This guide helps Shopify and TikTok Shop clothing brands choose a workable initial range. It also applies when an existing brand is adding a new category with limited sales history.
What should each garment contribute?
Give every proposed style a reason to belong. It might express the brand’s main idea, meet an established customer need, complete an outfit or test a specific opportunity. These are planning roles, not a required collection formula.
Write one sentence for each: “This garment is for [customer], who wants [use or experience], and will choose it because [specific reason].” If two styles make the same promise, ask whether both need to launch together.
Scroll sideways to view all columns →
| Proposed role | What to investigate |
|---|---|
| Main expression of the brand | Does the garment demonstrate the fit, material or design promise clearly? |
| Companion piece | Does it support a real customer outfit or use, with its own reason to buy? |
| Existing-customer request | What evidence supports the request at the intended price and size range? |
| New opportunity | What remains unproven, and how much cash can you commit to learning? |
Shopify’s assortment-planning guide distinguishes the variety of products from the variations within them. For clothing, that distinction makes the colour and size decisions part of the range plan, rather than an afterthought.
How many variants are you creating?
Count the combinations before approving the range. Each style-colour-size combination is a separate inventory variant for this planning exercise. The total garment count does not tell you how much depth each variant will have.
Consider these illustrative plans, each with 300 garments:
Scroll sideways to view all columns →
| Plan | Styles | Colours per style | Sizes per colour | Variant combinations | Average units per combination |
|---|---|---|---|---|---|
| A | 2 | 2 | 5 | 20 | 15 |
| B | 3 | 3 | 5 | 45 | About 6.7 |
Plan B spreads the same quantity across more than twice as many combinations. The averages are arithmetic, not recommended size allocations. Actual quantities should reflect customer evidence and the supplier’s permitted splits.
More combinations may give customers useful choice, but they can also leave too little depth in the sizes people want. Plan A may concentrate stock more clearly, but that does not make it automatically right for your audience. The decision is which choices matter enough to justify their inventory.
How should you decide the size split?
Use evidence relevant to your customer and product. If you have sales history, examine units sold by size alongside stock availability, returns and fit feedback. A size that sold fewer units because it was unavailable is not evidence of weak demand.
For a first launch, be explicit about what you do not know. Gather information around the intended garment, price and sizing approach. Generic follower demographics cannot tell you how many units of every size will sell.
You can use a provisional size allocation, but label it as a hypothesis and discuss it with your production partner. Check whether the proposed measurements and grading support the customer promise before using that size range in public.
Do not remove sizes merely to make the spreadsheet simpler. Consider your intended customer, fit responsibilities and production feasibility together. If the proposed launch cannot serve that customer well, revise the plan deliberately.
Is the demand evidence strong enough?
Separate attention, stated interest and purchases. A trending video can help identify a product question; it cannot establish the sales your own range will achieve.
TikTok Shop’s Product Opportunities guidance provides discovery signals. For your decision, connect a signal to the market, date, garment, price and audience you are actually serving.
Look for agreement between several relevant observations: existing orders, customer requests, fit feedback, interest at a stated price and a clear use for the garment. Record contrary evidence too. Customers asking for a different sleeve length or price point may change the specification rather than simply justify more units.
If you lack buying evidence, use that uncertainty to size the commitment. Do not call a product a bestseller or forecast a sell-out before it has earned that description.
Does the assortment fit your production cash?
Calculate the commitments for each style and colour, then add them together. Supplier minimums, separate material purchases and development costs may make the total different from “garment count × unit price.”
Illustrative comparison: 300 garments with an assumed landed-cost basis of US$22 require US$6,600. Expanding to 450 garments at the same assumed basis requires US$9,900: US$3,300 more before separately charged development or other expenses. This is arithmetic, not a real quotation or recommended order.
Compare that extra commitment with the evidence for the added products and the cash needed for fulfilment, marketing and a possible reorder. A larger collection that consumes the entire budget can leave little room to respond when one style performs better than expected.
Use the MOQ guide to record material constraints and the cost guide to test selling economics. Avoid counting the same material purchase twice.
What should be decided before you add another style?
Create a short assortment sheet containing each style’s customer purpose, price, colour/size plan, evidence, development status, required purchase and unresolved risk. Add a proposed reorder trigger and the supplier conditions you still need confirmed.
Then assess the weakest row. You may need to improve the garment, validate its price, simplify its colours or delay it while another product moves ahead. The objective is a range with a clear purpose and an affordable learning plan.
Frenzee is being built to connect merchandising intelligence, sourcing and production so those choices can stay together. Get an invite. Chat and the workspace are coming soon.