CASE STUDIES

Partnered with the Town Manager and their parking vendor to move beyond surface-level metrics. 

We ditched the canned dashboards and ran real price sensitivity analyses to understand what visitors were actually willing to pay — and when. We looked at historical patterns, blended in weather, time-of-day effects, and even special event overlays to fine-tune pricing recommendations. The result? A pricing model that feels fair to locals, profitable to the town, and adaptable in real time. 

 Worked with external marketing partners to move past vanity metrics and into ROI.
Impressions and click-thru rates are cute — but we went deeper. By overlaying ZIP-level booking and retail data, we surgically cut out underperforming audiences and reallocated spend to regions that actually convert. We didn’t just report performance — we fixed it. Every marketing dollar now works harder because it’s backed by real outcomes, not just pretty charts.

Helped the Promotions and Events Committee shift from intuition-based scheduling to laser-targeted event planning.
We blended data from past events, retail traffic, weather patterns, parking demand, and marketing results to identify the sweet spot for future events — right down to the ideal date and demographic. No more guessing who might show up or when. Now, events are planned for the right audience at the right time — and it shows in attendance, revenue, and local buzz. 

 Foot-traffic tools and the like often stop at visualization — we go deeper.
You can look at dwell times and heat maps all day, but what do they actually mean?
We dive into the raw data behind the visuals and blend it with retail, weather, booking trends, and ZIP-code-level intelligence to complete the story.
Then we translate insight into action — what to promote, when to promote it, and to whom — instead of just admiring the traffic. 

 Built collaborative forecasting tools with property managers to help local businesses prep for demand.
By combining reservation data (when people booked, where they’re from, how many are coming) with external data like school calendars, weather, and retail behavior, we built rolling forecasts for local shops and restaurants. That means better inventory planning, smarter staffing, and more efficient food prep — because a last-minute Saturday crowd shouldn’t be a surprise. 

 Helped local retailers align stock levels with actual foot traffic and visitor profiles.
Using forecasted visitor trends, point-of-sale data, and weather patterns, we identified inventory mismatches — like beachwear peaking after people had already gone home. One boutique reduced end-of-season markdowns by 27%, simply by reordering based on forecasted check-in dates and demographics. Inventory moves faster when it aligns with who’s actually in town.