Making growth more consistent for a local winery

Context
A direct-to-consumer winery with a loyal following, but sales were heavily tied to release moments. Everything in between felt unpredictable, and it was hard to tell what was driving engagement and purchases.

What we did
We started by figuring out what programs were successfully driving conversions. We connected engagement and purchase behavior using Google Analytics to understand the connection between how customers engaged with the brand and when they bought

From there, we built a more intentional approach to marketing. Content became more consistent between releases so the brand stayed top of mind. Campaigns still supported launches, but we added more education to build a stronger connection with customers. Email design was revamped to be more consistent and brand-aligned, and automations were reworked to keep customers engaged over time.

What changed
Email open rates increased and overall email engagement improved as new automations and campaigns replaced ad-hoc blasting. Repeat purchases became more predictable, no longer spiking only around release moments. And for the first time, the team had a clear picture of which marketing activities actually moved the needle. This made making day-to-day decisions faster and less stressful.

Turning data into decisions for a food nonprofit

Context
A multi-location food nonprofit serving thousands of clients, with strong attendance but inconsistent retention. There was a lot of data, but no clear view of why attendance patterns were changing.

What we did
We analyzed attendance and retention patterns across locations and connected them to behavior over time. From there, we identified where clients were likely to drop off and what patterns drove that.

The most unexpected insight came from attendance groupings: clients who came with others were significantly more likely to return than those who came alone. This single finding reshaped how the team thought about programming, outreach, and how they measured success.

We translated all of this into simple dashboards so teams could quickly see where engagement was strong and where it dropped off.

What changed
Data that was once hard to interpret became something the team could actually use. They could spot drop-off points earlier, understand client behavior more clearly, and make better program decisions to improve engagement.

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