Running Two Dog Hotels Means Twice the Data to Manage
When you operate a single location, keeping tabs on performance is manageable. When you run two dog hotels simultaneously, the data starts to pile up fast. Between booking rates, invoicing totals, occupancy by kennel type, and repeat customer trends, I was drowning in numbers that lived inside our Gingr software and nowhere else.
Gingr is genuinely well-built for day-to-day pet care operations. But pulling meaningful weekly or monthly KPIs out of it in a format I could actually review at a glance? That was a different problem entirely.
The Problem With Raw Exports
Gingr allows data exports, which sounds helpful until you open the file. What comes out is a flat database — rows and rows of booking records, customer entries, and invoice line items. There is nothing wrong with that raw data. The issue is that it does not tell you anything on its own. I needed to see occupancy trends week over week, compare revenue between both locations, track average booking value, and flag when repeat customer rates were slipping.
I started building the dashboard myself. I know my way around Excel well enough to write basic formulas and set up a few pivot tables. I got maybe a third of the way in before the complexity of what I actually needed became clear. Keeping data from two locations cleanly separated, building dynamic date filters that updated automatically, and then making the whole thing look clean enough to read in five minutes each Monday — that combination was beyond what I could realistically deliver on my own without spending weeks on it.
Handing It Off to Someone Who Could See the Whole Picture
After spending a few evenings going in circles with nested IFs and mismatched table references, I reached out to Helion360. I explained the setup — two locations, weekly Gingr exports, the KPIs I cared about, and the fact that I needed this to be something I could update myself without rebuilding it every time.
Their team asked the right questions upfront. They wanted to understand which metrics were actually decision-driving versus which were just nice to have. That distinction shaped the entire structure of what they built.
What the Finished Excel Dashboard Actually Does
The dashboard Helion360 delivered covers the core KPIs I needed across both locations in a single view. Booking performance is tracked weekly and monthly, with a rolling comparison so I can see whether this week is up or down relative to the same period last month. Revenue is broken out by location and by service type — daycare, overnight stays, grooming add-ons — which I could never see clearly before.
Customer data is handled through a separate tab that feeds into the main dashboard. It tracks new versus returning customers, average spend per visit, and a simple retention indicator that flags if repeat booking frequency is changing. All of it updates when I paste in a fresh Gingr export. No manual re-entry, no formula rebuilding.
The design is clean and straightforward. Color-coded KPI cards at the top give me a quick read before I even scroll down. The charts below are there when I want more detail, but I do not have to look at them every time.
What I Learned From This Process
The biggest takeaway for me was that Excel automation is genuinely its own discipline. Building a dashboard that looks clean, stays stable when data changes, and is actually maintainable by a non-developer takes a specific combination of skills — database logic, Excel formula architecture, and layout thinking — that do not always overlap in the same person.
I also underestimated how much the design side matters. A dashboard that is functional but cluttered is one I will stop checking regularly. The version I have now is one I actually open every Monday morning without dreading it.
If you are in a similar spot — running a multi-location operation, sitting on clean data from your booking software but unable to turn it into a usable reporting tool — Helion360 is worth reaching out to. They handled the parts I could not, delivered something I can maintain on my own, and did it faster than I would have figured it out myself.