Growth and lifecycle marketing for consumer brands. I run the whole idea: come up with the campaign, launch it, and find out honestly whether it worked. Then build the programme that keeps the customers it brought in.
I studied economics because I wanted to understand how value is created, then spent five years watching that question play out on a shop floor and in a database.
I was the only marketer on a small team at a 150-location business, which meant the campaigns, the collaborations, the POS setup, the website and the app were all mine to run. Before that, 25 to 30 campaigns a quarter across a national chain, piloted in test stores before anything went wide.
The other half of my work is the unglamorous end: deciding who gets the message and who very much does not, building the flows that run while everyone sleeps, and finding the point in a customer's second month where they quietly stop caring. I am finishing an MSc in Financial Engineering and starting an MS in Marketing Analytics at Rutgers, and I write my own SQL.
Built the community and creator programme from zero: no list, no messaging, no tooling. Worked prospects by hand, wrote the hooks, produced the assets, and reported what converted every week.
Lifecycle campaigns in Braze across email, push and in-app for a crypto wallet, adapted for more than ten markets. Used Amplitude to find where new holders dropped off, then rewrote the onboarding around what the data showed rather than what we assumed.
Built go-to-market from nothing for an early-stage platform: positioning, partner outreach, the company's first webinar programme, and activation tracking by channel so the team could see which sources produced users who actually stayed.
Owned CRM for a 600,000-member database across 150+ locations: email, SMS, push and direct mail. Built segmentation and reactivation, launched four trigger journeys (welcome, abandonment, win-back, post-purchase) as an always-on layer beside the promo calendar, and tested relentlessly on subject lines, offers and send times.
Promotional and loyalty campaigns across a national chain with a multi-million member programme, from creative brief to in-store launch. Piloted new mechanics in a fifteen-store test group before anything went national.
Loyalty and app-based offers executed on the floor rather than in a deck, plus reputation management across review platforms and a roster of local partners and ambassadors.
A tool in Python and Tableau that simulates campaign experiments across open, click and purchase rates, with the output already shaped for a readout.
Research into turning polyethylene waste into textile for sneaker production, looking at whether circular material systems hold up outside a pitch deck.
Sustainability programme work on reusable packaging: how a refill model reaches a shopper who has spent their whole life throwing the container away.
Most campaigns ship and nobody agrees in advance what would count as working. I set the hypothesis first, pilot before scaling where that's possible, and come back with a straight answer, including when the answer is that it didn't work.
Welcome, abandonment, win-back, post-purchase. Built once, running in the background, catching the people your promotional calendar was never going to reach. This is usually the cheapest revenue in the business and the last thing anyone builds.
Not a dashboard nobody opens. Which cohort is leaving, when, and what to do about it, in language the commercial team can use. I cut one reporting cycle from a week to a day because a report that arrives late isn't a report.
At a 150-location business I was the only marketer on the team: concept, collaborations, events, POS setup, site and app, and the reporting on top. If your team is small, I'm used to owning a lot of surface area at once.
You have customers leaving and no clear answer why. I find where and when.
You know you need onboarding, win-back and post-purchase flows. I build them.
Loyalty programme design, a migration between platforms, or a CRM function that needs standing up from scratch.
Bring the data, or bring the suspicion that something is wrong with it. Either works.