I own and operate the measurement and decision infrastructure that drives revenue, margin, and cash flow across subscription payment systems. Across this work I have owned optimization across billions in annual TPV and a eight-figure subscription revenue base — directly influencing billthrough, net revenue, and EBITDA across global merchant programs. I influenced product, engineering, finance, and operations roadmaps across teams without direct reporting lines. The tools and frameworks on this portfolio are a working demonstration of that system — covering attribution, optimization, allocation, monitoring, control, and forecasting.
Payment optimization is one of the highest ROI levers in subscription businesses — often outperforming CAC reduction or pricing changes on a risk-adjusted basis, with payback measured in weeks rather than quarters.
Across this system, I have directly influenced $100M+ in annual revenue recovery and delivered +2–8 bps EBITDA improvement across key merchant programs, with higher impact in under-optimized markets — at merchants processing billions in annual payment volume. Improvements in billthrough directly increase NRR (Net Revenue Retention), extend customer lifetime, and reduce CAC payback periods.
Most subscription businesses measure authorization rate. Authorization rate tells you whether a single payment attempt succeeded. It does not tell you whether you got paid.
Billthrough — the rate at which subscribers are successfully billed across all retry attempts over a billing window — is the metric that maps to revenue. The delta between authorization rate and billthrough is where material revenue is lost, and it is invisible until you classify it.
At scale, a 1pp improvement in billthrough is not a metric improvement. It is a high-margin incremental EBITDA contribution, multiplied by subscriber LTV, compounded across every market where it applies.
At scale, payment inefficiencies represent a recurring revenue leakage equivalent to 2–8% of topline for most subscription businesses — with direct, measurable impact on EBITDA margin and operating cash flow. Most of it is recoverable. Every percentage point of billthrough improvement increases NRR, extends subscriber lifetime, and reduces the effective CAC payback period.
Revenue Leakage Sources
We prioritized classification accuracy over retry timing early in the program — accepting slower initial recovery in exchange for a taxonomy that made every downstream decision faster and more defensible. That sequencing choice alone changed where the business invested for the following two years.
| Layer | Function | Tool | Financial Impact | Typical Scale |
|---|---|---|---|---|
| Attribution | Classify every decline as retryable, actionable, or terminal | Decline Classifier → | Eliminates undifferentiated retries; stops waste on terminal failures | 1–3pp billthrough lift; $500K–$2M revenue recovery |
| Optimization | Model revenue impact of billthrough changes net of retry fees | Billthrough Simulator → | Quantifies EBITDA impact; builds investment case for engineering resources | 2–5pp modeled improvement; +20–80bps EBITDA |
| Allocation | Score issuer performance; identify where failure is systemic | Issuer Scorecard → | Surfaces issuer-level negotiation levers; prioritizes relationship investment | Issuer-specific +1–4pp auth rate; fee reduction |
| Monitoring | Detect anomalies in authorization, settlement, and fraud signals | Anomaly Detector → | Catches revenue degradation before it compounds; triggers investigation | Prevents $100K–$1M+ revenue erosion per incident |
| Control | Self-serve payments data Q&A for non-technical stakeholders | Payments Q&A → | Consistent metric answers across product, finance, and operations | 70% reduction in ad hoc data requests |
| Forecasting | Model subscriber LTV and revenue retention curves by cohort | Cohort Modeler → | Connects billthrough improvements to NRR; values churn prevention in LTV terms | 1pp billthrough = 8–15 months incremental LTV per sub |
The Payments Stack Audit generates a ranked action list with estimated annual revenue return and investment level for each initiative. Representative outputs at a mid-market subscription business:
| Initiative | Est. Investment | Est. Annual Return | ROI Multiple | Payback | EBITDA Impact | Effort |
|---|---|---|---|---|---|---|
| Retry logic optimization — decline taxonomy + timing | $20K–$50K | $800K–$2M | 20–40× | <1 month | +30–80bps margin | Low–Medium |
| Billing configuration audit — PSP settings, retry caps | $20K | $400K–$1M | 20–50× | <1 month | +15–40bps margin | Low |
| Routing intelligence — PSP selection by decline type | $150K–$300K | $500K–$1.5M | 3–10× | 2–4 months | +20–60bps margin | Medium–High |
| Signal enrichment — tokenization + 3DS optimization | $150K | $300K–$800K | 2–5× | 3–6 months | +12–30bps margin | Medium |
| False decline reduction — issuer data sharing | $300K–$500K | $600K–$2M | 2–4× | 6–12 months | +25–80bps margin | High |
Operating under constrained engineering capacity, I prioritized initiatives based on ROI and payback thresholds — making explicit trade-offs between higher-return but lower-effort interventions and structural changes requiring significant engineering investment. The sequencing decisions were as consequential as the initiatives themselves.
Returns are illustrative ranges for a subscription business processing $100M–$500M annually. Investment estimates reflect engineering effort level: Low = ~$20K (1–2 engineer sprints), Medium = ~$150K (one quarter, small team), High = ~$500K (multi-quarter cross-functional program). The Payments Stack Audit generates return figures calibrated to your specific volume, ATV, and optimization maturity.
This system is not a reporting layer. It is an operating cadence — a set of decisions made at defined frequencies by specific stakeholders, each with a measurable revenue consequence.
| Decision | Frequency | Who Uses It | What It Drives | Revenue Impact |
|---|---|---|---|---|
| Retry logic update — timing, suppression rules, attempt caps | Monthly | Payments Analytics, Engineering | Billthrough rate improvement; fee cost reduction | +$500K–$2M/yr |
| Decline taxonomy refresh — reclassify new failure codes | Monthly | Payments Analytics, Operations | Accurate retry targeting; reduced terminal decline waste | +$200K–$800K/yr |
| PSP routing change — reallocate volume by decline segment | Quarterly | Payments Analytics, Product, Finance | Authorization rate improvement; net revenue per transaction | +$300K–$1.5M/yr |
| Issuer performance review — identify underperformers | Quarterly | Payments Analytics, C-Suite | Escalation to PSP account team; issuer-level relationship action | +$100K–$500K/yr |
| PSP contract / fee renegotiation — backed by billthrough attribution data | Annual | Finance, Payments Analytics, Legal | Fee reduction; SLA enforcement; concentration risk management | +$200K–$1M/yr |
| Payment expansion / market entry decision | Annual | Product, Finance, C-Suite | Method coverage; market billthrough baseline; LTV model by market | Strategic |