AI vs Custom Product Recommendation Rules in WooCommerce

Recommending the right products can help WooCommerce stores increase order value and make shopping easier for customers. When shoppers find relevant alternatives, complementary products, or useful extras, they have more reasons to explore the catalog and add items to their carts.

However, not every store needs the same recommendation strategy. Some businesses want AI to suggest relevant products automatically, while others prefer to control exactly which products appear and how they are sorted.

This is where AI-powered recommendations and custom product recommendation rules come in. Both approaches help store owners display upsells and cross-sells, but they differ in how recommendations are selected and managed.

In this guide, we compare both approaches, explain when to use each one, and show how AI product recommendations for WooCommerce can help you build a flexible product recommendation strategy.

What Are Product Recommendations in WooCommerce?

Product recommendations are suggestions that guide customers toward other products they may want to purchase. They can appear on product pages, cart pages, checkout pages, or other locations across your WooCommerce store.

For example, a customer viewing a laptop might see a more powerful model as an alternative. A shopper adding a camera to their cart might see a compatible memory card or camera bag.

WooCommerce supports related products, upsells, and cross-sells. Each serves a different purpose.

• Upsells: Suggest alternatives or upgrades that may better meet a customer's needs, often at a higher price.
• Cross-sells: Recommend complementary products that customers may want to purchase alongside their selected items.
• Related products: Display other products associated with the item being viewed, often based on shared categories or tags.

A WooCommerce product recommendation strategy helps store owners decide which products to display, where to display them, and how to select relevant suggestions.

Traditional product linking can work well for small catalogs. However, managing recommendations across hundreds or thousands of products can become time-consuming. AI and custom recommendation rules offer different ways to manage this process.

What Are AI Product Recommendations?

AI product recommendations for WooCommerce use artificial intelligence to identify products that may be relevant to a customer's current shopping context. Instead of requiring store owners to manually select every recommended product, an AI-powered system can generate suggestions based on available product information and its recommendation logic.

For example, a customer viewing a coffee machine might receive suggestions for compatible accessories or alternative machines. The system can use product details and the information available to it to identify potentially relevant options.

With AI product recommendations for WooCommerce, store owners can automate parts of the recommendation process and reduce the need to create individual product associations manually.

The exact results depend on the plugin, AI model, product data, and recommendation settings. AI recommendations should be tested to ensure they are relevant to the products being viewed and the customer's shopping needs.

How AI Recommendations Work

The process generally involves the following steps:

1. The store owner connects the supported AI service and configures the recommendation settings.
2. The recommendation system accesses the product information needed for its task.
3. The AI generates product suggestions based on the available information and configured recommendation type.
4. The store displays the recommendations at selected locations.

This approach is useful for stores with large catalogs or frequently changing product selections. It can reduce manual work while helping customers discover products they might otherwise overlook.

Benefits of AI Recommendations

Less manual product selection: Store owners do not need to define every product relationship individually when AI can generate suggestions.

Flexible product discovery: AI can identify potentially relevant products based on product information rather than relying exclusively on manually assigned links.

Support for larger catalogs: Automated recommendations can simplify product merchandising when a store carries many products.

More efficient upselling and cross-selling: AI can generate suggestions for different recommendation types, helping store owners present alternatives and complementary products.

However, AI recommendations are not always the best choice for every scenario. Store owners may need to review the results, manage API costs, and provide accurate product information to maintain recommendation quality.

What Are Custom Product Recommendation Rules?

Custom product recommendation rules let store owners define how products are selected and displayed. Instead of relying on AI to generate the recommendations, you configure filters and ranking factors to determine which products qualify and how they appear.

For example, an electronics store might create a rule that recommends featured accessories priced between $20 and $60. Another rule could display top-rated products from a selected category.

Custom rules provide predictable results because the store owner controls the selection criteria. They are particularly useful when recommendations must follow specific merchandising plans, price limits, stock requirements, or promotional priorities.

How Custom Recommendation Rules Work

Custom recommendations generally follow these steps:

1. Create a recommendation rule and choose its type, such as upsell or cross-sell.
2. Select where the recommendations should appear.
3. Define product filters to determine which items qualify.
4. Choose ranking factors to control the order of the results.
5. Configure the display settings and activate the rule.

For example, you could create a cross-sell rule that selects in-stock products from a particular category and sorts them by sales, from highest to lowest.

Custom rules offer direct control over the recommendation process. However, store owners must decide which filters and ranking factors make sense for their products and customers.

Benefits of Custom Recommendation Rules

Greater control: You decide which products qualify for recommendations and how the system sorts them.

Consistent results: Products are selected according to your defined conditions rather than depending on AI-generated suggestions.

Support for merchandising strategies: You can prioritize featured products, sale items, specific categories, or products within a chosen price range.

Flexible product placement: Rules can be configured for different locations, allowing you to use separate strategies on product, cart, and checkout pages.

Easier troubleshooting: When recommendations are unexpected, you can review the rule's filters, ranking factors, and priority settings to identify the cause.

Custom rules are especially useful when your store has clear product relationships or specific business requirements. The main limitation is the time needed to create and maintain rules as your catalog and merchandising strategy change.

AI vs Custom Recommendations: Key Differences

Both approaches can support upselling and cross-selling, but they provide different levels of automation and control.

The main difference is that AI automates product suggestions, while custom rules let store owners define the selection process.

Neither approach is universally better. AI is useful when you want to reduce manual work, while custom rules are preferable when you need specific products to appear under clearly defined conditions.

When Should You Use AI Recommendations?

AI recommendations are useful when you want to automate product suggestions and make product discovery easier without manually defining every relationship.

1. You Have a Large Product Catalog

Managing product recommendations manually becomes difficult when your store has hundreds or thousands of products. You may need to identify suitable alternatives, complementary products, and related items for different product categories.

AI can help automate this process by generating suggestions from available product information. This reduces the need to manually configure every product association.

For example, a home accessories store could use AI recommendations to suggest complementary items for different furniture products.

2. Your Products Have Complex Relationships

Some products have relationships that are not obvious from their categories or tags. Customers may need accessories, compatible products, or alternatives based on features and intended use.

AI can help identify potential relationships when the relevant information is available. For instance, a product description might mention compatibility with another item, allowing the recommendation system to consider that relationship.

You should still check the suggestions carefully. AI-generated recommendations may not always understand technical compatibility or product restrictions correctly.

3. You Want to Reduce Manual Work

If your team spends considerable time maintaining product links, AI recommendations can reduce some of that workload.

Instead of selecting every recommended product manually, you can configure AI-powered rules and let the system generate suggestions. Your team can then review the results and focus on improving product data or refining the overall strategy.

This is especially useful for stores that frequently add new products or update their catalogs.

When Should You Use Custom Recommendation Rules?

WooCommerce product recommendation rules are better suited to stores that require precise control over product selection and display.

1. You Need to Promote Specific Products

Suppose you want to promote selected products during a seasonal campaign. You may need recommendations to include only featured products or items from a particular category.

Custom rules allow you to define filters that restrict the eligible products. This helps ensure the recommendations support your promotional strategy.

For example, a clothing store could recommend selected winter accessories on relevant product pages without including unrelated products.

2. You Have Clear Pricing Requirements

Price can influence which products you want to recommend. You might want to display affordable accessories alongside a main product or suggest more expensive alternatives as upsells.

Custom rules can filter products by price range and rank them according to price or other supported factors.

This gives you greater control over the types of offers customers see. It can also help keep recommendations aligned with your product positioning and pricing strategy.

3. You Want Predictable Product Selection

Some stores need consistent recommendations to support established merchandising plans. For example, a store might want to display featured products first or prioritize items with strong sales.

Custom rules allow you to select ranking factors such as popularity, price, rating, sales, stock, and newness. You can also choose the sorting direction.

Because the rules follow defined conditions, they are useful when you want to understand why particular products appear in a recommendation section.

AI vs Custom Rules: Which Is Better for Upselling and Cross-Selling?

The right approach depends on the type of recommendation you want to display and how much control you need.

AI for Automated Upselling

Upselling encourages customers to consider an alternative or upgrade. AI recommendations can help identify potentially relevant products based on available catalog information.

For example, a shopper viewing a basic office chair might receive suggestions for alternatives with additional features. The store owner can configure an AI recommendation rule and review whether the suggested products are appropriate.

AI can reduce manual selection, but it does not guarantee that every suggestion will be more valuable or suitable for the customer.

Custom Rules for Controlled Upselling

Custom rules are useful when upsells must follow specific requirements. You could select products from a particular category, set a price range, and rank eligible products according to price or rating.

This works well when you have a defined upgrade strategy and want to maintain consistent product selection.

AI for Complementary Cross-Sells

Cross-selling helps customers discover products that complement their current selection. AI can generate suggestions based on available product information and configured recommendation logic.

For example, a shopper buying a coffee machine might receive recommendations for coffee filters or cleaning accessories if the product information supports those relationships.

Review the recommendations to ensure the products are compatible and genuinely useful.

Custom Rules for Targeted Cross-Sells

Custom cross-sell rules are useful when you know which products or categories should be promoted together. You can also configure supported options to consider the categories of products currently in the cart or the customer's purchase history.

For example, a sports store could recommend selected accessories when a customer adds running shoes to the cart.

The best approach depends on whether you prioritize automated suggestions or strict control over the products being displayed.

How to Create Product Recommendation Rules in WooCommerce

The Extendons AI Product Recommendations - Upsell/Crosssell plugin lets you create AI-powered and custom recommendation rules from one place.

Follow these steps to configure a recommendation rule.

Step 1: Install and Configure the Plugin

Download the plugin ZIP file from your WooCommerce account. In your WordPress dashboard, go to Plugins → Add New → Upload Plugin.

Upload the ZIP file, click Install Now, and activate the plugin.

Next, navigate to WooCommerce → Settings → Product Recommendations to access the configuration options.

If you want to use AI-powered recommendations, enter your OpenAI API key under the AI configuration settings. Select a supported model, save the settings, and validate the connection.

Custom recommendation rules do not require AI-generated product selection, so you can use custom mode when you prefer to control recommendations manually.

Step 2: Create a Recommendation Rule

Open the Recommendation Rules tab and click Add New Rule.

Give the rule a descriptive name so you can identify its purpose later. Enable the rule and configure its priority if multiple rules may apply.

Choose the recommendation mode:

• AI: Use AI-powered product suggestions.
• Custom: Define product selection filters and ranking factors.

Then select the rule type. Choose upsell for alternative or upgrade suggestions, or cross-sell for complementary products.

Step 3: Choose the Recommendation Placement

Select where the recommendations should appear. The plugin supports the following placements:

• Product page
• Cart page
• Checkout page

Consider the customer's shopping context when selecting a placement.

For example, product pages are useful for suggesting alternatives, while cart and checkout pages can help shoppers discover complementary items before completing their purchases.

You can configure different rules for different placements to support separate merchandising strategies.

Step 4: Configure Custom Product Filters

If you select custom mode, define which products qualify for the recommendations.

The plugin supports filters for:

• Category
• Stock status
• Product type
• Tags
• Price range
• Featured status
• On-sale status

You can add multiple filters to narrow the selection. Products must satisfy all configured conditions to qualify.

For example, you could recommend in-stock, featured accessories from a selected category within a specific price range.

Step 5: Set Ranking Factors

After filtering the products, configure how they should be sorted.

Available ranking factors include popularity, price, rating, sales, stock, and newness. You can choose whether each factor sorts from high to low or low to high.

For example, a store promoting popular products might rank items by sales from high to low. A store prioritizing affordable accessories could sort eligible products by price from low to high.

Ranking controls help you decide which qualifying products appear first.

Step 6: Configure Cross-Sell Options

For cross-sell rules, you can enable options to include products from the same categories as items in the cart or consider the customer's purchase history.

These settings help align cross-sell suggestions with the customer's current or previous shopping activity.

Enable the options that fit your recommendation strategy and the data available to your store.

Step 7: Customize the Display

Set the widget title and description to explain why the products are being recommended.

You can also choose how many products to display and whether to show product prices and Add to Cart buttons.

If you want to display WooCommerce product recommendation in a specific page or post, copy the shortcode generated for that rule and add it to the relevant location.

Save the rule and test it on your storefront to confirm that the recommendations appear as expected.

How to Choose the Right Recommendation Strategy

Before selecting AI or custom rules, consider the size of your catalog, your merchandising requirements, and the amount of control you need.

Consider Your Catalog Size

A large catalog may benefit from AI-generated suggestions because manually defining every product relationship can take considerable time.

A smaller catalog with clear product relationships may work well with custom rules. You can define the selection criteria once and maintain them as needed.

Evaluate Your Control Requirements

If you need recommendations to follow specific category, stock, or price conditions, custom rules offer more direct control.

If you want the system to generate suggestions automatically, AI mode may be more appropriate. You should still review the recommendations to ensure they align with your business goals.

Match Recommendations to Shopping Intent

Think about what customers need at each stage of their journey. Product pages can support alternative product suggestions, while cart pages can present complementary items.

Use recommendation types and placements that make sense for the products being viewed. Avoid displaying unrelated suggestions simply to fill a recommendation section.

Test and Review Performance

Monitor how customers interact with your recommendations. Review product clicks, Add to Cart activity, conversions, and average order value where suitable analytics are available.

Compare different rules and placements to understand which combinations perform best. If a recommendation section receives little engagement, review the products selected, widget title, placement, and number of items displayed.

Remember that recommendations alone do not guarantee higher sales. Product relevance, pricing, page performance, and the overall shopping experience all influence results.

Conclusion

AI and custom product recommendation rules offer two useful ways to improve product discovery in WooCommerce. AI helps automate product suggestions, while custom rules provide greater control over product selection and ranking.

The right choice depends on your catalog, merchandising strategy, and support requirements. If you want automated suggestions, AI mode can reduce manual work. If you need recommendations to follow specific conditions, custom rules offer predictable results.