discount-optimizer
Discount Optimizer
Promotional discounts are one of the fastest ways to drive volume on TikTok Shop — but cutting price without a margin model turns short-term spikes into long-term losses. This skill calculates mathematically grounded discount levels by working from your unit economics upward, ensuring every promotion moves product while protecting profit.
Use when
- You are planning a TikTok Shop flash sale or platform-wide campaign and need to set a discount percentage that hits a GMV target without pushing gross margin below a minimum acceptable threshold.
- You are evaluating whether a stacked promotion — platform voucher, affiliate commission, or creator coupon code — is still profitable after layering all cost components together.
- You have aging inventory above a target days-on-hand threshold and want to find the minimum effective discount depth to accelerate sell-through before a reorder cycle.
- You are comparing multiple discount mechanics such as percentage off, bundle pricing, or buy-one-get-one to determine which structure maximizes contribution margin at a given sell-through volume target.
What this skill does
Discount Optimizer takes your unit cost, current list price, minimum acceptable margin percentage, target conversion rate uplift, and inventory velocity data as inputs. It then runs a margin-preserved discount calculation that identifies the maximum allowable discount ceiling, models expected volume uplift using your conversion rate assumptions, and outputs a ranked set of discount scenarios with projected margin impact, break-even sell-through volumes, and a recommended promo price. Each scenario clearly shows the discounted price, resulting margin percentage, estimated units sold at projected conversion uplift, total contribution margin generated, and a break-even comparison against running no promotion. When stacked promotions including platform vouchers, creator commissions, and ad spend cost per order are included as inputs, the skill incorporates all of those cost layers before producing its final recommendation, ensuring nothing is hidden in the math.