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Making profit predictable.
Roposo Clout showed dropshippers their margin, not their profit. I designed the profit model behind the Pricing Calculator, so dropshippers could see the real economics before publishing to Shopify.
Role
Sole Product Designer
Timeline
~3 weeks
Team
2 PMs + 2 engineers
[ The Problem ]
Margin was not profit.
New dropshippers saw a healthy markup and assumed it meant profit. They missed what came after — ad spend, failed deliveries, RTO (return to origin), misc charges. Experienced dropshippers already accounted for these. The platform had no shared way to reason about it.
[ Research ]
Experts had already built the missing logic.
Top dropshippers had their own Excel sheets covering confirmation, delivery, RTO, and ad costs. The logic worked — it just lived outside the product, and only the experienced had it. My job was to turn that logic into a model everyone could use.
[ The Model ]
The model came from grouping.
The first version was a flat list of a dozen fields — accurate, but impossible to scan or trust in the moment. The change that made it work was structure. I grouped every value into the three questions a dropshipper actually asks
What am I selling?
Purchase Price; Sale Price; Margin
How many will actually deliver?
Expected Orders; Confirmed / Cancelled; Delivered / RTO
What will I spend?
Ad Spend; RTO Cost; Misc. Charges; Discounts
Some values came from the product, some from the dropshippers, and the rest were handled by the system.
[ Layout ]
The model shaped the layout.
Fixed context stayed visible, discounts sat close to pricing, inputs were grouped for simulation, and outcomes stayed separate for decision-making.
[ Simplification ]
The model decided what to show.
With the structure in place, each field faced one test: does changing it change the decision? If it did not, it went.
Return % removed. It only moved profit by about 1–2%, and most dropshippers ignored it or folded it into Misc Charges. Not worth a permanent field.
Total ad spend became ad spend per order. Dropshippers don't think in total campaign spend — they think in cost per order, the number they already read in Meta Ads Manager. The input matched the number in their head.
No recommended price. Earlier versions suggested one. But there is no single best price — some want higher margin, some want higher volume. The model shows the result of a dropshipper's own inputs; it does not pick them. It informs the decision; the dropshipper makes it.
No Calculate button. Dropshippers keep adjusting selling price, ad spend, and delivery %, so a button would only repeat the same step. Profit now updates live. Until all required inputs are filled, it stays N/A and Push to Shopify is disabled.
Open to inspection. The profit is not a black box. Dropshippers can open the full breakdown — Orders, Earnings, Spends — and check the math. Inputs on the left, results on the right.
[ Integration ]
It lives inside publishing, not beside it.
The calculator was available at two high-intent moments — on the PDP while evaluating a product, and again inside Push to Shopify before publishing. Once a seller settled on a price, it carried back and prefilled the selling price automatically.
[ Impact ]
It changed how pricing was decided.
The biggest change was consistency. Pricing started following one logic, and internal KAMs used the same model when working with dropshippers, So the platform and the dropshippers finally reasoned about profit the same way.
64%
of Push-to-Shopify journeys used the calculator before publishing
37%
of dropshippers changed their selling price after calculating
28% ↓
post-publish price edits, which reduced price-sync issues
Within three weeks, about 80% of active dropshippers had tried it.
[ What I Learned ]