The iKawn Mirror ROI calculator is a scenario model for retail buyers. It estimates possible revenue, contribution, ROI, and payback from a minimal set of inputs: deployment type, monthly shoppers or visitors reached, average order value, current conversion rate, gross margin, and currency.
A virtual try-on ROI model is useful only when it makes assumptions visible without asking the buyer to invent technical uplift numbers. Reach, order value, current conversion, gross margin, deployment model, and cost basis all change the business case.
The iKawn Mirror calculator is therefore a planning tool, not a promise. It uses a sourced default uplift and public pricing starting points so retail teams can see which assumptions matter before they commit to a pilot or rollout.
The calculator is not a result. It is a way to see whether the selected web, app, or kiosk deployment model has enough commercial headroom to justify a pilot.
How to use the ROI calculator responsibly
The calculator should help a buyer ask better questions, not create a guaranteed ROI claim. It pre-fills conversion lift from a cited public AR commerce benchmark instead of asking the user to invent an uplift number.
A useful model compares conservative, base, and high assumptions. It should be refreshed with actual pilot data after a real deployment, especially qualified interactions, product interest, and downstream conversion where measured.
What the calculator measures
The calculator estimates scenario-level incremental revenue, contribution, ROI, and payback from a small set of buyer inputs: deployment type, monthly shoppers or visitors reached, average order value, current conversion rate, gross margin, and currency.
The practical question for a buyer is how this translates into the store journey: what the shopper sees, what staff can support, what content must be prepared, and what decision the retailer wants after the try-on moment.
Key considerations:
- It uses buyer inputs and cited assumptions, not guaranteed iKawn outcomes.
- It separates revenue, gross contribution, cost, net contribution, ROI, and payback.
- It supports web-based, app-based, and kiosk-based try-on models.
How to use it
Choose the deployment model first, then enter the monthly shopper or visitor reach, average order value, current conversion rate, and gross margin. The calculator uses the pricing model and a cited uplift benchmark to keep the form short.
The practical question for a buyer is how this translates into the store journey: what the shopper sees, what staff can support, what content must be prepared, and what decision the retailer wants after the try-on moment.
Key considerations:
- Use total monthly traffic reached by the try-on experience, not total company traffic.
- Use average order value for the target category.
- Change deployment type to compare web, app, kiosk purchase, and kiosk rental economics.
How to read the output
The output is an estimate for decision discussion. The headline metric cards show the base case first, while the scenario tabs under those large numbers show conservative, base, and high cases.
The practical question for a buyer is how this translates into the store journey: what the shopper sees, what staff can support, what content must be prepared, and what decision the retailer wants after the try-on moment.
Key considerations:
- Do not treat the output as a guaranteed ROI.
- Use it to prepare better questions for a pilot.
- Ask for a scoped deployment conversation before committing budget.
How to evaluate this before a pilot
A strong pilot starts with a narrow use case and a clear store environment. It should prove whether shoppers understand the experience, whether selected garments remain faithful, and whether the retail team can move from visual exploration to a useful next action.
Evaluation checks:
- Use monthly shoppers, visitors, or app users reached by the try-on experience.
- Use average order value for the target category.
- Review the sourced uplift assumption rather than trusting one output.
- Use the selected web, app, or kiosk deployment model as a starting cost assumption.
What the pilot has to prove
The pilot should produce operational confidence, not only a visual demo. Retail teams should be able to start, reset, explain, and support the experience during realistic store conditions.
Proof points:
- Shoppers understand the experience without long explanation.
- Store teams can start, stop, reset, and introduce the flow cleanly.
- The catalog owner can refresh garments without breaking the retail journey.
Where expectations need to stay clear
The page should help a buyer understand where virtual try-on is useful and where normal retail judgment still matters. That clarity improves trust and reduces the risk of overpromising before a deployment is validated.
Boundaries:
- The calculator does not guarantee revenue, contribution, conversion uplift, margin, or payback.
- It uses public iKawn Mirror pricing as a starting cost assumption; scoped quotes can change the final commercial model.
- The model should be updated with measured pilot data before a rollout decision.
Scope this for a real retail environment
Share the market, store format, garment category, launch timeline, and whether this is for a store pilot, mall activation, or campaign. iKawn can then map the right Mirror workflow and demo path.