I run strategic enterprise customer success the way customers need it run — operational, evidence-driven, and quiet during the moments that matter most.
Most recently at Cloudflare across strategic enterprise CSM and partner success engagements: media, sports, financial technology, industrials, IT services, and platform partnerships — including The Associated Press through the 2024 U.S. election cycle, MLB, Warner Bros. Discovery, Binance, Carrier, Kyndryl, and IBM Cloud Internet Services. 15+ years across Apple consumer channel sales and Cloudflare enterprise CS. Claude is part of the daily practice, not a separate workstream.
Supported The Associated Press through the 2024 U.S. election cycle by coordinating a 48-hour staffed-support plan with global support leadership — TAM handoffs, joint escalation channel, real-time internal/customer coverage during one of the highest-stakes operational windows of the year.
🏢 Strategic Enterprise Book
Supported customers across media, sports, technology, financial technology, industrials, and IT services — including Stack Overflow, MLB, Warner Bros. Discovery, Binance, Carrier, Kyndryl, and IBM Cloud Internet Services. Consumption-based and seat-based engagements.
🔎 Portfolio-Scale Visibility
Built a multi-thousand-account portfolio audit and a customer-migration scoping dashboard used in partner sales and renewals cycles. AI-assisted analytics tied directly to QBR and renewal conversations.
🎯 11 quarters / $84M+
Consecutive quarters at or above quota at Apple, on an annual channel territory of $84M+. Designed the onboarding toolkit that supported a 2x increase in US Channel Sales headcount.
Approach
Strategic enterprise customer success is operational work. The customer relationship is the surface; what holds it up is whether the operating model behind it actually runs — escalation paths that people trust before they need them, renewal motions that don't show up as a surprise, account-level visibility that survives a team change.
The good customer success conversations start with empathy for how the customer's business actually works. You learn how the customer makes money, what they're accountable for internally, and what their definition of "the platform held up" looks like — before you propose changes to how they use it.
AI is part of the daily practice. I use it to do the analytical work that historically sat on a separate team or didn't happen at all: portfolio audits, account-level reporting, dashboard prototypes, customer-facing material at QBR. The judgment calls — what to escalate, who to bring into the room, when to push back, when to absorb — are still relationship work. AI gives me leverage on the data half so I can spend more of my time on the customer half.
In customer-success terms: renewals the customer expects and trusts. Expansions grounded in actual usage and roadmap fit. Escalations that resolve faster because the operating layer underneath them already exists. Strategic moments — like an election night — that the customer doesn't have to think about, because the plan was built before it mattered.
Success stories
Success story · 01
Election Night, Quietly
The Associated Press runs the vote-tally infrastructure that powers national election calls. Election cycles are their highest-stakes operational window of the year — and a primary cyber-target window.
Problem The customer's most consequential 48 hours of the year was approaching with no purpose-built coverage plan.
Action Coordinated a 48-hour staffed-support plan with global support leadership: TAM handoffs, joint escalation channel, real-time internal/customer coverage.
Impact AP went through election night without operational drama on the platform side. AP has since expanded on the platform.
This was less a project than a posture. AP didn't need a deck about how prepared we were. They needed to know — before the night, in concrete terms — that the operating model behind the platform would hold up if something went sideways at 2 a.m. on November 5th.
I worked with global support leadership to design a 48-hour staffed-coverage plan: TAM handoffs at fixed intervals, a joint escalation channel that internal and customer-side stakeholders were already in before the night began, and a real-time internal coverage rhythm so that any escalation had a clear owner at any hour.
The right outcome here was the absence of drama. Election night ran. The customer's operational view of Cloudflare during that window was steady, predictable, and well-staffed.
The judgment call worth naming: most of the value happened before the night. The work was not the heroics — it was making sure heroics weren't needed.
The hard part was deciding what would be on the plan and what would be off it. The night was the easy part, because the plan had already done its work.
Success story · 02
The Migration That Wasn't Visible
A digital experience platform partner with a multi-hundred-customer embedded base on Cloudflare's CDN and security layer. The migration affected real end-customers' production environments.
Part A — What the customer base actually looked like
Problem The data existed but wasn't connected: customer-level usage on one side, commercial records on another, no clear operating view tying them together.
Action Built an AI-assisted reconciliation workflow comparing historical commercial records against per-customer usage signals.
Impact A material set of customer records surfaced for commercial review. A repeatable audit path that both teams could trust.
The embedded relationship had grown over years across renewals, platform changes, and internal ownership transitions on both sides. None of the parts were broken in isolation — but the operating view that connected customer usage to commercial coverage didn't exist as a single artifact.
I built an AI-assisted reconciliation workflow to compare historical records against customer-level usage signals. The output was the missing operating view: a customer-by-customer picture both sides could validate and act on, packaged in a way that tied findings to customer footprint rather than blame.
The lesson: at scale, customer success work creates hidden operational debt. You don't resolve that with another meeting. You resolve it by building the view that was missing.
Part B — What the customers actually needed to migrate
Problem The migration affected real end-customer production environments and lacked account-level scoping, readiness visibility, and a durable commercial model.
Action Built the operational scoping layer: DNS-readiness audits, package mapping, pricing logic, and a partner-facing dashboard.
Impact Partner sales and renewals teams adopted the dashboard. Customer-level migration risk became visible and actionable.
The strategic direction was understood. The blocker was operational clarity — at the customer level. Every customer in the embedded base had a slightly different configuration, a slightly different package mix, and a slightly different readiness state. The work was building the layer that made each customer's migration manageable in its own right.
I built DNS-readiness audits across the customer base, account-level package mapping, pricing logic, and best-practices guidance for configuring the new model at scale. As the operating conversations matured, the reporting matured with them. The work eventually shipped as a partner-facing dashboard used by partner sales and renewals teams to scope and manage customer migrations.
A commercial pattern showed up in the early migration data — customers' effective ACV moving in a direction the partner hadn't anticipated. I surfaced the systemic pricing risk to internal leadership and proposed a corrective approach grounded in long-term customer outcomes rather than short-term renewal math.
The dashboard isn't really the artifact. The artifact is the customer-by-customer view of a migration that previously didn't have one.
Success story · 03
The Customer Map That Didn't Exist
A strategic platform partnership where customer-level reporting was limited and the customer base spanned thousands of accounts across industries, geographies, and verticals.
Problem Customer-level visibility was limited, making it difficult to assess footprint, service coverage, expansion potential, and migration timing.
Action Built a 3,500+ account audit with Claude Code that turned limited reporting into customer-footprint visibility across industries, geographies, verticals, and service usage.
Impact A usable customer map for QBR, expansion, and migration-planning. Surfaced the resell expansion conversation around data localization and Magic Transit.
Most people would have treated limited reporting as the end of the conversation. I treated it as the start of the audit.
I built a 3,500+ account portfolio audit to recover customer-level visibility from the data available on our side, mapping accounts by industry, geography, vertical, and service usage. The output was the customer map that didn't exist: a practical view of footprint, service coverage, expansion potential, and migration risk that internal and partner-aligned teams could use in QBR, expansion conversations, and migration planning.
What the audit gave us first was standing. It proved the customer view was recoverable and could be operated against. From there, the same analysis told different stories depending on who needed to act — migration sequencing for one stakeholder, resell expansion grounded in actual customer footprint for another.
The work was making the customer view visible. From there, the right conversations got easier to have.
Working with AI
I use AI as a working tool in the customer-success practice, not as a branding point.
In my day-to-day, I use Claude (Claude Code / OpenCode) as an analytical partner for the infrastructure behind the success stories on this site: portfolio audits, account-level reporting, package-pricing analysis, migration-readiness checks, dashboard prototypes, customer-facing material at QBR, and workflow automation that shortens the gap between "I have a question about this account" and "I have a usable answer."
The model does not make the strategic calls. It helps me move faster once the call is made — and lets me bring more evidence into the customer conversation than I otherwise could.
The work generally falls into a few patterns:
Portfolio-scale audits. Workflows that normalize large sets of account, usage, and customer-footprint data to surface patterns that would be difficult to find manually. The install-base customer map and DNS-readiness audit both started here.
Customer-facing analytics. Account-level views, renewal trend analysis, expansion modeling, and the kind of material that shows up in a QBR. The value isn't asking AI for an answer; it's building a repeatable analytical pattern that survives the cycle.
Dashboards the customer keeps using. Interfaces and trackers that customer and partner teams can keep using after the initial analysis is done. The goal isn't dependency on me; it's a working operating view they can run themselves.
Operational automation. Draft customer communications, intake tracking, order-flow support, and other workflow automation that reduces overhead and makes follow-through easier.
What changes when a CSM has analytical leverage they did not used to have: the customer conversation stops being bottlenecked on whether someone has time to manually pull and reconcile data. The follow-up email that used to take a day takes an hour. The QBR slide that used to require a separate analyst doesn't.
This is the kind of work I want to keep doing.
Background
Cloudflare — Customer Success and Partner Success, 4+ years. Started as a CSM during a major plan-to-offering transition, taking on a 295-customer book — roughly 10x the typical starting portfolio. Promoted to Senior CSM on the Strategic Enterprise book, supporting media, sports, technology, financial technology, industrial, and IT services customers, including The Associated Press through the 2024 U.S. election cycle. Then promoted to Senior Partner Success Manager, owning post-sale success across three of Cloudflare's largest platform partners, including IBM Cloud Internet Services as a consumption-based enterprise account.
Apple — Channel Sales, 12 years. Ran an $84M+ annual territory across consumer and wireless channel. 11 consecutive quarters at or above quota. Designed the onboarding toolkit that supported a 2x increase in US Channel Sales headcount. Ran a partner-website pilot that drove +16% sell-through on Apple Watch (amazon.com) and +21bp on MacBook Pro (bestbuy.com).