Payments · Revenue · Execution
Most global businesses are losing 2–8% of revenue in payments — and cannot quantify where or why.
I build and operate the revenue prioritization layer — a system that turns payment data into quantified revenue decisions.
Payments teams can see the problems.
They can't prioritize what to fix — or what it's worth.
That gap is where revenue is lost.
The system that runs this function
— identifies exactly where revenue is leaking
— quantifies what is actually recoverable
— outputs the next actions to take, ranked by risk-adjusted ROI and time-to-realization
This is how I frame and prove ROI — turning payment performance into clear revenue cases that can be used in both internal prioritization and external commercial conversations.
If this layer doesn't exist in your organization, it's already costing you 2–8% of revenue — often invisibly.
And in most cases, no one is accountable for fixing it.
Roles this function supports
Head of Revenue Optimization
Head of Payments
VP, Revenue Intelligence
Merchants I drove payment outcomes for
Spotify · Amazon · Netflix
At Boku · 13 years
Payments, Finance, Product, and Revenue Intelligence across 60+ global markets — building the systems that kept enterprise subscription businesses billing.
This capability is typically fragmented across Payments, Finance, and Product — I've built and operated it as a single, accountable function.
Based in Denver · Open to remote
register.andrea@gmail.com →$100M+
Recovered & incremental revenue
60+
Markets across the program
50K+
Subscribers recovered
2–8%
Typical revenue recovery range
For a $500M revenue subscription business, this typically represents $10–40M in recoverable revenue.
Why now
Payment infrastructure has standardized visibility. Integration is no longer the constraint. The bottleneck has shifted to deciding what actually drives revenue.
Most organizations have the data to see this. No function is accountable for acting on it.
Companies typically hire this function when payment performance is visible but revenue impact is not clearly quantified or owned.
Revenue Function Scope
This is the function required to close this gap:
Quantify revenue leakage across the payments lifecycle
Prioritize initiatives by net revenue impact
Align Product, Engineering, and Finance on execution
Turn payment data into a weekly revenue roadmap
Translate payment performance into revenue narratives used in commercial and customer conversations
The system below is how I do that.
Revenue Outcomes Owned
Revenue recovery
Recover 2–8% of failed revenue — failed payments, retries, decline handling
Conversion performance
+2–10pp billthrough lift — checkout through billing
Payment economics
+2–8bps margin improvement — routing, fees, cost optimization
Prioritization
Allocate engineering resources as capital — prioritized by recoverable revenue, payback period, and execution risk
Proven at Scale
$100M+
Recovered and incremental revenue — multi-year, global programs
60+
Markets across LATAM, Europe, SEA, East Asia, North America
4 yrs
As Director, Business Analytics (Payments & Revenue) at Boku
Spotify · Amazon · Netflix
Among the global merchants I drove payment outcomes for
Scale
Across merchants processing billions in annual payment volume — enterprise scale, multi-market, carrier and card.
Where small percentage improvements translate into multi-million dollar outcomes.
This work directly impacts revenue forecasts, margin, and capital allocation decisions at the executive level.
Direct linkage to revenue bridge: billthrough ↑ → net revenue ↑ → EBITDA ↑ → valuation → reinvestment capacity.
Time to value
Initial opportunities identified within 2–4 weeks, with quantified revenue cases. Execution delivers measurable impact within the first quarter, with typical payback inside one quarter.
Where this sits
Between Payments, Product, and Finance — reporting into the CRO or CFO, with direct ownership of revenue performance across the full payment lifecycle.
This function is typically built as a dedicated role or team, depending on company scale and payment complexity.
Typically engaged as a senior individual contributor or function lead, with scope expanding into team build-out as revenue impact is proven.
Payments are not infrastructure.
They are a revenue system.
Authorization rate tells you if a payment succeeded.
It does not tell you if you got paid.
Revenue is lost across the lifecycle — and rarely owned.
Billthrough is the metric. Authorization rate is an input.
A concrete example
A 3% drop in billthrough in Brazil is often caused by missing Pix coverage or retry timing misaligned with local pay cycles — not a payment processing failure. The fix is a product decision, not an engineering one. Most teams never see it because they are measuring auth rate, not billthrough by method.
Core output
Every week, this system produces a ranked list of revenue actions — sized in dollars and prioritized by risk-adjusted ROI.
Each action is sized by revenue impact, effort, and time-to-realization.
This is also how revenue opportunity is communicated — turning payment data into clear, defensible business cases for leadership and commercial teams.
Decision Engine — how I set weekly priorities across Product, Engineering, and Finance
Payments Revenue Layer
Most companies optimize payments in silos — authorization rates, retries, or cost in isolation. The revenue that disappears between checkout and collection is invisible because nobody owns the full system.
At Boku I operationalized this system across global markets — what previously required sustained analytical effort is now instrumented to run continuously.
It converts payment performance into a ranked revenue roadmap — sized in dollars and aligned to execution.
This is the revenue prioritization layer that sits on top of the payments stack — using routing, issuer, and retry data to quantify and prioritize revenue impact.
Decision Engine Layer
Translates payment performance into quantified revenue gaps — surfaced as actionable decisions
Outputs the exact actions to take — ranked by risk-adjusted ROI and time-to-realization
Each action is sized by revenue impact, effort, and time-to-realization — a capital allocation input, not just a task list.
This layer increases the value of payment platforms by translating performance data into revenue outcomes customers can act on — strengthening both product value and commercial conversations.
This shifts payment platforms from infrastructure providers to revenue partners — directly influencing customer retention and expansion.
Revenue Capture
Acquisition · Payment method coverage · Checkout conversion · Attach rate
Payment Optimization
Authorization · Decline taxonomy · Retry logic · Issuer performance
Revenue Intelligence
Billthrough · NRR · Cohort LTV · Forecast vs actual
Payments Economics
Cost of payments · PSP benchmarking · Routing · Margin optimization
Example — Decision Engine Weekly Priority Output
Example output: 12 prioritized actions · $2.2M annual opportunity · Top 3 = 66% of impact
How the system drives execution
Business Outcomes Delivered
Not all identified revenue is recoverable — prioritization focuses on the highest-probability, highest-impact opportunities first.
Revenue Recovery
Recover 2–8%
of failed revenue
Unclassified payment failures and misaligned retry logic — recoverable with the right framework
Conversion
+2–10pp
billthrough lift
Each pp compounds through NRR and subscriber LTV — the CFO-level argument for payment investment
Margin
+2–8bps EBITDA
via cost & routing
Fee structure, PSP routing, and interchange optimization converted into measurable margin improvement
Capital Allocation
Prioritized roadmap
by ROI / payback
Engineering investment decisions ranked by recoverable revenue — defensible to CFO and board
Reduces forecast volatility by improving revenue predictability across payment performance.
I specialize in payment and revenue performance for global businesses — the layer between checkout, authorization, and collection where most revenue is silently lost. I operate at Head-of / executive scope across Payments, Finance, and Product — owning programs end-to-end from diagnostic infrastructure to realized outcomes.
Build decline taxonomy, retry frameworks, and billthrough measurement across 60+ markets
Define KPI frameworks and instrumentation standards used by Product, Finance, and Operations
Output the exact actions to take — ranked by ROI, sized by recoverable revenue
The same failure patterns appear across markets, carriers, and merchants. That pattern recognition is what makes it possible to diagnose quickly and execute with confidence in new environments.
Billthrough is the metric. Auth rate is an input.
Most revenue loss is unclassified, not unavoidable.
You cannot optimize what you have not classified.
Prioritize by recoverable revenue, not volume.
The goal is not better analysis — it is better decisions.
A five-step operating cadence. Identifies the highest-impact opportunities within weeks. Measurable execution within one quarter.
Map the full revenue flow
Authorization to collection to retention. Understand the lifecycle before touching any metric.
Diagnose and classify revenue loss
Declines, retries, identity state — classified as recoverable or terminal. You cannot prioritize what you have not classified.
Quantify recoverable impact
Translate classification into a dollar figure. This is what makes prioritization defensible to finance and leadership.
Prioritize by impact, not volume
Volume tells you what is common. Recoverable revenue tells you what matters. Rarely the same list.
Drive execution across teams
Partner with Product and Engineering to ship. Analytics that does not change operations is documentation.
What this replaces
Before
Dashboards showing what happened
Manual analysis to identify issues
Unclear prioritization across teams
ROI estimated after implementation
Weeks of analysis before any action
After
Continuous identification of revenue gaps
Automated classification of failure types
Ranked actions by net revenue impact
ROI quantified before execution begins
Execution plan ready within 2–4 weeks
$100M+ recovered · 50K+ subscribers · 60+ markets
Problem Unclassified payment failures across 60+ markets — revenue loss unquantified and unowned→Action Built global decline taxonomy and recovery prioritization framework→Outcome $100M+ recovered, 50K+ subscribers retained
Problem: Revenue loss was uncategorized — without decline classification, prioritization was guesswork and engineering was pointed at the wrong problems.
Decision: Build the taxonomy before optimizing retry timing. Limited engineering capacity meant choosing one. That decision changed where the business invested for two years.
Tradeoff: Standardizing decline definitions across carriers and markets took time. But once every failure was classified as retryable, actionable, or terminal, the roadmap became defensible to finance and leadership.
Outcome: +2–10pp billthrough improvement by market. $100M+ recovered. Program expanded to link payment performance to identity state — shifting scope from payments optimization to system-level revenue recovery.
20% fewer ad hoc requests · 50% dashboard adoption lift
Problem Reactive analytics with fragmented metrics — no shared source of truth→Action Rebuilt data model, KPI governance, and self-serve infrastructure→Outcome 50% adoption lift, 20% fewer ad hoc requests
Problem: Analytics function was reactive. Teams ran their own numbers, arrived at different answers. No shared source of truth.
Decision: Fix definition alignment before building anything new. Canonical KPI definitions were the first deliverable — not the last. Without that foundation, every dashboard becomes a negotiation.
Outcome: Self-serve platform trusted by Product, Finance, and Operations. Team time shifted from reporting to decision-making. Function scaled without proportional headcount growth.
Daily C-suite visibility · Issuer-level attribution · Real-time root cause
Problem No issuer-level visibility — performance changes had no attributable root cause→Action Built issuer scorecard and daily C-suite KPI dashboard→Outcome Real-time attribution to issuer, market, and merchant
Problem: No issuer-level visibility. Optimization efforts targeted the wrong variables — nobody could see which issuers were driving performance changes.
Decision: Build the issuer scorecard and C-suite KPI dashboard in parallel. Executives needed root cause on the same day the number moved — not in the next analysis cycle.
Outcome: Daily attribution to issuer, market, and merchant. Executives could see the number move and understand root cause without additional analysis.
What I was accountable for — not what I contributed to
Boku, Inc. · Denver · 2021 – Jun 2025
Across merchants processing billions in annual payment volume
Subscribers recovered
Markets
Boku, Inc. · 2019 – 2021
Boku, Inc. · 2016 – 2019
Education
B.S. Economics
North Greenville University · Summa Cum Laude
Trustee's Scholarship · Dean's List (8 semesters)
Core Tools
These articles define the frameworks I apply operationally — decline taxonomy, payment method prioritization, orchestration strategy. They are strategic signal, not blog content.
Most subscription businesses track what authorizes. The revenue they are not collecting lives in their decline file, uncategorized and unactioned. Covers decline taxonomy, retry timing, pay period alignment, grace period segmentation, and what changes when AI agents start initiating transactions.
Used to define recovery strategy and decline taxonomy across 60+ markets
Read Article →AI coding has compressed payment method integration from months to weeks. That removes the constraint most product teams used to rely on for prioritization. A regional deep dive across SE Asia, Europe, and LATAM, with an interactive decision matrix and organizational cost model.
Informs how payment method decisions are evaluated on revenue realization, not conversion alone
Read Article →At least a dozen companies are actively hiring for Head of Payments, Director of Payments, or Senior Manager of Payments. Every one of those roles exists because the payment stack generates data and nobody is systematically turning it into decisions. Payment orchestration platforms have a choice: build tooling for those people or use AI to replace the need for them.
The structural argument for why autonomous optimization is the category-defining move
Read Article →The Payments Revenue OS is built from ten analytical modules across four layers. Each module addresses a specific question in the revenue lifecycle — together they cover everything a Head of Payments owns.
Payment Method Planner
Which payment methods to add, in which markets, in what order — ranked by revenue opportunity. Covers 25 markets with PSP availability, FX, settlement, and dispute data.
25 markets · PSP data · Live DE
Acquisition Conversion Diagnostic
Model conversion across the full acquisition funnel — checkout, payment method performance, retry recovery. Synced with Payment Method Planner selection.
Visual funnel · Conversion gaps · Live DE
Billthrough Simulator
Model the revenue impact of billthrough improvements across markets and user segments. Separate passive churn from active churn, quantify retry opportunity.
Passive churn · Retry value · Live DE
Decline Classifier
Classify decline codes into retryable, actionable, and terminal. The classification determines what is recoverable — and what to prioritize first.
Decline taxonomy · Recovery sizing · Live DE
Issuer Scorecard
Score issuer performance against benchmarks. Auth rate, decline patterns, and recovery rates vary materially by issuer — most teams can't see it without this layer.
Issuer benchmarks · Auth gaps · Live DE
Anomaly Detector
Surface unexpected shifts in payment performance before they compound. Auth rate drops, billthrough spikes, decline pattern changes — detected and sized.
Anomaly detection · Impact sizing · Live DE
Cohort Revenue Modeler
Connect billthrough performance to LTV and NRR. Model how a 1pp improvement in billthrough compounds across cohort lifetime — the CFO-level argument for payment investment.
LTV modeling · NRR impact · Live DE
Payments Q&A
Ask any payments question — decline codes, retry mechanics, network rules, PSP behavior. Grounded in 13 years of payments operations across 60+ markets.
AI-powered · Payments expertise
Cost of Payments Diagnostic
Benchmark your cost of payments in basis points against PSP benchmarks. Identify interchange optimization, routing inefficiency, and fee structure gaps.
Bps benchmarking · PSP comparison · Live DE
Payments Stack Audit
Assess the maturity and coverage of your current payments stack — redundancy, data access, routing intelligence, and operational readiness for scale.
Stack maturity · Coverage gaps · Live DE
All 10 modules · Live Decision Engine · Ranked by net revenue impact
Run any combination. The system aggregates findings and generates your next revenue actions — ranked by ROI, sized by impact, ready to execute.
Example output: 12 prioritized actions · $2.2M annual opportunity · Top 3 = 66% of impact
AI has not changed what I think analysts are for. It has changed what analysts spend their time on. The shift is from execution to judgment — from producing outputs to evaluating them, from running analysis to defining what is worth analyzing.
In practice I use AI in three ways. For exploratory analysis, it compresses the time between a question and a hypothesis. I can surface patterns in a dataset, stress-test a framework, or pressure-test an assumption in minutes rather than hours. The judgment about what the pattern means and whether it matters is still mine. AI makes the exploration faster, not the interpretation better.
For building tools and prototypes, AI functions as a coding collaborator. This portfolio — including the seven interactive analytical modules in the Payments OS — was built using AI as the primary development partner. That is a demonstration of what an analytically rigorous operator can ship when execution is no longer the constraint.
For thinking through analytical frameworks, I use AI as a thinking partner rather than a source of answers. It is useful for stress-testing a proposed metric definition, identifying what a framework is missing, or articulating a position more precisely. The framework design is always mine. The AI accelerates the refinement.
Building an AI-first analytics function requires more infrastructure investment than a traditional one, not less. When every analyst on the team can generate outputs in minutes, the leverage of the person who defines and enforces the measurement infrastructure increases proportionally. Canonical definitions, instrumentation standards, and metric governance become the critical investment because they are the shared foundation that makes AI-generated analysis consistent and trustworthy across the team. Without that foundation, AI produces faster disagreements. The analyst who owns the trust layer becomes the most leveraged person in the function.
The risk in analytics is not that AI replaces analysts. It is that teams treat AI-generated outputs as analysis and stop asking whether the question was right in the first place. That is where trust infrastructure matters most: canonical definitions, instrumentation standards, and metric governance that ensure everyone is asking the same question before AI helps answer it faster.
The most forward-looking version of this work is the analytics team building and maintaining the AI instruction layer for the entire organization. In practice this means owning the Skills.md files — prompt artifacts that encode canonical KPI definitions, metric logic, and measurement standards into the AI tools every team uses. When an engineer asks Claude Code to write a revenue query, when a PM asks an AI assistant to pull conversion data, when finance asks for a churn report — the Skills.md is what ensures every output uses the same definition of billthrough, the same cohort logic, the same churn calculation. Canonical definitions stop being documentation that people may or may not read. They become machine-readable and enforceable at the point of generation. The analytics team that builds and maintains that layer owns something that scales across every AI-assisted workflow in the company — not just the ones their team runs directly.
"The analytics team that owns the AI instruction layer owns the measurement standards for the entire organization. Canonical definitions stop being documentation. They become enforceable at the point of generation."
An analytics team earns its seat at the table by being the most trustworthy voice in the room, not the fastest one. When everyone can generate a number in minutes, the job is not analysis. It is ensuring the organization asks the right question and gets the same answer every time.
I lead from the data. I write SQL, review schemas, and debug dashboards alongside my team. That is what earns the credibility to push back on flawed assumptions and develop analysts who trust your judgment.
At Boku I built a revenue analytics function that went from reactive report production to a self-serve platform trusted by product, finance, and operations across 60+ markets. The most important work was not the dashboards. It was the instrumentation, the metric governance, and the decline taxonomy. That is trust infrastructure. That is what I build.
My focus is shortening the cycle from question to decision. I define clear ownership between product, engineering, and analytics so teams can move quickly without ambiguity. The goal is not just better analysis, but an organization that consistently makes better decisions.
"The best analytics functions don't just answer questions — they change what questions get asked. I build teams that make the business more curious."
01
Dashboards are only as valuable as the data behind them. I treat observability, instrumentation, and data quality as first-class deliverables, not afterthoughts. A team that owns its data quality earns the trust of the business.
02
The goal of analytics work is not a report. It is a better outcome. I hold my teams to a standard where every piece of work traces back to a decision that was changed or a question that was answered differently because of it.
03
I stay close to the data. I can write SQL, debug a broken dashboard, and review a schema alongside my team. That technical credibility is what makes it possible to push back on stakeholders, spot flawed assumptions, and develop analysts who trust your feedback.
04
Good analytics infrastructure, clear documentation, and strong analyst development compound over time. I build functions designed to outlast my direct involvement, where the systems and people are strong enough to run without me.
Billthrough is the metric. Authorization rate is an input.
Identity state directly impacts payment success and recovery. You cannot separate authentication from billing performance.
Decline taxonomy is infrastructure, not analysis. You cannot optimize what you have not classified.
Retry strategy should be driven by cash flow timing and user state, not fixed schedules.
Payment and identity systems should be evaluated on revenue realization, not conversion lift alone.
Where I Fit
Org placement
Sits between Payments, Product, and Finance — reporting into the CRO or CFO, with direct ownership of revenue performance across the payment lifecycle.
I build and operate systems that identify where revenue is lost, quantify what is recoverable, and prioritize what to fix first.
I operate at the intersection of Revenue (CRO) · Finance (CFO) · Product / Payments
Revenue · CRO
Growth and recovery. Translates payment performance into top-line outcomes the business can act on.
Finance · CFO
Margin and capital allocation. Converts payment data into investment decisions defensible to board and leadership.
Product · Payments
Execution across the stack. Owns the roadmap prioritization that connects payment signals to engineering decisions.
Roles this function fits
Head of Payments
Head of Revenue Optimization
VP, Revenue Intelligence
Business Impact I Typically Drive
If this layer doesn't exist in your organization,
it's already costing you revenue.
I'm currently exploring roles where this function can be built and scaled.
If revenue collection is a constraint in your business:
I can quickly identify where revenue is being lost — and what to fix first.
Preference for hybrid work in Denver or remote-first companies.