictive scores, and offline interactions.

How is ContactPigeon different from traditional recommendation engines?

ContactPigeon connects recommendation logic with unified customer data, predictive insights, automation, and owned channels. Instead of showing generic product widgets, recommendations adapt to each shopper's real-time behavior, intent, customer profile, and lifecycle stage.

Can recommendations be personalized for different customers?

Yes. Recommendations can adapt for first-time visitors, returning shoppers, VIP customers, inactive customers, high-intent browsers, and custom audience segments based on live and historical customer data.

Can I control which products are prioritized?

Yes. Retailers can apply business rules to prioritize categories, high-margin products, private labels, seasonal items, overstock products, or specific campaigns while still using customer intent and predictive signals.

Can recommendations be used inside automated flows?

Yes. Product recommendations can be activated inside automated journeys such as browse abandonment, cart recovery, replenishment, win-back, post-purchase, and lifecycle campaigns.

How long does implementation take?

Implementation time depends on catalog setup, data sources, channels, and recommendation use cases. ContactPigeon supports onboarding and setup so teams can start with priority journeys first and expand over time.

How is Menura AI applied to product recommendations?

Menura AI helps interpret customer behavior, predictive intent, customer segments, and product catalog signals to recommend products shoppers are most likely to engage with or buy.