This is an announcement placeholder
This is an announcement placeholder
Algonomy Recommend™ shows each shopper the products most likely to convert, and delivers timely messages and experiences that inspire action, helping retailers grow sales without losing merchandising control.
Retailers know relevance drives revenue, yet most personalization tools create new challenges.
Shoppers browsing a single item often miss the full outfit.
Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Returning shoppers don’t need to re-browse your entire catalog.
Recommend prioritizes the brands and categories each shopper loves, and adjusts banners and editorial content to showcase new arrivals from those exact labels.
Discount visibility can cheapen premium perception.
Recommend limits markdown exposure to relevant audiences, and delivers sale messaging only to shoppers most responsive to offers, preserving exclusivity for others.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Shoppers browsing a single item often miss the full outfit. Recommend uses merchandising logic to pair complementary pieces and personalizes the promotional banner or seasonal campaign that highlights trending collections.
Add banners, widgets, or content anywhere on your site without developer help. Quickly deploy seasonal offers or new product stories from an intuitive visual interface.
Add banners, widgets, or content anywhere on your site without developer help. Quickly deploy seasonal offers or new product stories from an intuitive visual interface.
Add banners, widgets, or content anywhere on your site without developer help. Quickly deploy seasonal offers or new product stories from an intuitive visual interface.
Add banners, widgets, or content anywhere on your site without developer help. Quickly deploy seasonal offers or new product stories from an intuitive visual interface.
Recommend™ integrates seamlessly across client- and server-side implementations, ensuring speed, SEO-friendliness, and data accuracy.
Real-time updates from product catalog to personalization engine
Connect effortlessly with the systems you already use, eliminating silos and enabling continuous optimization across the shopper journey
Algonomy’s infrastructure and processes meet the highest standards of data protection, privacy, and reliability. We secure every transaction and data stream so your teams can personalize confidently, at scale, and within compliance boundaries.
Head of Webshop Development
CEO
Deliver faster paths to purchase with guided, personalized shopping experiences, powered by agentic AI that keeps shoppers engaged and drives higher conversions.
Deliver unique, contextual search results based on the individual shopper’s behavior. Eliminate instances of zero results with search that learns from the wisdom of crowds.
Guide and inspire your shoppers with stunning outfits at key touchpoints—driving higher conversions and order values.
Harness Wisdom of the Crowd to Build Trust, Create Urgency, and Accelerate Conversions
Recommend™ decides what experience to show and delivers it at the right time, and the right place – helping retailers increase conversion, protect margins, and create seamless shopper journeys – built for modern merchandising.
Support easy buying with individualized, behavioral-driven experiences that remove the friction from commerce across the customer lifecycle.
Deepen loyalty while increasing revenue and repeat purchases with complementary offers and recommendations that complete, enhance, or replenish.
Balance manual curation and automated optimization by combining the best of real-time AI-driven decisioning and the most advanced set of merchandising tools and configurations on the market.
Yes. Personalization should respect your business and merchandising commitments. Through a simple user interface, merchandisers can set custom weights for different attributes such as brand, category, price, newness, and more. In addition, personalization delays can be set to impact how quickly you want to influence search results.
The personalization software is equipped with an Experience Optimizer (XO) that selects the winning strategy based on the Unified User Profile Service (UPS), the chosen KPI — RPV, AOV, Conversion Lift, etc., shopper’s stage in the funnel. This AI-driven decisioning by XO ensures the highest performing strategy is chosen, and the most relevant products are shown to every shopper, without the need for manual merchandising.
Algonomy understands the unique needs of different retail segments and has proven expertise with the verticals of fashion & apparel, grocery and hypermarkets, convenience stores, restaurant/QSR, specialty, pharma & healthcare, beauty, consumer electronics, and more.
Yes, absolutely. We provide the industry’s only Experience Browser (XB) that provides instant transparency into why an experience was chosen for a given individual. The XB overlays right on top of your website, so you don’t have to leave your browser tab, and can audit AI decisions with a single click, and dig into the user profile, strategy evaluation, rules, and the process of personalized product recommendation selection.
You can use our advanced recommendation software DeepRecs™, which leverages product data to contextually recommend new, seasonal, and long-tail products that would’ve otherwise remained buried due to lack of historical events. DeepRecs NLP uses deep learning algorithms to analyze catalog descriptions, reviews, and other textual data to infer relationships between products. DeepRecs Visual AI extracts features from product images and identifies relevant items for a personal shopper experience, in fashion & apparel and furniture verticals.
Yes. Recommend allows for open-time personalization of emails. It lets you personalize the contents of an email based on when the email is opened — updating messaging and recommendations based on the latest activity, context, and purchases, while also checking the latest inventory levels. The aim is to eliminate redundant experiences, be it on emails or on digital channels. This ensures that personalized product recommendations in eCommerce are always timely and relevant, enhancing the customer experience at every touchpoint.
Yes, we understand that you may have specific business objectives that require manual merchandising, e.g. promote high margin brands or ensure certain products are never recommended. While Recommend constantly optimizes and selects the best performing products, we also allow you to boost, restrict, or set manual rules to help you meet your business goals. However, we suggest respecting the AI engine and avoid setting up too many restrictive rules that can end up creating a sub-optimal experience for the shoppers.
The Advanced Merchandising feature adds an extra layer of intelligence and product knowledge to product recommendation — providing a scalable method of managing cross-sell, up-sell, and complete-the-bundle recommendations without having to manually merchandise every SKU.
Manual merchandising leaves recommendation slots unfilled when products go out of stock and new products either are not recommended or do not have recommendations until the merchandiser is able to get around to adding them. Advanced merchandising automatically fills slots when empty or swaps in new items.
