vcharlene.com
This is who I am.
This is what I've built.
This is what I'm learning.
I've spent more than 20 years helping organizations untangle complex marketing, technology, and operational challenges. Today I'm equally interested in exploring how AI can simplify work, accelerate learning, and help people get more done without burning out.
Who I Am
I didn't start in marketing technology. I started in mathematics. At the University of Illinois, I studied math alongside web publishing and computer science — which tells you something about how my brain works. I've always been the person who sees patterns and immediately wants to build a system around them.
After finishing my MBA at Middle Tennessee State University, I spent the better part of two decades doing the thing I'd figured out I was good at: helping organizations connect their marketing goals to the technology and processes that could actually deliver them. Not the theoretical version. The real, messy, cross-functional, "why does this campaign fire at 2am" version.
I've worked inside companies and alongside them as a consultant. I've led teams, run RFP processes, rebuilt marketing operations from the ground up, and sat in more platform migration planning sessions than I can count. I've been the person who knows both the strategy and the technical architecture — which, honestly, is still a rare combination in this space.
These days I'm a Principal Consultant at Slalom, where I lead MarTech transformations and Agile marketing programs for enterprise clients. I'm also building things on the side — AI experiments, productivity systems, a print-on-demand business called CopperBoom — because the best way I learn is by actually doing the work.
Outside of all that: I'm a mom, an entrepreneur, a coffee enthusiast, a lover of 90s R&B, a foster and adoptive parent, and someone who genuinely believes technology should make real life simpler and more meaningful. Not just corporate life. Real life.
What I've Done
Selected work highlights
Not a resume. A few projects worth talking about — the ones that were genuinely hard, that changed how I think, or that I'm still proud of a few years later.
The challenge
Migrating hundreds of customer journeys and campaigns from Iterable to Braze. The requirements-gathering phase alone — manually reviewing each campaign, logging audience logic, mapping data dependencies — was going to take months. That kind of work is slow, inconsistent, and burns out the people doing it.
What we built
- Connected Claude directly to Iterable to read live journeys on demand
- Built standardized Confluence templates Claude auto-populated
- Created a Catalog Exporter to map product data for Braze
- End-to-end: Iterable → Claude → Confluence, no manual copy-paste
What happened
- ~80% reduction in documentation effort per journey
- Hundreds of campaigns documented automatically
- Consistent, structured output across the whole team
- Engineering no longer needed for data catalog audits
The challenge
A global brand with fragmented marketing technology, no consistent operating model, and marketing and technology teams that weren't working in sync. They needed more than a platform upgrade — they needed a new way of operating.
What we did
- MarTech transformation strategy and platform consolidation
- Governance framework design for marketing operations
- Operating model redesign for cross-functional teams
- Agile transformation with squad coaching and enablement
What mattered
This one was a reminder that technology is rarely the actual problem. The platform was fine. The way teams were working together — and the lack of shared processes, ownership, and visibility — was where the friction lived.
The challenge
Building a marketing automation platform that could serve dozens of dealerships with personalized, timely communication — without requiring each location to manage their own campaigns from scratch.
What we built
- Enterprise-scale automation platform for 50+ dealerships
- 3,500+ personalized campaign variations
- Scalable architecture that could grow without rebuilding
- Hands-off execution at the dealership level
The real win
Scale without chaos. When you get the architecture right, adding the 51st dealership doesn't require a new project — it's just configuration. That kind of system thinking is what I love most about this work.
The challenge
Lennox needed a real enterprise marketing automation capability — not just a platform, but the strategy, structure, and governance to actually use it at scale across 18 business units.
What changed
- Campaign output grew from 12 → 110+ segmented lifecycle emails per month
- 72% year-over-year revenue increase through automation programs
- Full Marketo implementation with Salesforce integration
- Lead generation strategy for retail and dealer segments
What I learned
The difference between 12 emails a month and 110 isn't more people — it's the right architecture. When automation is designed well, it multiplies what a lean team can actually do.
What I'm Learning
Notes from the field
An ongoing collection of articles, experiments, and things I'm thinking about. Sometimes it's a polished take. More often it's just something I worked through and want to remember.
What I'm Building
The AI Lab
I learn best by building. These are real projects solving real problems — not demos, not proof-of-concepts, not things I built to write a blog post about. Each one started with a genuine need and a question: can AI actually help here?
What I learned
- The best AI workflows don't replace the thinking — they eliminate the grunt work so you can focus on it.
- Consistency matters as much as speed. One template, every time, from everyone.
- Documentation built for the migration became a long-term operational asset.
What I learned
- AI does best when it has constraints. Brand voice, format rules, examples — these are the inputs that make output actually usable.
- Building for a business you own teaches you things you'd never learn in a consulting engagement.
What I learned
- The system only works if it fits how you actually think — not how a productivity guru says you should.
- AI accountability works differently than human accountability. It's more consistent, less emotional — which is sometimes exactly what you need.
What I learned
- Structure matters more than motivation — when the system removes friction, behavior follows naturally.
- Simplicity always beats complexity, especially when you're designing for someone else.
- The best systems are designed around how people actually work, not how they're supposed to work.
Let's Connect
I'd love to hear from you.
Whether you're working through a MarTech challenge, exploring what AI could realistically do for your team, thinking through a platform migration, or just want to connect — reach out. I'm always up for a good conversation about work that actually matters.
No pitch deck required. No agenda. Just a conversation.