15 Attribution Views Across 7 Dimensions: How to Build Real Revenue Visibility

The Setup

Most businesses at the $1M revenue stage know their top-line number. Some can break it down by product. Very few can tell you revenue by product, by affiliate, by campaign, by month, with initial/rebill/refund splits, per SKU variant, across two currencies. The gap between "we know our revenue" and "we know where every dollar came from and what it cost to acquire" is the gap between reporting and decision infrastructure.

According to Gartner's 2024 Marketing Analytics Survey, only 23% of marketing organizations report having "strong" attribution capabilities, despite 89% rating attribution as "important" or "critical." A 2024 Forrester study on revenue intelligence found that companies with multi-dimensional attribution systems make pricing, channel, and product decisions 40-60% faster than peers relying on aggregate reporting. The attribution gap is not a technology problem — enterprise tools from Salesforce, HubSpot, and specialized platforms like LeadsPedia, TUNE, and Phonexa all offer attribution capabilities. It is an implementation and discipline problem: building the data infrastructure and maintaining the rigor to track every dollar at the transaction level across multiple dimensions.

The Stealth Labz operation, run by Michael George Keating, 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 is not a dashboard. It is a unit economics engine.

What the Data Shows

The attribution system operates across seven dimensions: product (38 product families, 100+ SKU variants), affiliate (14 tracked source IDs), campaign (38+ distinct funnels), time (28 monthly periods), transaction type (initial purchase, rebill, refund), currency (ZAR and USD for the multi-geography operation), and payout (affiliate cost per offer per source).

Any combination of these dimensions can be queried. This produces the 15 structured data views that form the attribution stack:

Revenue by product (USD-normalized), revenue by product (detailed time series), revenue by product (Konnektive-native), revenue by campaign (raw currency), revenue by campaign (USD-normalized), campaign order counts, monthly revenue by affiliate, monthly revenue by affiliate with transaction type splits, affiliate-by-SKU unit counts, affiliate-by-offer conversion counts, affiliate-by-offer revenue, affiliate-by-offer payout costs, transactions by affiliate-by-product, initial order counts by source-by-product-by-month, and initial orders by product time series.

To illustrate the depth: a typical business at $1M revenue can answer "what is our refund rate?" with an approximate aggregate number. This system answers: "6.0% on PRD-01 ($32,535 refunds on $542,356 gross), 11.2% on PRD-02 ($16,138 on $144,932), 8.6% on PRD-03 ($9,446 on $110,355). Weighted portfolio average: 7.2%. AFF-01-sourced refund rates are higher than STL-sourced. Here is the monthly trend for each."

According to a 2023 Harvard Business Review analysis of data-driven decision making, the most common failure mode in growing businesses is not lack of data but lack of structured data — the inability to query across multiple dimensions simultaneously. Revenue data exists in CRM systems, affiliate platforms, and payment processors. The attribution system's value is in the normalization, structuring, and cross-dimensional accessibility of that data.

The operational impact of this attribution depth informed every major portfolio decision documented across the case study series. The PRD-01 decline (detailed in CS23) was visible in real time through the product revenue time series — not as an aggregate quarterly decline, but as a month-by-month trajectory from $173K to $410 in initial revenue. The affiliate dependency (detailed in CS17) was quantifiable because every dollar was tagged to its source: AFF-01 at $515,616 lifetime, AFF-02 at $165,217, STL-owned at $77,296. The product launch discipline (detailed in CS16) was possible because the operator could see within 30-60 days whether a new product had traction — by affiliate, by campaign, by transaction type.

How It Works

The attribution system is built on Konnektive CRM as the transaction-level source of truth, with structured exports that normalize, segment, and cross-reference the raw transaction data. This is not a business intelligence platform layered on top of existing systems. It is the data architecture itself — the way transactions are tagged, tracked, and exported from the moment of creation.

Each of the 15 data views answers a specific operational question. Views 1-3 answer "which products make money, when, and how much?" Views 4-6 answer "which campaigns drive volume and at what conversion rates?" Views 7-8 answer "which traffic sources generate revenue and what is the trend?" Views 9-13 answer "what is the unit economics relationship between each affiliate and each product?" Views 14-15 answer "what is the acquisition volume trend by source and product?"

The dual-currency tracking (USD and ZAR) reflects the operation's multi-geography model — PRJ-05 generates ZAR-denominated revenue in South Africa while the remainder of the portfolio operates in USD. Without currency-normalized views, portfolio-level analysis would conflate exchange rate movements with operational performance.

The system also tracks the cost side of attribution: affiliate payouts by offer, by source, by month. This means gross margin can be calculated at the affiliate-by-product-by-month level — not just at the portfolio level. The 97.7% aggregate COGS ratio (detailed in C7_S153) is the portfolio average. The attribution system reveals that AFF-01-sourced revenue ran at over 100% COGS while STL-owned revenue ran at 23-47% COGS. That distinction — invisible in aggregate reporting — is the difference between scaling losses and scaling profit.

What This Means for Decision-Makers

Revenue visibility is not a reporting function. It is decision infrastructure. The operator who knows revenue by product, by affiliate, by campaign, by month, with rebill/refund splits, makes fundamentally different allocation decisions than the operator who knows the bank balance.

For PE evaluators, the existence of a structured attribution system at this scale is itself a signal of operational maturity. Most businesses at the sub-$1M revenue stage operate on aggregate reporting. Building 15 cross-dimensional views across 7 attribution dimensions indicates an operator who treats revenue data as a capital allocation tool — not a backward-looking report. The attribution system did not just document the portfolio's history. It informed every scale decision, every kill decision, and every infrastructure investment. That is the difference between a business that reports on performance and a business that manages to performance.

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Related: [C7_S148 — 28 Months of P&L Data] | [C7_S151 — 38 Products Tested, 6 Scaled] | [C7_S152 — Revenue Distribution Across 18 Business Lines]

References

1. Gartner (2024). "Marketing Analytics Survey." Attribution capability self-assessment data across marketing organizations.
2. Forrester (2024). "Revenue Intelligence Study." Decision speed differentials between companies with and without multi-dimensional attribution systems.
3. Harvard Business Review (2023). "Data-Driven Decision Making." Analysis of structured versus unstructured data as a failure mode in growing businesses.
4. Keating, M.G. (2026). "Case Study: The Revenue Attribution System." Stealth Labz. Read case study