Furniture is a considered, high-value purchase. Shoppers rely on visual inspiration, dimensions, materials, and style compatibility, and their choices mix practical needs with how they want their space to feel.
This article shows how to use Smart Recommender in Insider One to inspire browsing, surface matching pieces, and help shoppers furnish a full room. It includes an A/B test walkthrough for room-set cross-sells and strategies for each page type.
Before you start: Make sure your product catalog includes the attributes this article filters on, such as Category, Style, Room Type, Season, and Set ID. Create missing custom attributes on the Product Attributes page and mark them as filterable.
What you can achieve with Smart Recommender
Picks for every room
Show recommendations by preferred room type, style, or color, such as Modern Living Room ideas.
Complete the look
Show complementary pieces, such as a coffee table and lamp next to a sofa.
Style alternatives
Show other styles or colors of the product being viewed.
Best-selling pieces
Feature top-performing products to guide first-time buyers.
Pick up where they left off
Show returning shoppers the items they viewed before.
Checkout add-ons
Suggest rugs, side tables, or lighting that match the cart to raise average order value (AOV).
Style affinity
Personalize by room preference, materials, and styles such as Scandinavian or Rustic.
A/B testing
Test widget visuals, product card details, and placement.
Walkthrough: Cross-sell full room sets
Furniture shoppers often furnish a whole room rather than buy a single item. Recommending matching pieces raises AOV and makes planning easier.
This walkthrough tests two cross-sell strategies on sofa product detail pages:
Variant A: Recommend coffee tables in the same style.
Variant B: Recommend rugs in the same style.
Step 1: Create the recommendation strategies
Navigate to Recommendation Strategies and click Create. Configure the first strategy as follows:
Select Product Detail Page as the page type.
Select Purchased Together as the algorithm.
Enter the number of products to recommend.
Exclude products purchased in the last four weeks.
Add a filter for the category attribute: Category + is + Coffee Tables.
Add a second filter: Style + matches the item they're currently viewing.
If you do not have a Style attribute, create it on the Product Attributes page (for example, Scandinavian, Minimalist, or Rustic).
Create the second strategy with the same settings and change only the category filter. Keep the Style filter:
Category + is + Rugs.
Note: Keep every other setting identical between the two strategies. If more than one setting changes, you cannot tell which change caused the difference in results.
Step 2: Launch the campaign
Navigate to Web Smart Recommender and click Create.
Select the integration method for the widget.
On the Segments step, select customers with a low AOV under Purchasing Behavior and set your threshold.
On the Rules step, use Page Rules to target the product detail pages of sofas.
On the Design step, assign one strategy to each variant and set the traffic allocation for each variant.
Click Edit Design to open the Advanced Product Card Designer and customize your product cards.
Select the locales and stores where the campaign appears.
Review the campaign details, confirm that every setting matches your goal, and click Launch.
Note: Every attribute you show on a product card must exist in your product catalog. To show more information, create custom attributes on the Product Attributes page.
Step 3: Track campaign metrics
Navigate to Smart Recommender Analytics.
Find your campaign in the Campaign and Variant Metrics table. When the test period ends, click the campaign name.
Compare Direct Revenue, Average Order Value (AOV), and Conversion Rate for the two variants. This shows whether coffee tables or rugs raise AOV more for low-spending customers.
If one variant clearly wins, set its traffic allocation to 100% so every visitor sees the better strategy.
After you find a winner, test other pairings, such as lamps or side tables.
Step 4: Optimize your campaigns
After the campaign has run long enough to collect data, use Smart Recommender Analytics to improve it:
Engagement funnel: Review product impressions, click-through rate, add-to-cart rate, and conversion rate to see where shoppers drop off.
Campaign and variant performance: Compare AOV, Conversion Rate, and Direct Revenue in the Campaign and Variant Metrics table, then refine or scale the winning variant.
Product-level impact: Open Top 100 Product Analytics to see which recommended products perform best. Give extra visibility to strategic products that perform below expectations.
Category trends: Use Category Analytics to find the categories that drive the most conversions, and decide whether to prioritize them or promote weaker ones.
Use cases by page type
Home page
Seasonal refresh picks
Show the most viewed seasonal pieces, such as patio furniture in summer.
Create a strategy with the Most Popular algorithm.
Turn on the Enhance recommendations based on Attribute Affinity toggle.
Add the filter Season + is + Summer.
If you do not have a Season attribute, create it on the Product Attributes page.
Tip: A/B test seasonal visual cues and the widget position (hero area versus mid-page).
Room starter sets
Help new shoppers furnish a room with best-selling sets, such as Living Room Essentials.
Create a strategy with the Top Sellers algorithm.
Add two filters: Category + is + Starter Sets, and Room Type + is + Living Room. Connect them with an AND connector.
Target new users on the Segments step.
Note: To recommend the rest of a set, create a custom attribute called Set ID on the Product Attributes page and give every item in a set the same value (for example, Living Room Set 01).
Tip: A/B test headlines such as "Build Your Dream Living Room" and "Your Living Room Starts Here."
Recently viewed items
Remind returning shoppers of sofas, nightstands, or chairs they explored before.
Create a strategy with the Recently Viewed algorithm.
Exclude products the user already purchased.
Target returning users on the Segments step.
Note: Recently Viewed and User Based rely on each user's past behavior. Target returning users so these widgets always have data to show.
Tip: A/B test Recently Viewed against User Based.
Product detail page
Complete the look
Show pieces commonly bought with the item, such as cushions, rugs, or coffee tables with a sofa.
Create a strategy with the Purchased Together algorithm.
Add the filter Category + is one of + Rugs, Coffee Tables, Side Tables, Cushions.
Leave Attribute Affinity off.
Tip: Try headlines such as "Customers Often Add These."
Picks based on recent browsing
Show each shopper products that match their most recent interactions.
Create a strategy with the User Engagement algorithm.
Exclude recently purchased products.
More for this room
Show more pieces for the same room or furniture type.
Create a strategy with the Trending Products algorithm.
Add two filters: Room Type + matches the item they're currently viewing, and Category + contains + the category they're currently viewing. Connect them with an OR connector.
If you do not have a Room Type attribute, create it on the Product Attributes page.
Category page
Deals in this category
Show the most discounted items in the current category, such as beds on the beds page.
Create a strategy with the Highest Discounted algorithm.
Add the filter Category + matches + the category they're currently viewing.
Leave Attribute Affinity off.
Tip: Add badges such as "30% Off" and A/B test the widget at the top versus the bottom of the page.
Best sellers in this category
Show the best-selling products in the current category.
Create a strategy with the Top Sellers algorithm.
Add the filter Category + contains + the category they're currently viewing.
If your categories are hierarchical, select the Expand the category filter to include recommendations from the next category if there aren't enough products to display checkbox, so the widget still fills when the current category has few products.
Turn on the Enhance recommendations based on Attribute Affinity toggle. This prioritizes preferred styles and colors.
Cart page
Finish the set
Suggest matching side tables, lamps, or décor items.
Create a strategy with the Most Popular algorithm.
Add the filter Category + is one of + Lighting, Side Tables, Wall Décor, Accessories.
Leave Attribute Affinity off.
Tip: A/B test headlines such as "Complete the Look" and "Add the Final Touches."
Reach free shipping
Show items that help shoppers reach your free shipping threshold.
Create a strategy with the Checkout Recommendation algorithm and set the spend threshold (for example, 500 USD).
No filters are required. Attribute Affinity is optional.
Tip: If you do not offer free shipping, use Purchased Together instead.