Most WooCommerce stores are running “Related Products” and calling it a recommendation engine.
It’s not. WooCommerce’s built-in Related Products section pulls items from the same category or tag, then picks randomly from that pool. No purchase history. No browsing behavior. No “customers who bought this also bought” logic. Just random products that happen to share a taxonomy.
That means a customer buying a $300 espresso machine sees a $12 bag of decaf because they’re both in the “Coffee” category. A repeat buyer who’s purchased 6 items from you sees the same random spread as someone who landed on the page 3 seconds ago.
Amazon attributes 35% of its revenue to recommendations. Personalized product suggestions increase average order value by 10 to 30%, according to McKinsey. And your store is showing random products from the same category.
That’s not a minor gap. That’s one of the biggest revenue leaks in WooCommerce.
I spent months building a recommendation engine for WooCommerce because the existing options were either random (default WooCommerce), too expensive (enterprise SaaS), or too limited (“frequently bought together” with no real scoring). Here’s what I found when I compared every serious option.
Why WooCommerce Default Recommendations Are Random
This is worth explaining because most store owners don’t know this.
WooCommerce’s Related Products section uses a function called wc_get_related_products(). Under the hood, it queries products that share the same category or tag as the product being viewed, excludes the current product, and returns a randomized set. The randomization happens on every page load.
There’s no scoring. No weighting. No learning. No analysis of what sells together. The function has been essentially unchanged for years. WooCommerce core treats recommendations as a display feature, not a revenue feature.
This matters because store owners assume something intelligent is happening behind the scenes. They see 4 products in the “Related Products” section and think WooCommerce is doing the math. It isn’t. Those 4 products are random picks from the same category bucket.
If your store has 200 products in the “Clothing” category and someone views a winter jacket, the related products section is pulling 4 random items from that 200-product pool. A summer dress could show up next to a parka. A $15 t-shirt next to a $400 coat. There’s no intelligence filtering those results.
Every product page on your store with default related products is a missed opportunity. Let’s look at what actually works.
Quick Comparison Table
| Plugin | Best For | Recommendation Logic | Pricing | WooCommerce Native |
|---|---|---|---|---|
| Refinery Product Recommendations | Relevancy-scored suggestions based on purchase data | Purchase correlation, browsing patterns, trending, customer segments | $49-149/year | Yes |
| Product Recommendations by WooCommerce | Stores wanting official Automattic backing | Frequently bought together, category-based, top-rated | $99/year | Yes |
| Also Bought for WooCommerce | Simple “bought together” on a budget | Order history co-occurrence | ~$49/year | Yes |
| Nosto | Enterprise stores with big budgets | AI/ML behavioral analysis, email recs, pop-ups | $500-2,000+/month | SaaS with WooCommerce connector |
| LimeSpot | Mid-market stores wanting personalization | Behavioral AI, segmented recommendations | $18-200+/month | SaaS with WooCommerce connector |
1. Refinery Product Recommendations
Best for: Stores that want relevancy-scored recommendations without SaaS pricing.
This is my plugin, so I’ll be upfront about that. I built it because I kept running into the same problem across WooCommerce stores: the default related products are random, and the alternatives are either $500/month SaaS tools or basic “frequently bought together” plugins that don’t score anything.
Refinery Product Recommendations works by analyzing your existing WooCommerce order data to build relevancy scores between products. It processes your order history to find which products actually appear in the same carts, which ones the same customers buy over time, and what’s trending right now versus last quarter.
How the scoring works:
The plugin builds a correlation map from your orders table. If 40% of people who buy Product A also buy Product B, that pair gets a high relevancy score. If Product C is trending this week with 3x its normal sales velocity, it gets a recency boost. If a customer has purchased from you before, their recommendation set gets filtered against their purchase history so they see products they haven’t bought yet.
This isn’t machine learning running on external servers. It’s straightforward data analysis running inside WordPress, using the purchase data WooCommerce already stores. The scoring updates as new orders come in.
What you get:
- Relevancy scoring based on actual purchase correlation
- Browsing pattern analysis for intent-based recommendations
- Trending product detection with recency weighting
- Customer segment awareness (new buyers see different recommendations than repeat customers)
- Multiple display locations: product pages, cart, checkout, shortcode for custom placements
- All data stays in your WordPress database
Pricing: $49-149/year depending on site count. Included in the RefineryWP All Access Pass ($199-399/year for the full plugin suite).
Where it falls short: It’s newer than the competition. Doesn’t have the years of install base that Automattic’s plugin has. If you need AI-driven email recommendations or pop-up widgets, that’s SaaS territory.
Best for: WooCommerce stores doing $10K+ per month that want recommendations based on actual purchase data, not random category matching, and don’t want to pay monthly SaaS fees for it.
2. Product Recommendations by WooCommerce (Automattic)
Best for: Stores that want official Automattic-backed recommendations.
This is the “official” WooCommerce recommendation plugin, built and maintained by Automattic. It extends WooCommerce’s default related products with more intentional logic.
What it does:
The plugin offers several recommendation types: Frequently Bought Together (based on order history co-occurrence), Top Rated products, New Arrivals, Recently Viewed, and custom category-based suggestions. It gives you more control over what shows up compared to the default random shuffle.
The Frequently Bought Together engine analyzes order data to find product pairs that appear together. It’s a step above random, but the logic is binary: products either appear together in orders or they don’t. There’s no scoring gradient that weights how often or how recently those co-purchases happened.
Pricing: $99/year for a single site.
Where it falls short: The recommendation logic is simpler than what you’d get from a dedicated recommendation engine. It identifies co-occurrence but doesn’t weight by frequency, recency, or customer segment. You get “these products were bought together” but not “these products have a 43% purchase correlation and are trending upward this month.” The display options are also more limited.
Best for: Stores that want a basic upgrade from random related products and prefer buying from Automattic directly. If the WooCommerce brand name matters to you for plugin support and compatibility confidence, this is the safe pick.
3. Also Bought for WooCommerce
Best for: Budget-friendly “frequently bought together” without complexity.
Also Bought keeps it simple. It analyzes your order history to find products purchased together and displays them in an “Also Bought” section on product pages. No bells and whistles. No complex configuration.
What it does:
The plugin scans your existing orders and builds co-purchase relationships. When a customer views a product page, it shows other products that previous buyers purchased alongside that product. The logic is straightforward order history analysis.
Pricing: Around $49/year.
Where it falls short: It’s a single recommendation type. You get “frequently bought together” and that’s it. No trending products, no personalization based on customer history, no browsing behavior analysis, no cart or checkout placements. It does one thing and does it adequately, but if you want a full recommendation strategy across your store, you’ll outgrow it quickly.
Best for: Small stores that want a quick improvement over random related products without paying for a full recommendation engine. If your catalog is under 200 products and your monthly order volume is under 500, this handles the basics.
4. Nosto
Best for: Enterprise WooCommerce stores with large catalogs and big budgets.
Nosto is a full personalization platform. Product recommendations are one feature in a larger suite that includes personalized content, pop-ups, email recommendations, and A/B testing. It processes behavioral data on their servers using machine learning algorithms.
What it does:
Nosto tracks browsing behavior, purchase history, and session data to build individual customer profiles. Its ML models generate personalized recommendations that adapt in real time. The engine handles 1:1 personalization at scale, meaning every visitor can see different recommendations based on their behavior.
It also powers personalized email campaigns, dynamic bundles, and segment-based merchandising rules. For stores with 10,000+ SKUs and complex catalogs, Nosto’s ability to surface relevant products from massive inventory is genuinely powerful.
Pricing: Custom pricing, typically $500 to $2,000+ per month depending on traffic and features.
Where it falls short: It’s expensive. For a WooCommerce store doing $30K to $100K per month, spending $500+ monthly on a recommendation SaaS is a tough call. The ROI needs to be substantial to justify that cost. Setup requires integrating their JavaScript snippet and configuring their dashboard, which is a separate system outside of WordPress. Your customer behavioral data also lives on Nosto’s servers, not yours.
Best for: WooCommerce stores doing $500K+ per month with large catalogs, a dedicated marketing team, and budget for enterprise personalization.
5. LimeSpot
Best for: Mid-market stores that want AI recommendations without enterprise pricing.
LimeSpot sits between basic plugins and enterprise SaaS. It offers AI-driven recommendations with more accessible pricing than Nosto, though it’s still a monthly fee.
What it does:
LimeSpot uses behavioral AI to personalize product recommendations across product pages, cart, and email. It supports multiple recommendation types: bought together, viewed together, trending, personalized picks, and post-purchase recommendations. The system learns from browsing and purchase patterns over time.
It also offers A/B testing for recommendation placements and analytics to measure recommendation revenue.
Pricing: Starts at $18/month for basic plans. Meaningful personalization features start closer to $100/month. Pricing scales with traffic.
Where it falls short: Monthly SaaS pricing adds up. At $100/month, you’re spending $1,200/year for recommendations. The lower-tier plans are limited in recommendation types and display options. Like Nosto, it’s a separate platform outside WordPress with its own dashboard and data storage.
Best for: Stores doing $50K to $250K per month that want personalized recommendations and are willing to pay a monthly fee but can’t justify Nosto-level pricing.
How to Choose: Decision Framework
The right plugin depends on 3 things: your monthly revenue, your catalog size, and how much you want to spend.
If you’re doing under $10K/month: Start with Also Bought or Product Recommendations by WooCommerce. Basic co-purchase logic is a massive upgrade from WooCommerce’s random defaults, and the investment is under $80/year.
If you’re doing $10K to $100K/month: This is where Refinery Product Recommendations fits. You need real relevancy scoring, not just co-purchase detection, but you don’t need (or want to pay for) a $500/month SaaS platform. Purchase correlation with frequency and recency weighting, trending detection, and customer-aware recommendations at a flat annual price.
If you’re doing $100K to $500K/month: Evaluate whether a flat-rate plugin or a SaaS tool makes more sense for your catalog size. If you have 5,000+ SKUs and a marketing team that will actively manage recommendation rules, LimeSpot or similar mid-tier SaaS tools start to earn their monthly fee. If your catalog is under 2,000 SKUs, a WooCommerce-native plugin still handles it.
If you’re doing $500K+/month: Enterprise SaaS (Nosto, Clerk.io) starts to make sense. At this revenue level, even a 2% lift from better personalization pays for $2,000/month in tooling. You also probably have the team to manage a separate platform.
If you’re running a CRO program: Recommendations are one of the highest-impact elements to optimize. Before picking a tool, audit how much revenue your current “related products” section is actually generating. Most stores have no idea, and the answer is almost always “not much.”
What to Look for in a Recommendation Plugin
Beyond the specific plugins above, here’s what separates a real recommendation engine from a glorified category filter.
Purchase correlation scoring. The plugin should analyze actual order data to find which products sell together and how frequently. Binary co-occurrence (“these appeared in the same order once”) is different from weighted correlation (“these appear together in 40% of orders”). Weighted scoring produces better recommendations.
Recency weighting. Products that are selling together this month should rank higher than products that sold together 8 months ago. Seasonal patterns, new product launches, and shifting customer preferences all make recency matter.
Customer awareness. A repeat buyer who’s purchased 5 products from you should see different recommendations than a first-time visitor. At minimum, a recommendation engine should exclude products a customer already owns. For stores running subscriptions, recommendations should also factor in active subscriptions so you’re not pushing products a customer already receives monthly. Pairing recommendations with a subscription retention strategy compounds the cross-sell opportunity.
Multiple placement options. Product pages are the obvious location, but cart and checkout recommendations often convert better because the customer is already in buying mode. A good side cart with recommendation widgets is one of the fastest ways to increase AOV.
Measurable results. Track recommendation click-through rate (target: 5 to 15% vs. 1 to 3% for random), add-to-cart rate from recommendations, and revenue attributed to recommended products. If you can’t measure it, you can’t improve it.
The Revenue Case for Better Recommendations
Here are the numbers that matter.
Default WooCommerce related products get 1 to 3% click-through rates because random suggestions rarely match buyer intent. Relevancy-scored recommendations get 5 to 15% click-through rates because the products actually make sense together.
Even modest math tells the story. If your store gets 10,000 product page views per month and your average order value is $75:
- Default related products at 2% CTR and 10% conversion: 20 additional orders, $1,500/month
- Scored recommendations at 8% CTR and 15% conversion: 120 additional orders, $9,000/month
That’s a $7,500/month difference from changing one section on your product pages. The actual numbers will vary based on your catalog, traffic, and pricing, but the directional difference between random and scored is consistent.
If you’re managing site operations for a WooCommerce store and haven’t audited the recommendation section, that’s one of the fastest wins available. It doesn’t require a redesign, a new theme, or a platform migration. Just replace the random engine with one that actually scores products.
The Bottom Line
WooCommerce’s built-in Related Products feature is random. It’s been random for years. It will probably stay random because WooCommerce core treats recommendations as a display feature, not a revenue feature.
Every store I’ve looked at that switches from default related products to any scored recommendation engine sees measurable improvement. The specific plugin matters less than the shift from random to intentional.
That said, the options break down clearly:
- Best overall: Refinery Product Recommendations. Relevancy scoring with purchase correlation, recency weighting, and customer awareness at a flat annual price. No monthly SaaS fees, no external data processing.
- Best for Automattic loyalists: Product Recommendations by WooCommerce. Simpler logic, but official backing and guaranteed compatibility.
- Best budget option: Also Bought for WooCommerce. One recommendation type, done simply.
- Best for enterprise: Nosto. Full personalization platform with ML. Budget required.
- Best mid-market SaaS: LimeSpot. AI recommendations without enterprise pricing, though monthly costs add up.
Your product pages are showing recommendations right now. The question is whether those recommendations are based on data or based on random selection. For most WooCommerce stores, the answer is random. And that’s costing more than you think.
Mike Valera builds WooCommerce growth tools at RefineryWP.