Winning a new customer is expensive; selling more to the ones who already buy is the fastest way to improve margin. Suggested products in CommerceUp are not simple “others also bought” banners. They are strategic cross-sell recommendations on the cart or product page, designed to raise average ticket without adding friction for the B2B buyer.
Suggested vs. substitutes: what is the difference?
| Feature | Purpose | Cart example |
|---|---|---|
| Substitutes | Mitigate stockouts. | The customer orders Blue Ink (out of stock); we suggest Blue Ink Brand B. |
| Suggested | Raise ticket (cross-sell/upsell). | The customer orders Blue Ink (in stock); we suggest cartridges, reams, or cleaner. |
Profitable use cases
- Complementary products (cross-sell): The “fries and soda” of your industry. Screws with nuts, drills with bits, coffee with disposable cups.
- Habitual replenishment: For the distributor who buys supplies every month. The portal plays the sales rep who says “Don’t forget the thermal rolls.”
- Launch boost: An organic way to introduce a new line to customers who already trust an established brand.
Golden rules for configuration
- Commercial logic, not filler: Do not associate products at random. There must be a purchase correlation or a margin incentive for your business.
- Less is more: We recommend showing 2 to 4 well-profiled suggested products. Too many options freeze the decision.
- B2B precision: In CommerceUp, the suggested product always shows the price from the logged-in customer’s list, avoiding pricing confusion.
Delegating cross-sell to system rules ensures the suggestion happens 100% of the time — even if your customer buys at 3 a.m. on a Sunday.
Discover CommerceUp B2BConfigure substitutes
Frequently asked questions
Are suggestions shown if the original product is out of stock?
That is where substitutes come in. Suggested products are used to add extra items to the main order.
Can I automate what to suggest?
You can configure precise manual rules. The SourcingUp team also implements AI agents that analyze purchase history to optimize these recommendations dynamically.