built this via Chat...sure you already did....tough one but doable:
1. Start with a company universe
Pull a broad list from LinkedIn Sales Navigator, Apollo, or Crunchbase for industries where high transaction activity is likely (ecommerce, ticketing, marketplaces, subscription services, travel, etc.).
2. Use web and app traffic as the core input
Gather monthly unique visitors and session counts.
Apply an assumed conversion rate for the industry (e.g., ecommerce 2–3%, marketplaces 5–7%, SaaS free-to-paid 1–2%).
Multiply by assumed average order value (AOV) for the industry.
Example: 1M monthly visits × 2% conversion × $60 AOV = $1.2M monthly in transactions (before seasonality adjustment).
Apptopia / Sensor Tower (for app-driven businesses)
Pull monthly active users (MAU) and assumed in-app purchase or transaction frequency per user.
3. Add public proxy signals to refine the estimate
Review site velocity: Track monthly review counts on Trustpilot, Yelp, Google Reviews.
If a business gets 2,000 reviews/month and the industry benchmark is 1 review per 200 orders → ~400,000 monthly orders.
Job postings: More openings in payments, fraud prevention, and customer service often track with high transaction volume.
Social engagement: Follower growth and engagement rates can correlate with customer transaction spikes (esp. in B2C).
4. Create an assumption-based scoring model
Inputs: traffic volume, app usage, review velocity, and job posting growth.
Weight these according to their correlation with transactions in that industry (e.g., 50% web traffic × conversion × AOV, 30% review velocity, 20% other signals).
Output: Estimated monthly and yearly transaction volume entirely from public sources.
5. Example flow in practice
- 1.
Get traffic → Similarweb says 750k monthly visits.
- 2.
Apply industry conversion (2.5%) → ~18,750 orders/month.
- 3.
Apply AOV ($50) → $937,500 monthly volume.
- 4.
Cross-check with reviews: 500 reviews/month, 1 review per 40 orders → ~20,000 orders/month (close enough to support the estimate).
