AI Product Recommendations Powering the Future of Personalisation

AI is changing how we shop — big time.

Consumers struggle to find what they want online, leading to wasted ad spend and lost sales for merchants. But imagine if every shopper saw exactly what they needed, right when they needed it.

Traditional "customers also bought" recommendations are old news.

Google's AI-powered shopping and its impact on product recommendations promise to surface relevant alternatives and complementary items that can boost product discovery, conversions, and your average order value (AOV).

This isn't magic; it's smart data at work. Getting it right depends on quality data, strategic placements, and continuous testing.

BigCommerce, as an Open SaaS platform, empowers merchants to leverage these AI personalised product recommendations through native capabilities, partner integrations like Feedonomics, and a robust app store.

For online retailers, achieving hyper-personalisation is the holy grail for customer retention. Statistica reports 47% of global business leaders identify data accuracy as their measure of success, and 44% attribute real-time data speed to repeat purchases.

This guide goes beyond theory to show you how to actually put a recommendation system using AI to work on your BigCommerce store. You'll get practical steps, guardrails to avoid common pitfalls, and insights for both B2C and B2B commerce.

Whether you're moving fast with partner apps or building custom with APIs, you’ll learn how to create recommendations that lift sales and build trust.

What are AI product recommendations?

AI product recommendation engines examine customer preferences to suggest products that match each person's interests and shopping habits. These systems learn from purchase patterns, browsing history and clicks to predict what someone might want next.

Traditional merchandising requires manual setup. Store managers create product bundles, link related items, and build conditional rules by hand. Anyone who has uploaded thousands of products knows how tedious this process becomes. Manual approaches work best when you lack enough customer data to train algorithms, or when your brand demands highly curated selections that follow specific editorial guidelines.

Setting clear objectives.

A system that recommends products is designed to support different business goals based on customer behavior and where they are in their buying process. Use cases vary by customer journey stage:

• Awareness helps people discover new products early in their journey. Customers see suggestions like "Popular or trending in your area" or "Because you viewed X" that introduce relevant recommendations.

• Interest encourages larger purchases by showing complementary items during consideration. "Complete the look" recommendations increase average order values by suggesting matching pieces.

• Desire influences decisions toward premium options through cross-selling and upselling. "Upgrade to Pro" messages guide buyers toward higher-value alternatives.

• Action focuses on post-purchase history. These recommendations drive repeat buying and build long-term customer relationships, potentially improving customer satisfaction, which has dipped recently.

Managing realistic expectations.

AI systems enhance rather than replace human merchandising skills. Recommendation algorithms excel at processing large datasets and spotting patterns, but people provide creative vision, brand understanding, and seasonal awareness that machine learning cannot replicate.

The best approach combines both strengths. AI handles personalisation while merchandisers guide the system, establish boundaries, and add creative insights. This partnership ensures recommendations feel relevant and authentic to your brand's voice and current campaigns.

Guardrails for trustworthy AI-powered product recommendations

AI recommendations can boost revenue by surfacing relevant products, but success requires following key guardrails to maintain customer trust and effectiveness.

Start with clean product data. Recommendations are only as good as the underlying catalog. Ensure accurate titles, complete attributes, current pricing and availability, and quality images before adding personalisation layers. Incomplete or vague product descriptions lead to irrelevant product suggestions that erode trust.

Measure real impact, not assumptions. Always validate performance with A/B tests or holdout controls rather than relying on gut feelings. Track whether recommendations actually drive conversions or just consume valuable screen space. For example, test recommendation widgets against clean product pages to measure actual lift in purchase rates.

Respect customer privacy and choice. Use only consented first-party data and provide clear opt-out options for personalisation. Transparent data practices build the trust necessary for customers to engage with AI-driven features.

Design with discipline. Limit the number of recommendation modules, especially on mobile devices, where space is premium. Monitor impact on page load times and customer engagement. Retire underperforming widgets that don't generate clicks because a cluttered checkout screen with irrelevant suggestions can derail purchases.

Place recommendations thoughtfully. Context matters as much as content. A well-timed suggestion on a product page can guide discovery, while the same recommendation in the wrong place feels intrusive. Consider the customer's mindset at each touchpoint.

Without these guardrails, even sophisticated AI models can damage the personalized shopping experience and reduce conversions rather than improve them.

Quick-start setup for impact.

To maximize the immediate impact of AI product recommendations, focus on these actionable steps:

Prioritise high-intent zones with strategic placement.

• Product detail pages (PDPs). Launch with either "Similar items" (for alternative discovery) or "Frequently bought together" (for immediate cross-sell). Shoppers here are actively engaged with a product, making them receptive to highly relevant suggestions.

• Cart page. Implement "Complete the look" recommendations. At this stage, customers are preparing to check out, and subtle suggestions for complementary items can significantly boost average order value (AOV) without disrupting their purchase intent.

Resist the urge to clutter your homepage with recommendation rows initially. While tempting, homepage rows often have lower intent and can dilute the impact of your first test. Focus on the PDP and cart where the context is strongest.

Catalog cleanup to deliver relevant AI.

Before activating any recommendations, perform a rapid audit of your product catalog.

• Titles and descriptions: Ensure they are accurate, keyword-rich, and clearly describe the product. AI relies heavily on this textual data for relevance.

• Images: Use high-quality, consistent imagery. Visual appeal is paramount for enticing clicks.

• Key attributes (size, color, material, brand): Verify these are complete and standardised. Granular attributes empower AI to make more intelligent, more precise recommendations (e.g., suggesting a "green cotton t-shirt" rather than just a "t-shirt").

• Price and stock levels: Absolutely critical. AI should never recommend an out-of-stock item or display incorrect pricing. Up-to-date information is non-negotiable for a positive customer experience.

Think of your product data as the fuel for your AI engine. Poor data leads to "garbage in, garbage out." A clean, well-structured catalog directly translates to more intelligent and practical recommendations.

Refine recommendation logic to guarantee quality.

• Cap repetition. Implement a rule to prevent recommendation modules from displaying more than two items from the same brand. Exclusivity promotes diversity in suggestions and encourages broader product exploration.

• Exclude out-of-stock products. Configure your system to filter out unavailable products automatically. There's nothing more frustrating for a customer than clicking a recommendation only to find the item out of stock.

• Exclude low-margin products. Strategically omit products with very low-profit margins from recommendations, especially for cross-sell or upsell initiatives. Focus the AI's power to increase sales.

While AI is powerful, it benefits from intelligent boundaries. These simple rules enhance the user experience and ensure your recommendations align with your business's financial goals, preventing AI from "optimising" for irrelevant or unprofitable outcomes.

Define weekly KPIs.

• Click-through rate (CTR) on recommendation modules. This measures the effectiveness of your recommendations in grabbing attention and generating interest. A high CTR indicates that the suggested products are relevant and compelling to your audience.

• Attach rate. This metric (number of recommended items added to cart / total items purchased) directly shows how often recommendations lead to additional purchases. It's a key indicator of successful cross-selling and upselling.

• Revenue per session (RPS). This represents a holistic measure of how much revenue each user session generates. If your recommendations are working, RPS should see a noticeable lift, demonstrating their overall impact on your bottom line.

• Focus on these three metrics weekly for a clear picture of performance. Avoid getting lost in too many data points; these provide a balanced view of engagement, conversion and financial impact.

Learn, iterate, optimise.

• A/B split test. Implement a 50/50 A/B split test for your initial placements (e.g., half your users see the "Similar Items" module on PDPs, the other half don't). Run this test for at least one complete promotional cycle (e.g., an entire week, or longer if you have a longer sales cycle).

• Identify winning placement. Once the test concludes, analyse your KPIs. Identify the recommendation placement that generated the highest CTR, attach rate and RPS.

• Iterate on copy and assortment: Keep the winning placement enabled. Then, begin to iterate on the specific copy used to introduce the recommendations (e.g., "You might also love" vs. "Handpicked for you") and experiment with different recommendation assortments (e.g., only same-brand vs. mixed-brand suggestions).

Don't aim for perfection on day one. Start simple, gather data, and make data-driven decisions. Continuous testing and iteration are the keys to unlocking the full potential of AI recommendations.

Why B2B sales cycles are different

While the core concept of AI product recommendations for B2C remains the same for B2B buyers — showing the right product to the right person — the B2B environment introduces crucial complexities that demand a different approach. B2C is often about impulse and individual preference. B2B is about logic, relationships and operational efficiency.

For B2B companies, personalization goes beyond individual taste. It's about understanding the entire account, the role of the person shopping, and the operational needs of their business.

Account-specific catalogs and contract pricing are king.

In B2C, everyone generally sees the same public catalog and pricing. In B2B, what's available and how much it costs is often dictated by a specific contract with one particular account.

Your AI needs to be deeply integrated with your CRM and ERP systems. Recommendations must only surface products available to that specific account's catalog and always display their negotiated contract pricing. Recommending a product they can't buy or at the wrong price is a quick way to erode trust.

Reorder cadence is your retention superpower.

B2C often aims for a new, exciting purchase. B2B frequently revolves around repeat orders of essential supplies, parts or services.

AI can become incredibly powerful here by predicting reorder needs. "Customers like you reorder this every three months" or "Your stock levels suggest a reorder soon" become vital recommendations. These prompts aren't about discovery; it's about efficient replenishment and ensuring your customers never run out of critical items.

Be mindful of role-aware placements.

You might have a procurement manager (buyer) who needs to find SKUs and process orders quickly, and a department head (approver) who needs to see cost summaries or strategic alternatives.

Tailor the recommendations based on the user's role within their organization.

• For the buyer. Focus on "Reorder essentials," "Compatible parts," or "Frequently purchased together with your last order." Make it about speed and accuracy.

• For the approver. Recommendations might lean toward "Cost-saving alternatives," "Higher-efficiency models," or bundles that offer volume discounts, emphasizing long-term value.

Understand the constraints of MOQ cross-selling.

Unlike B2C, where you buy one of something, B2B often deals with minimum order quantities (MOQs) or bulk purchasing.

Your cross-sell recommendations shouldn't suggest a single unit of a product with a 100-unit MOQ. Instead, if a customer is buying item A with an MOQ of 50, recommend item B that also meets a similar MOQ and complements item A. The copy should be utilitarian and direct.

Example copy: Instead of "You might also like," use:

• "Reorder essentials"

• "Compatible parts for your equipment"

• "Commonly purchased with this item (bulk savings available)"

• "Upgrade options for your current model"

How BigCommerce supports AI recommendations

Artificial intelligence has changed the way shoppers discover products. Instead of scrolling through endless catalog pages, buyers now expect tailored suggestions that feel almost intuitive. For ecommerce brands, this shift is less about hype and more about measurable impact. Personalised recommendations lift conversion rates, increase order values, and create customer loyalty that outlasts any single purchase.

BigCommerce makes this possible by offering two things most retailers struggle to balance: speed and control. Merchants can launch quickly with partner apps or build custom experiences through open APIs. Either way, clean product data sits at the center. Without it, even the most advanced tool will fall short.

Build on a strong data foundation.

Recommendations rely on the quality of catalog data. Titles, attributes, categories, images and current price or availability form the bedrock of accuracy. A missing attribute or outdated price can derail trust and lead to mismatched results.

Feedonomics, part of the Commerce family of brands, ensures data is enriched and distributed without errors. Enrichment goes beyond filling blanks. It corrects inconsistencies, aligns categories, and adds attributes that present better content-based filtering. A single fixed attribute can transform a recommendation set on a product detail page.

Consistency matters across channels as well. When feeds reach Google, Meta, marketplaces, and social platforms, shoppers expect to see the same price, description, and availability they saw on the store. Frequent syncs, transparent governance, and suppression of out-of-stock items prevent surprises. A simple routine of daily syncs, validation checks and backfills for new SKUs keeps everything fresh. Assigning ownership of data quality ensures nothing slips through the cracks.

Two paths to implementation.

Every retailer faces the same question: launch quickly or build with more control? BigCommerce supports both.

1. Fast path: Partner apps.

Installing an app from the BigAI partner marketplace delivers results quickly. Options like Nosto, Klevu and Bloomreach cover a wide range of needs. Nosto focuses on onsite personalisation, Klevu combines search with recommendations, and Bloomreach emphasizes a guided journey.

Setup follows a straightforward flow: install, authorise, map your catalog and events, then place widgets in key locations such as product pages, carts or search results. Merchants should also confirm permissions, understand pricing models, and review support coverage before activating.

Most apps provide basic merchandising controls such as pinning, excluding, or applying diversity caps.

1. Flexible path: Composable and headless.

Some teams prefer control over speed. Open SaaS flexibility makes this possible. Developers can embed recommendation APIs directly into custom storefronts while still using BigCommerce as the backend.

Choices around server-side rendering (SSR) or client-side rendering (CSR) affect performance and SEO. Clear documentation helps teams weigh these trade-offs. Modules can be injected into product detail pages, carts or search results, with caching windows and fallbacks in place to handle API timeouts. Version control and dependency management reduce risk when updating integrations.

For merchants staying closer to default themes, BigCommerce also provides widgets that shorten the path to value without locking stores into a single approach.

Feedonomics and omnichannel advantage.

Accurate recommendations don't stop at the storefront. Omnichannel success depends on consistent feeds across Google, Meta, marketplaces and social networks. Feedonomics surfaces enriched data everywhere, ensuring that an item promoted on a marketplace aligns with what the shopper sees on the brand's own site.

Error handling and sync frequency matter here as much as they do on the storefront. Automatic suppression of out-of-stock products prevents wasted ad spend and disappointed customers. For example, when a retailer corrected a size attribute, the recommendation engine instantly displayed better-matching clothing items on PDPs while ads across channels adjusted to reflect actual availability.

Governance and best practices

Data and AI tools require stewardship, not just setup. Governance ensures syncs run smoothly, errors get resolved, and business rules are applied consistently. Merchandising teams should define who owns catalog accuracy, while technical teams manage API access and export checks before launch.

For custom builds, sandbox environments allow safe testing of recommendation modules. Developers can validate performance, stress-test caching strategies, and ensure graceful fallbacks when APIs respond slowly. Simple habits like documenting ownership and reviewing permissions reduce friction later.

Final word

AI product recommendations have evolved far beyond Amazon's "customers also bought" approach. Modern systems use deep learning to analyse user behavior, past purchases, demographics and user preferences to deliver personalized shopping experiences that boost conversions and average order values.

Success starts with quality product data — clean, structured catalogs directly translate to intelligent recommendations. Without this foundation, even sophisticated collaborative filtering algorithms fail to deliver relevant suggestions.

Feedonomics ensures data accuracy across all channels, while the platform's flexibility supports both rapid deployment and long-term customisation. This combination makes it fast to ship initial implementations and safe to scale sophisticated recommendation systems.

BigCommerce combines the flexibility of open SaaS with the reliability of partner apps and enriched data feeds. Whether moving fast or building for the long haul, merchants can create experiences that help customers discover products more easily — and keep coming back.

FAQs about AI product recommendations

What are AI product recommendations, and how do they work?

AI product recommendations use machine learning algorithms to analyse customer data, browsing patterns, and product attributes to suggest relevant items. These systems learn from purchase history and clicks to predict what shoppers might want next, automatically surfacing personalized suggestions that match individual interests and shopping habits.

What types of data are used to generate AI product recommendations?

AI product recommendations use several data types: customer purchase patterns, browsing history, click behavior, product attributes (size, color, material, brand), catalog information (titles, descriptions, pricing, availability), and customer preferences. Clean, accurate product data with complete attributes enables more precise and intelligent recommendations.

How does AI personalisation differ from traditional recommendation engines?

Traditional recommendation engines use manual setup with "customers also bought" suggestions and require store managers to create product bundles by hand. AI personalisation automatically learns from customer behavior, processes large datasets to spot patterns, and delivers dynamic suggestions that adapt to individual shopping habits without manual intervention.

What are the benefits of using AI for product recommendations in ecommerce?

AI product recommendations boost revenue by increasing conversion rates, raising average order values, and building customer loyalty. They improve product discovery, drive repeat purchases, and create hyper-personalised shopping experiences. The system processes large datasets to surface relevant alternatives and complementary items that traditional manual merchandising cannot match.