Variants, bundles & collections
Why this matters for your business
Three catalog concepts that done well drive significant revenue, done badly create operational chaos:
- Variants — a t-shirt has 4 sizes × 3 colors = 12 SKUs. Generating these manually is tedious; mismanaging inventory per variant causes oversells.
- Bundles — "Buy 3, save 15%" or "Skincare routine bundle" — proven to lift AOV. But bundle inventory math (when you sell a bundle, decrement components) is notoriously error-prone.
- Collections — "Summer 2026", "Under $50", "Vegan" — manually maintained collections drift; rule-based ones stay current.
The platform handles all three: variant matrices generated + managed; bundle pricing + inventory bound automatically; collections built by rules that re-evaluate as catalog changes.
What this typically unlocks
| Outcome | Result |
|---|---|
| Time per multi-variant product | 5 min vs. 30 min |
| Bundle AOV uplift | +15-25% |
| Collection maintenance | 0 hours vs. weekly manual |
| Variant inventory accuracy | near 100% |
What you actually get
Variants
| Capability | Description |
|---|---|
| Matrix builder | Define dimensions (size × color × material); auto-generate all SKUs |
| Per-variant pricing | Override base price for specific variants |
| Per-variant inventory | Track individually; sync to all channels |
| AI-suggested variants | "Most stores like yours offer these sizes too" |
Bundles
| Capability | Description |
|---|---|
| Component-based | Bundle = N components; inventory math automatic |
| Bundle pricing | Discount % off component sum; or fixed price |
| Mix-and-match | "Pick any 3 from this set for $X" |
| Auto-suggestions | "Customers who bought A also bought B" → bundle suggestion |
Collections
| Capability | Description |
|---|---|
| Rule-based | "All vegan + under $50 = Vegan Affordable collection" |
| AI-suggested | "These 12 products would make a great holiday gift collection" |
| Auto-maintenance | New products that match rule auto-add |
| Manual override | Pin specific products; exclude others |
Real merchant scenarios
Scenario A — Apparel brand handles 12-variant SKUs
Setup. Brand selling t-shirts. 4 sizes × 3 colors = 12 variants per design × 50 designs = 600 SKUs.
Pre-platform: Manual variant configuration per design took ~30 min × 50 = 25 hours.
With matrix builder: 5 min × 50 = ~4 hours. Saved 21 hours.
Inventory: All 600 variants tracked individually; oversell rate near 0 across multichannel.
Scenario B — Bundles drive 22% AOV lift
Setup. Skincare brand created "Daily routine bundle" (cleanser + serum + moisturizer) at 12% off component sum.
Result over 60 days:
- Bundle units sold: 380
- Bundle AOV: $94 (vs. $52 single-product AOV)
- Bundle attach rate (cart with bundle vs. without): +22% AOV across all carts
Scenario C — Auto-maintenance collection
Setup. "Under $50" collection. Manually maintained; products drifted out as prices changed.
Switched to rule-based: "All products where price < 50 AND in stock."
Result: Always current. New products that meet criteria auto-add. Products that go above $50 auto-remove. Zero maintenance.
Best practices
✅ Use matrix builder for any multi-variant product. Saves hours.
✅ Bundle pricing should leave merchants ahead vs. components sold separately. Verify AOV uplift compensates discount.
✅ Use rule-based collections wherever possible. Manual collections drift.
❌ Don't create variants for slight differences. Color variants make sense; "extra strong" vs. "strong" usually doesn't.
❌ Don't ignore bundle inventory math. Sell a bundle, all components decrement. Misconfiguration causes oversells.
Plan tiers
| Capability | Free | Starter | Pro | Agency | Enterprise |
|---|---|---|---|---|---|
| Variant matrix builder | ✓ | ✓ | ✓ | ✓ | ✓ |
| Per-variant pricing | ✓ | ✓ | ✓ | ✓ | ✓ |
| Bundles (component-based) | — | ✓ | ✓ | ✓ | ✓ |
| Mix-and-match bundles | — | — | ✓ | ✓ | ✓ |
| Rule-based collections | — | ✓ | ✓ | ✓ | ✓ |
| AI variant suggestions | — | — | ✓ | ✓ | ✓ |
| AI collection suggestions | — | — | ✓ | ✓ | ✓ |
| Multi-shop catalog | — | — | — | ✓ | ✓ |