Product recommendations

Increase revenue with product recommendations based on the shopper’s unique interests.

Watch 4 min platform demo

Don’t waste precious email or onsite real estate by suggesting the same items to everyone. Instead, help each shopper conveniently discover products and content they might like while achieving your unique business objectives. By tailoring product recommendations to each stage of the customer journey across channels, you can generate 10% more revenue. 

  • Boost product discovery and AOV through recs that up-sell, cross-sell and deep-sell.
  • Improve your bottom line by promoting high-margin items. 
  • Clear your warehouse by promoting excess stock to those customers who are most likely to convert.
  • Encourage purchases by tailoring recommendations to each customer's unique affinities, such as price, size or color.

Comprehensive set of recommendation types, including:

“Frequently browsed” & “Frequently purchased”

Suggests products that this individual shopper has browsed or purchased frequently.


"People like you buy"

Suggests the most likely purchase by comparing the shopper’s purchase history with the purchasing behavior of other customers who’ve viewed this product.

People like you buy - Product Recommendations - Optimised.png

"Frequently bought with this" & "Purchased together"

Suggests complementary products based on what people who bought this product have ended up buying with it.

Frequently bought with - Product Recommendations - Optimised.png

"After viewing this, people buy"

Suggests the product that shoppers who viewed this product most often went on to buy.

After viewing people buy - Product Recommendations - Optimised.png

“Similar products”

Suggests products which are similar to the product on the current page, based on the contents of product details, using Natural Language Processing.

Similar products - Product Recommendations - Optimised.png

“Related products”

Suggests products you've defined as related to the current product for merchandising purposes.

Related products - Product Recommendations - Optimised.png

“Best-sellers” & “Trending”

Suggests products that are trending with other shoppers right now.

Best sellers - Product Recommendations - Optimised.png

“New arrivals”

Suggests the latest products, e.g. from across the store, or from the customer’s favorite category.

New Arrivals - Product Recommendations - Optimised.png

Why Fresh Relevance?

Flexibly design the strategy that’s right for you

We give you full merchandising control to design the product recommendations or content recommendations strategy that is right for your business. Build your own strategy by mixing over 30 different recommendation types, merchandising and business filters. For example, you can control which brands appear side-by-side, a vital feature if you’re a retailer selling multiple labels.

Product Recs - Strategy - Optimised.png

Make recommendations personal

71% of brands use basic cross-selling recommendations. To stand out from the competition, make your suggestions more personal and base them on customer behavior, like browsing history, geo-location, category affinity, and purchase history. Treat customers differently based on their stage in the purchase funnel (home page, product page, cart page) or customer lifecycle (first-time user vs. VIP customer). If cookies are enabled, you can serve product recommendations and content recommendations based on previous browsing behavior even before a shopper has signed up with an email address.

Product Recs - Serve Dynamic Recs - Optimised.png

Harmonize recommendations across channels

Provide a consistent cross-channel experience and maximize conversion potential by aligning the most relevant recommendations at all stages of the purchase funnel across email, website, mobile and app. You can recommend products and content anywhere – on navigational menus, the homepage, category pages, site overlays, banners, and inside bulk and triggered emails.

Product Recs - Harmonise across channels.png

Use powerful AI

Our recommendations engine uses solid machine learning to track and analyze the behavior of each shopper. It looks at the shopper’s real-time contextual, browsing, purchasing and demographic data. The platform then filters recommendations using the rules you set to select the products and content each shopper or reader is most likely to be interested in at this moment in time. 

Product Recs - Use Powerful AI - Optimised.png

Serve dynamic recommendations

Product recommendations are based on the most up-to-date data to ensure you don’t suggest products that are out of stock, that the customers just purchased or that have changed in price. You can provide multiple fall back data sources to ensure that customers are always presented with the best possible suggestion. 

Product Recs - Serve Dynamic Recs - Optimised.png

Test and optimize

Set up A/B/n tests for any recommendation element – including the layout, design and location - to understand which recommendation type drives most revenue for your business. Automatically optimize individual pieces of content, or the entire CX, across channels using Machine Learning. Analyze your personalization campaigns in Google Analytics.

Product Recs - Control Groups - Optimised.png

Make your recommendations convert even better

Triggered emails

Include recommendations in cart, form and browse abandonment messages to increase click throughs. Learn more >

Triggered Email - Product Recommendations - Optimised.png

Popularity messaging

Add an element of urgency and speed up on-site conversions with live trends, showing how many people have viewed or bought the recommended item recently. Learn more >

Popularity Messaging - Product Recommendations - Optimised.png

Ratings and reviews

Reduce purchase anxiety and reassure shoppers by including rating stars from other customers. They are pulled in real-time from your product rating provider, ensuring that only recent and positive scores are shown. Learn more >

Ratings & Reviews - Product Recommendations - Optimised.png


Localize recommendations based on the shopper’s physical location at the moment of engagement. Learn more >

ProductRecommendations-Geotargeting - Fixed.svg

Exit intent popovers

Reduce exit rates by suggesting products and content in exit-intent triggered pop-ups. Learn more >

ProductRecommendations-ExitIntent - Fixed.svg

Ready to give recommendations a go?

book a demo


How long does it take to get started?

No integration projects or product feeds needed, meaning you can deploy recommendations faster, and manage them simpler and at lower costs. Dynamic SmartBlocks allow you to add product recommendations and content recommendations to your site without any need to access or touch the source code.

How does Fresh Relevance integrate with my martech stack?

Built to play nicely with the existing martech stack, Fresh Relevance is completely platform agnostic and supports all ESPs and ecommerce platforms.

Will I receive help in the onboarding process?

Our experienced onboarding team will take you through the steps so you can start generating more revenue from day one.

How much does it cost?

Our unique pricing model is based on the average traffic on your website. So you can use product recommendations as often as you like, the costs will stay the same. Contact us here for a quote.