I've generalized these to protect client confidentiality. Company names are placeholders, campaign IDs aren't real, and I haven't made up any numbers. The mechanics are real.
A B2B organization had a marketing automation platform and a CRM that weren't talking to each other cleanly. Leads were scored inconsistently, routing rules were manual or stale, and sales didn't trust the "MQL" label. Hot leads sat unworked while reps chased whatever felt urgent that day.
I audited the existing scoring and grading model from scratch, then sat down with sales to redefine what "sales-ready" meant together. From there I built automated routing that surfaces a lead the moment it hits that bar and flags it for a quick human check before it reaches a rep, with a backup approver covering vacations and sick days so the queue never stalls waiting on one person. The system does the sorting. A person still signs off before anything lands in someone's queue.
Same-or-next-business-day turnaround on qualified leads, and a shared definition of "MQL" both teams trusted.
A corporate merger left the subscriber database fragmented across systems that had never been reconciled: B2B and B2C contacts mixed together with no clean way to tell them apart, and a five-million-record database carrying several hundred thousand duplicates. Every one of those duplicate records was also a line item on the CRM's per-record bill, so the mess wasn't just a data problem. It was a recurring invoice.
The CRM itself had no native deduplication tool, which tracks, since a vendor billing by the record has no incentive to help you shrink your record count. I built a deduplication process from scratch that merged duplicates while preserving full record history, so nothing about engagement history or contact lineage got lost in the cleanup. Alongside that, I wrote a standing SOP for retiring old, inactive records that were still counting against paid headcount without generating any return, so the database stopped quietly accumulating cost with no offsetting value.
Hundreds of thousands of dollars saved in subscription renewal fees, driven by cutting paid record headcount down to what the business actually used and eliminating auto-renewing line items nobody was touching anymore.
A recurring, editorial-style newsletter needed to serve more than one audience segment and region without splintering into disconnected programs or burning out the production process behind it.
I standardized one production workflow, from content intake through build, QA, and deployment, that worked across every audience variant. Messaging strategy and send cadence stayed coordinated centrally rather than getting decided fresh in each segment.
A full day of production time saved every month, and a cadence that moved from quarterly to monthly and held.
Standard email reporting leaned on open rate as the headline metric, and Apple Mail Privacy Protection had made that number unreliable enough that stakeholders were making decisions off noise.
I rebuilt the reporting standard around delivery rate, click-through rate, and opt-out rate as the actionable core, with open rate moved to directional-only context. I standardized the report structure and cadence so stakeholders got a consistent read every time.
Full stakeholder adoption. Every stakeholder who used to lean on open rate now makes the call off click-through rate instead, since open data stays unreliable as long as Apple Mail Privacy Protection is inflating it.
Landing pages were built once per campaign and left alone, with no structured testing or iteration tied back to performance data.
I standardized the landing page build around what actually drives a fill: a reliable call-to-action, copy that gives the visitor a real reason to convert, and an asset worth the exchange, whether that was a downloadable PDF or a webinar registration. I also moved conversion reporting from a one-time readout at campaign close to something structured and live, tracking downloads and form fills throughout the run instead of only at the end.
Stakeholders had real data behind interest in their offering while campaigns were still in flight, not just after the fact, and started shaping future assets off what that data showed instead of guessing.
The mechanics in these case studies are real. The company names are placeholders, swapped out to protect people who trusted me with their data.
