AI Product Recommendation Engine for Ecommerce — Hyper-Personalization
Particular Audience is an AI product recommendation engine for ecommerce: it personalizes every product list, carousel and recommendation using shopper behavior, product imagery and item metadata — no cookies required.
AI PERSONALIZATION
Unique Experiences For Every Visitor
Personalize every single product list for each unique customer.
Solve the endless aisle problem, by showing the right thing to the right person at the right time.
Delight customers with the eCommerce experiences of tomorrow. Powered by AI that understands what it’s looking at, AI that can read, AI that knows how people similar to you have browsed and bought.
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Visual similarity
Attribute affinity
Wisdom of the crowd
ROBUST DATA FOR THE WIN
Your Customers Are Their Clicktale.
You have 0.000001% of your customer data, and 100% of your item data. Product data is robust and privacy preserving.
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AI powered contextual commerce
Exceptional eCommerce starts here.
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A DIFFERENT WEBSITE FOR EVERY CUSTOMER
Unique Experiences Are The Future.
You are not the only option. Help customers find what they're looking for, to stop them going elsewhere.
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Performance Across The Longtail
Our technology has the deepest understanding of products online, we process over 100 million items interpreting compatibility and similarity from behavioral comparison, attribute affinity and appearance
Behavioral
Collaborative filtering uses the browsing behavior of all of your customers to define which items go together and which items are alternatives to one another - making cross-sell or finding the right item easy.
Computer vision
AI capable of understanding how things appear and what items are most visually similar.
Natural language
Solve the cold start issue for metadata rich items using natural language processing (NLP) to mine item descriptions for common similar attributes.
Complete Analytics
The most comprehensive measurement suite in market. Efficient optimization depends on it.
Engagement
Engagement at a carousel, slot and algorithm level ensures accurate representation of data.
Attribution
SKU level attribution provides visibility down to a line item helping you to understand the exact items that were recommended, engaged with and purchased.
Incrementality
A/B Testing tracks metrics between cohorts and by page element without a key of code.
Algorithm Performance
Compare how competing algorithms perform in varied context.
Google Analytics
We even integrate into your own Google Analytics to give you visibility on performance in context to the rest of your user experience.
Inventory Allocation
Dependency and/or Choice Modelling provide intelligence on which items depend on others to sell, and which items cannibalise. Improving stock depth and allocation decisions.
Competitive Insights
Report on net gain +/- loss by brand, product or category. Understand the impact boosting and product sponsorship.
Product Area Analysis
Mixed basket analysis demonstrates impact on cross-category discovery, and helps us to understand the impact of recommendation logic on different parts of your retail store.
Longtail Analysis
Enhance discovery of long tail items and increase revenue contributors in your top 80% of turnover: De-risking stock and suppliers while increasing basket size and conversion rates.
100% Managed Services, or via API
Our team do literally all the heavy lifting, from integrations, onsite creative development, reporting and optimisation
Integration
Frontend
Reporting & Optimisation
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Success Stories
“The team over at Particular Audience is amazing!
They are proactive, do all the heavy lifting, and collaborate closely with your team to make sure you meet launch deadlines.
PA keeps revenue and margins in mind when providing smart solutions to your e-commerce needs.”
– Jose Quiroz
Digital Marketing Strategist, Shoe Palace
AI powered contextual commerce
Exceptional eCommerce starts here.
Get Started
Common questions
What is an AI product recommendation engine for ecommerce?
Software that decides which products each shopper sees in carousels, product lists and bundles. Particular Audience combines behavioral (collaborative filtering), visual (computer vision) and attribute (natural language) signals to rank those products for each customer.
Which recommendation strategies does Particular Audience support?
Similar items, visually similar items, frequently bought together and bundles, buy again and replenishment, and personalized product lists, each measurable at carousel, slot and algorithm level.
How does it handle new products with no sales history?
Natural language processing reads item descriptions and computer vision reads product images, so new items get relevant recommendations before any behavioral data exists.
Does it need cookies or personal data?
No. Recommendations are driven by product data and in-session behavior, so they work without third-party cookies or personal data.
How is it integrated?
As a fully managed service — PA handles integration, front-end widgets, reporting and optimization — or via API for teams that render their own experiences.
How are results measured?
A/B testing between cohorts, SKU-level attribution and algorithm comparison. Case studies label each result as a controlled test or an observed outcome.