The Problem: Tracking User Progress Across a Dynamic App
I was working on a Bubble.io subscription and venue check-in app that used geofencing to track when users arrived at specific locations. The concept was straightforward enough — members check in, their status updates, and the app reflects their engagement level in real time.
But before any of that could go live in Bubble.io, I needed to define the underlying data architecture. Specifically, I had to design a custom status system using unique data fields and values in Excel. The idea was to map out every possible user state — active, checked in, lapsed, pending, and more — along with the field names, data types, and trigger conditions that would eventually power the app's logic.
I figured it would take a few hours. It ended up being one of the more complex spreadsheet challenges I had faced.
Why Excel Felt Like the Right Starting Point
Using Excel to plan data fields before building in a no-code platform like Bubble.io makes a lot of sense. You get a flexible workspace to define field names, value types, and status rules without being locked into the platform's UI while still figuring things out.
I started building a master sheet — columns for field name, data type, allowed values, default state, and update triggers. For a simple app, this would be manageable. But our requirements kept growing. We had membership tiers, geofence-based check-in rules, manual override states, and engagement scores that affected which status a user held at any point.
The deeper I went, the more interdependencies I uncovered. A single status like "active" meant different things depending on the membership tier and whether the user had checked in within a rolling 30-day window. Trying to flatten all of that into clean, non-redundant data fields without creating circular logic was genuinely difficult.
Where It Got Complicated
The core challenge with custom status systems in Excel is not just listing fields — it is making sure the values across fields are consistent and that each status can be derived from a clean set of conditions. When I started cross-referencing membership states with check-in history and engagement thresholds, the sheet became hard to read and even harder to validate.
I also had to think about how these Excel-defined fields would eventually map to Bubble.io's data types. Not every Excel value translates cleanly. Dates, booleans, option sets, and list fields all behave differently inside Bubble.io, and designing them incorrectly at this stage would create rework later.
After a few days of iteration, I had a partially working model but no confidence that it was complete or correctly structured for what the development team would need.
Bringing In Helion360
I came across Helion360 while looking for structured support on data-heavy Excel work. I explained the situation — a Bubble.io app in progress, a status system that needed to be fully mapped using unique data fields and values, and a data architecture that had to be developer-ready.
Their team understood the requirement immediately. They did not just clean up my existing sheet — they restructured the entire approach. They built a well-organized Excel workbook that separated the status definitions, field-level specifications, value constraints, and conditional logic into clearly labeled sheets. Each custom status was tied to specific field combinations, and every data field was annotated with the expected data type and its Bubble.io equivalent.
The geofencing-related fields, in particular, were handled with a level of precision I had not managed on my own. Latitude and longitude boundaries, check-in timestamps, and proximity flags were all accounted for as distinct fields with appropriate value ranges and fallback states.
What the Finished System Looked Like
The final Excel workbook was something the development team could actually use. Custom statuses were fully defined, every data field had a clear name and type, and the value logic was documented in a way that left no room for interpretation.
What I took away from this was a better understanding of how to approach data modeling before building — not as a rough sketch, but as a precise reference document. The difference between a messy status sheet and a developer-ready one comes down to structure, consistency, and thinking through edge cases before they become bugs.
If you are building something similar — a membership app, a check-in system, or any platform where user states need to be tracked across multiple conditions — and the Excel data architecture is getting away from you, Helion360 is worth reaching out to. They stepped in at exactly the right moment and delivered work that moved the project forward cleanly.