TL;DR
- A good match is not just shared category interest. It is shared category, country, timing, price, MOQ, readiness, and relationship context.
- The scorecard should support manual matching before automation and later inform product recommendations.
- Negative feedback is as important as positive intent because it teaches the marketplace why matches fail.
Why category matching is not enough
A buyer interested in resortwear and a brand selling resortwear may still be a poor match. The buyer may need immediate delivery while the brand is prebook only. The buyer may target opening orders under $2,000 while the brand requires $10,000. The buyer may be in a country the brand cannot serve reliably.
The marketplace should treat category as the starting point, not the recommendation.
The core dimensions
- Category fit: buyer demand overlaps brand assortment.
- Country fit: the brand can reasonably serve the buyer's market.
- Price fit: wholesale and MSRP match the buyer's customer and margin structure.
- MOQ fit: minimums are realistic for the buyer type.
- Timing fit: ready-to-ship or prebook windows match the buyer's open-to-buy.
- Readiness fit: the brand's operations can support the buyer's requirements.
- Relationship fit: existing rep, showroom, referral, or direct brand relationship helps trust.
- Strategic fit: the match helps build density in a target launch market.
A manual scorecard the team can use now
- Category overlap: 0 to 20 points.
- Country or shipping fit: 0 to 15 points.
- Price architecture fit: 0 to 15 points.
- MOQ and opening order fit: 0 to 10 points.
- Delivery timing fit: 0 to 15 points.
- Brand readiness: 0 to 15 points.
- Relationship or referral context: 0 to 10 points.
- Launch-market priority: 0 to 10 points.
The exact weights can change. The discipline is to score the reason for the match, not just the existence of a match.
How to capture match outcomes
Every manual match should produce an outcome record. Otherwise, the team will know that matches were created but not whether they were useful. The outcome does not need to be complicated at first. It needs to separate positive, neutral, and negative signals.
- Requested intro.
- Requested samples.
- Saved for later.
- Asked for pricing access.
- Not a fit: price.
- Not a fit: category.
- Not a fit: delivery window.
- Not a fit: MOQ.
- No response.
How this becomes recommendation logic later
The first recommendation engine is the operations team. If the team logs matches and outcomes consistently, product recommendations later have real learning data. The marketplace can learn which buyer types respond to which price bands, which categories need ready-to-ship depth, which countries require local fulfillment, and which rep relationships increase response.
This is why manual matching belongs inside the product data model rather than in a disconnected spreadsheet.
Buyer-brand matching should start manual and become smarter. The scorecard is the bridge between human judgment and future automation.