Contents
The Story
Most businesses at this stage know their total revenue. Some know it by product. Very few can tell you revenue by product × affiliate × campaign × month, with initial/rebill/refund splits, per SKU variant, across two currencies.
PRJ-02 built a revenue attribution system that produces 15 structured data exports across 7 attribution dimensions, tracking $1.08M in gross revenue across 38 products, 14 affiliate sources, 38+ campaigns, and 28 months of transaction data. Every dollar is traceable from the traffic source that generated it to the product it purchased to the month it occurred in.
This isn't a dashboard. It's a unit economics engine.
The Attribution Stack
Seven dimensions of revenue visibility: product (38 families, 100+ SKU variants), affiliate (14 tracked IDs), campaign (38+ funnels), time (28 months), transaction type (initial/rebill/refund), currency (ZAR and USD), and payout (affiliate cost per offer per source).
Any combination can be queried. Revenue by affiliate by month. Refund rate by product by campaign. Initial vs. rebill split by SKU variant by affiliate. The system answers questions most operators at this revenue level can't even ask.
What This Changes
A typical business at $1M revenue can answer "what's our refund rate?" with "about 5–8%, I think."
This system answers: "6.0% on PRD-01 ($32,535 / $542,356), 11.2% on PRD-02 ($16,138 / $144,932), 8.6% on PRD-03 ($9,446 / $110,355). Weighted portfolio average: 7.2%. AFF-01-sourced refund rate is higher than STL-sourced. Here's the monthly trend."
That depth informed every major decision in the portfolio: the PRD-01 decline (CS23) was visible in real time. The affiliate dependency (CS17) was quantifiable because every dollar was tagged to its source. The product launch discipline (CS16) was possible because the operator could see within 30–60 days whether a new product had traction.
Key Numbers
| Metric | Value |
|---|---|
| Structured data exports | 15 |
| Attribution dimensions | 7 |
| Products tracked | 38 families, 100+ SKU variants |
| Affiliates tracked | 14 |
| Campaigns tracked | 38+ |
| Time series depth | 28 months |
| Total gross revenue attributed | $1,075,946 |
| Transaction types | 3 (initial, rebill, refund) |
| Currencies tracked | 2 (ZAR, USD) |
Why This Matters
Revenue attribution isn't a reporting feature — it's a decision-making infrastructure. The operator who knows revenue by product × affiliate × month with rebill/refund splits makes fundamentally different decisions than the operator who knows their bank balance.
This system doesn't just report what happened. It informs what to do next.
Appendix A — Attribution Depth: Typical vs. This System
| Capability | Typical at $1M Revenue | This System |
|---|---|---|
| Total revenue | Yes | Yes |
| Revenue by product | Sometimes | Yes — 38 products, monthly |
| Revenue by traffic source | Rarely | Yes — 14 affiliates, monthly |
| Revenue by campaign | Rarely | Yes — 38+ campaigns, monthly |
| Initial vs. rebill split | Sometimes (aggregate) | Yes — per product, per SKU |
| Refund rate by product | Rarely | Yes — per product, per affiliate |
| SKU-level attribution | Almost never | Yes — 100+ variants |
| 28-month time series | Rarely | Yes — every dimension, every month |
Appendix B — The 15 Data Views
| # | Export | What It Answers |
|---|---|---|
| 1 | Revenue by product (USD) | Which products make money, when, and how? |
| 2 | Revenue by product (detailed) | Detailed product revenue time series |
| 3 | Revenue by product (Konnektive) | Konnektive-native product attribution |
| 4 | Revenue by campaign (raw) | Raw currency revenue per funnel |
| 5 | Revenue by campaign (USD) | USD-normalized revenue per funnel |
| 6 | Campaign order counts | Volume per funnel per month |
| 7 | Monthly revenue by affiliate | Revenue attribution by traffic source over time |
| 8 | Monthly by affiliate (split) | Revenue by affiliate with type splits |
| 9 | Affiliate × SKU counts | Which affiliates sell which products |
| 10 | Affiliate × offer conversions | Conversion counts by affiliate per offer |
| 11 | Affiliate × offer revenue | Revenue by affiliate per offer |
| 12 | Affiliate × offer payouts | Cost of traffic by affiliate per offer |
| 13 | Transactions by affiliate × product | Full breakdown per affiliate per SKU |
| 14 | Initial order counts | Order volume by source × product × month |
| 15 | Initial orders by product | Product-level initial order time series |
References
- Keating, M.G. (2026). "The Compounding Execution Method: Complete Technical Documentation." Stealth Labz CEM Papers. Browse papers