Sell More to Every Customer With AI-Powered Product Recommendations
Quick answer: An AI product recommendation engine analyses what shoppers browse, buy and ignore, combined with your catalogue data, to show each person the products they're most likely to want. Recommendations appear on product pages, in the cart, on the home page, in your app and in emails.
DEFINITION
What Is AI Product Recommendations?
Product recommendations are the "you might also like" and "frequently bought together" sections that help shoppers find more of what they want. Simple versions show bestsellers or manually chosen products, the same for everyone.
An AI recommendation engine learns from behaviour. It combines collaborative filtering, which finds patterns in what similar shoppers buy, with content understanding, which knows how products relate by category, style, price and attributes. Modern models also use the shopper's current session to adapt in real time.
The result is a personal set of suggestions for every visitor and every placement, governed by business rules you control, such as stock levels, margins and brand guidelines.
THE PROBLEM
The Problem
Shoppers rarely find everything they'd buy on their own. Large catalogues are hard to browse, search only helps when people know what they want, and many visitors leave after viewing a single product.
Generic recommendations don't help much. Showing the same bestsellers to everyone ignores what a shopper has just looked at, and manually curated lists take time to maintain and quickly go stale.
Off-the-shelf recommendation plugins often work as a black box. They may promote out-of-stock or low-margin items, ignore your merchandising priorities and offer little insight into why products were suggested.
Personalisation also stops at the website. Emails, app screens and post-purchase messages often show the same generic products, even when you already know exactly what each customer has browsed and bought.
SIGNS YOU NEED IT
Signs You Need This
Average order value has been flat for a long time
Most visitors view one product and leave
Recommendations show the same bestsellers to everyone
Merchandisers update related-product lists by hand
Your recommendation plugin ignores stock or margin
Emails feature the same products for every subscriber
THE SOLUTION
How Codersarts AI Solves It
Codersarts AI builds a recommendation engine trained on your catalogue and shopper behaviour. It learns which products are viewed and bought together, which attributes matter to which customers, and how intent changes during a session.
Recommendations are served through an API to every placement you choose: product pages, cart, home page, category pages, search results, your app and personalised emails. Each placement can use a different strategy, such as complementary items in the cart or similar alternatives on a product page.
Business rules keep you in control. You can exclude out-of-stock items, boost new arrivals or high-margin products, respect brand rules and set diversity limits. Performance is measured with proper tests, so you know the revenue the engine adds.
Merchandisers stay in charge without needing developers. A simple control panel lets them pin products for campaigns, boost seasonal ranges or exclude items, while the engine handles personalisation for everything else.
HOW IT WORKS
How It Works
Data and goals — we review your catalogue, behaviour data, placements and business goals
Model design — recommendation strategies are chosen for each placement
Build and train — models are trained on your data and served through an API
Integrate placements — recommendations are added to your site, app and emails
Test and optimise — A/B tests measure lift, and models and rules are tuned
KEY FEATURES
Key Features
Personalised suggestions — different products for every shopper
Real-time session awareness — recommendations adapt as shoppers browse
Multiple strategies — similar items, complementary items, trending and recently viewed
Business rules — stock, margin, brand and diversity controls
Cold-start handling — sensible suggestions for new shoppers and new products
Email recommendations — personalised products in campaigns and flows
Explainable logic — clear reasons such as "bought together" or "similar style"
A/B testing — measured lift in conversion and order value
Merchandiser controls — pin, boost or exclude products without developers
EXAMPLE
Example Workflow
A shopper views a pair of trail running shoes
The product page shows similar shoes in their size and price range
After adding the shoes to the cart, the cart page suggests running socks and a hydration belt
An out-of-stock jacket is excluded automatically
Two days later, a follow-up email suggests a matching running top
The shopper returns and adds it to a second order
WHERE IT FITS
Where It Works Best
Fashion and apparel — style, size and outfit recommendations
Electronics — accessories and compatible products
Grocery and consumables — replenishment and basket building
Home and furniture — complementary pieces and room sets
Marketplaces — relevant items across many sellers
Digital products and courses — next course or content suggestions
COMPARE OPTIONS
How It Compares
Custom recommendation engine | Recommendation plugin | Manual curation | |
Personalisation | Per shopper and session | Often basic | None |
Business rules | Fully controllable | Limited | Full, but manual |
Catalogue understanding | Attributes and behaviour | Behaviour only | Merchandiser knowledge |
Channels | Site, app and email | Usually site only | Wherever you update lists |
Measurement | Proper A/B tests | Vendor-reported | Rarely measured |
Ownership | You own the model | Vendor's platform | Yours |
BEFORE AND AFTER
Before and After
Area | Without AI recommendations | With AI recommendations |
Product pages | Same related products for everyone | Personal suggestions for each shopper |
Cart | No add-on suggestions | Relevant complementary items |
Stock | Out-of-stock items still shown | Excluded automatically |
Emails | Same products for every subscriber | Personal products per subscriber |
Merchandising | Manual list updates | Rules and boosts, updated automatically |
COMMON CHALLENGES
Common Challenges and How We Handle Them
New shoppers with no history — session behaviour and popular items fill the gap
New products — content-based models recommend them before sales history exists
Recommendation loops — diversity rules prevent showing the same items repeatedly
Proving value — A/B tests measure real lift against a control
INTEGRATIONS
Integrations and Platforms
The engine connects to e-commerce platforms such as Shopify, WooCommerce, Magento or custom stores through APIs and event tracking. Recommendations are served through an API to your site, mobile app and email platforms such as Klaviyo or Mailchimp.
Models run in your cloud or ours, with scheduled retraining and real-time serving. We choose methods based on catalogue size, traffic and latency needs.
TIMELINE
Implementation Timeline
Phase | What happens |
Weeks 1–2 | data review, tracking and placement plan |
Weeks 3–4 | model build, training and API |
Weeks 5–6 | site and email integration, A/B test launch |
After launch | monthly optimisation and new placements |
SECURITY
Security and Privacy
Shopper data stays in accounts you own
Your data is not used to train shared models
Personal data minimised in training
Privacy and cookie preferences respected
Access controls for merchandising rules
PRICING
Cost and Pricing
We quote a fixed price after reviewing your catalogue, data and placements, based on the number of placements, channels and integrations. There's no hourly billing.
Running costs are hosting, real-time serving and retraining, paid directly to your provider, and scale with traffic.
BUILD OR BUY
Custom Build or Off-the-Shelf Tool?
Recommendation apps and plugins are quick to install and work well for smaller catalogues. They're harder to control, often limited to your website, and rarely share their logic or let you set detailed business rules.
A custom engine fits your catalogue, rules and channels, can be measured properly and belongs to you. It suits businesses with larger catalogues, multiple channels or specific merchandising needs.
MEASURING SUCCESS
How We Measure Success
Average order value — basket size compared with a control
Conversion rate — purchases per session with recommendations
Recommendation click-through — clicks on recommended products
Revenue from recommendations — sales attributed to recommended items
Catalogue coverage — share of products that get recommended
GETTING STARTED
What You'll Need to Get Started
Access to your catalogue and product attributes
Order history and, ideally, browsing events
The placements you want to personalise
Your business rules, such as stock, margin and brand priorities
A merchandising owner to review results
QUESTIONS TO ASK
Questions to Ask Before You Start
Which placements matter most? — start with product pages and the cart
What should never be recommended? — define exclusions such as out-of-stock or restricted items
Which products should be boosted? — new arrivals, high margin or seasonal ranges
Do we track browsing events? — behaviour data improves results significantly
How will we measure lift? — agree an A/B test design before launch
MISTAKES TO AVOID
Common Mistakes to Avoid
Launching without a test — without a control you can't prove the value
Ignoring business rules — recommendations should respect stock and margin
Showing only bestsellers — personalisation is where the gains come from
Forgetting email — personal products in email often drive strong returns
Letting models go stale — retrain regularly as your catalogue and season change
BEST PRACTICES
Best Practices for Success
Start with two placements — product page and cart usually deliver the quickest wins
Track browsing events — they make recommendations far more relevant
Use diversity rules — variety keeps recommendations useful
Give merchandisers control — boosts and exclusions keep the business in charge
Measure monthly — review lift by placement and segment
KEY TERMS
Key Terms Explained
Collaborative filtering — recommending items based on what similar shoppers liked
Content-based filtering — recommending items with similar attributes
Cold start — recommending for new shoppers or products without history
Average order value (AOV) — average revenue per order
Placement — a place where recommendations appear, such as the cart
FIRST 90 DAYS
What the First 90 Days Look Like
First 30 days — product page and cart recommendations live in an A/B test
Days 31–60 — more placements and email recommendations added
Days 61–90 — models tuned from test results, with monthly reviews
WHO'S INVOLVED
Who's Involved
E-commerce or merchandising lead — owns placements, rules and goals
Developer or agency — integrates the API into your store and app
Email marketer — uses recommendations in campaigns
Codersarts AI team — builds, tests and hands over
WHY CODERSARTS AI
Why Teams Choose Codersarts AI
Fixed price — a clear scope, timeline and price agreed before work starts
Built around your process — designed for your data, rules and systems, not a generic template
Works with your team — we collaborate with your sales, marketing or IT team from day one
Start focused, then grow — launch a tight first version, then expand as results come in
You own it — full source code, documentation and IP transferred on delivery
BUILD IT WITH US
Related Services
RELATED USE CASES
Related Use Cases
FAQ
Frequently Asked Questions
Which e-commerce platforms does it work with?
Shopify, WooCommerce, Magento and custom stores, through APIs and event tracking.
How much data do we need?
Order history is the minimum. Browsing events and product attributes improve results significantly.
Can it recommend new products?
Yes. Content-based models recommend new products before they have sales history.
Can we control what's recommended?
Yes. You can exclude, boost or pin products and set rules for stock, margin and brand.
Does it work in emails?
Yes. Personalised products can be inserted into campaigns and automated flows.
Will it slow down our site?
Recommendations are served through a fast API and can load without blocking the page.
How do we measure results?
With A/B tests comparing recommendations against a control group.
Can it work in our mobile app?
Yes. The same API serves your app.
Is shopper data safe?
Yes. Data stays in your accounts, personal data is minimised and privacy preferences are respected.
How is this different from a plugin?
It's built around your catalogue and rules, works across channels and you own it.
Can it handle large catalogues?
Yes. Methods are chosen for your catalogue size and traffic.
Can marketplaces use it?
Yes. Recommendations can span many sellers, with rules for fairness and quality.
How long does it take?
A first version typically takes about 6 weeks.
Do we own the engine?
Yes. You own the models, code and data pipelines, with full IP transfer.
Can it recommend content or courses instead of products?
Yes. The same approach works for articles, courses and digital products.
How often are models retrained?
On a schedule you choose, often daily or weekly.
Can it use segments from our marketing tools?
Yes. Audience segments can shape which strategies each shopper sees.
Ready to Build Your AI Product Recommendations?
Tell us about your process and goals. We'll reply with a clear scope, timeline and fixed price.