The Challenge & Our Approach

From a broken baseline to a reimagined platform — what was missing, and how Ohio University built the solution.

Students had moved on. The system hadn't.

Five years ago, Ohio University Culinary Services recognized a widening gap between what students expected and what the dining technology could deliver. Students wanted convenience, personalization, and a mobile-first experience — and the existing platform wasn't built for any of that.

The question wasn't whether to change. It was how to build something that could actually grow with student expectations over time.

REVENUE AT THE START

~$100,000 / year

Online ordering existed — but had little room to scale and no mechanism to build student loyalty.

WHAT WAS MISSING

No loyalty program. Limited mobile experience. No data feedback loop to improve the system over time.

THE STAKES

As student expectations kept rising, the gap between the platform and reality was only getting wider — making the cost of inaction higher each semester.


A strong foundation, continuously improved.

The original Culinary Services team built OHIO Eats and the campus TMO platform from the ground up — earning recognition at the Illumia National Conference for what they created. When Application Administrator Brett Chambers joined three years ago, he focused not on rebuilding, but on scaling what was already working.

01 — ORDERING EXPERIENCE

Smoother, mobile-first ordering

Rebuilt the browsing and pickup flow to feel natural on mobile — reducing friction from discovery to order completion.

02 — REWARDS PROGRAM

A loyalty engine that drives return visits

Introduced targeted marketing strategies and refined the points and rewards store to create genuine repeat engagement.

03 — DATA & DECISIONS

A loyalty engine that drives return visits

Introduced targeted marketing strategies and refined the points and rewards store to create genuine repeat engagement.

04 — OPERATIONS ALIGNMENT

Usage insights driving every update

Real behavior data — not guesswork — guided each iteration, so improvements compounded over time.

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