Step 1: Define the right metrics
Three metrics capture the full ROI picture. Try-on adoption rate is the percentage of product page visitors who click the 'Try it on' button and complete at least one try-on — this tells you whether shoppers are discovering and using the feature. Target 15–25% adoption; below 10% usually indicates a placement issue. Conversion lift is the difference in add-to-cart and purchase rates between sessions that include a try-on versus sessions that do not — this is your direct revenue attribution metric.
Return-rate delta is the difference in your store's return rate in the 90 days post-install versus the 90-day baseline before install. This is the most important long-term ROI metric because return processing costs (shipping, restocking, customer service) are often larger than the revenue impact of conversion lift. A 25% reduction in return rate on a $500K/year apparel store typically saves more margin than a 20% conversion lift on a 5% adoption rate.
Step 2: Set your baseline before install
Before you install the widget, capture 30 days of baseline data. Pull from your analytics platform: (1) product page conversion rate (product page views to orders, not just add-to-carts); (2) average return rate across all apparel orders (returns divided by shipped orders); (3) revenue per product page session. Export these numbers to a spreadsheet and timestamp them. This baseline is your control group.
If your store has seasonal traffic spikes (holiday, back-to-school, summer swimwear), try to install the widget during a stable traffic period so your comparison windows are similar in shopper composition. Installing right before Black Friday and comparing against a normal October baseline will overstate lift. If you cannot avoid a seasonal period, use year-over-year comparisons rather than pre/post comparisons.
Step 3: Understand attribution windows
Attribution for try-on conversion should use a session-level model: if a shopper tries on a product in the same browsing session and then purchases, that purchase is attributed to the try-on. Photta's Business Dashboard uses this session-level model natively and reports it as 'Converted after try-on.' Do not use last-click attribution from your ad platform to measure try-on ROI — those tools do not track on-site try-on events.
For return-rate measurement, use a 90-day rolling comparison window with a 30-day offset. The offset accounts for the time between purchase and return: most returns arrive within 30 days of delivery, but some stores have 60-day return windows. Measuring return rates in weeks 1–4 post-install will undercount because the orders placed in the first week after install are still inside their return windows. Wait 90 days from install before drawing return-rate conclusions.
Step 4: Use Photta's Business Dashboard
Log in to business.photta.app and navigate to Analytics. The dashboard shows three panels: Try-On Volume (total and by product), Conversion Comparison (sessions with try-on vs. without), and Return Rate Trend (if you connect your order management system via the webhook). The conversion comparison panel is the most actionable — it shows you the exact percentage lift attributable to try-on sessions in real time, updated daily.
To enable return-rate tracking in the dashboard, go to Settings → Integrations and add your order management webhook. The dashboard accepts order-status webhooks from Shopify, WooCommerce, and custom endpoints in the standard format documented in Settings → Integrations → Webhook Docs. Once connected, return-rate data appears in the Analytics panel within 24 hours and is updated daily as new return events arrive.
Step 5: Compute the dollar payback
Use this calculation to estimate dollar ROI. Let R = your average return rate before install (e.g., 0.28), C = cost per return (shipping + restocking labor, typically $12–25 for apparel), M = monthly order volume, and D = return-rate drop (typically 0.07–0.10 based on Photta cohort data). Monthly return-cost savings = M × R × D × C. Example: 1,000 orders/month × 28% return rate × 8% reduction × $18 cost = $403/month in saved return costs. That alone covers the Starter plan ($49/mo) with $354 left over.
Add conversion lift revenue: Let P = product page sessions per month, A = try-on adoption rate (e.g., 0.20), L = conversion lift on try-on sessions (e.g., 0.22), and AOV = average order value. Monthly incremental revenue = P × A × L × (conversion rate) × AOV. For a store with 10,000 product page sessions, 20% adoption, 22% lift, 3% base conversion, and $85 AOV: 10,000 × 0.20 × 0.22 × 0.03 × $85 = $1,122/month in incremental revenue. Total monthly ROI at Starter plan: $403 + $1,122 − $49 = $1,476 net positive per month.