EquipmentShare runs a national equipment business with a sizeable direct-to-customer online channel. At that scale, the same e-commerce system also handles branch transfers, employee purchases, manual phone-order entries, and other internal activity. Separating those out was the first thing we had to fix.
The challenge
Direct online performance had softened on one of EquipmentShare's main e-commerce surfaces, and leadership wanted to know why. The bigger issue was buried in the reports themselves: year-over-year comparisons were quietly blending real customer orders with internal operational activity. Branch transfers looked like customer purchases, employee accounts looked like new customers, the order desk's phone-entry workflow looked like organic web traffic.
Until that was separated out, no comparison could be trusted.
What we built
Instead of patching individual reports with one-off filters that would drift apart, we defined "real customer order" once, at the platform's data layer. The rules reflected EquipmentShare's actual operating model: branch codes, employee patterns, the order-desk phone-entry workflow, internal email domains. As the operational shape changed, the rules could change with it.
With the definition in one place, every downstream view picked it up automatically: leadership reports, ad-hoc investigations, future dashboards, every analysis we did afterward. When operational patterns shifted, the change landed once and propagated everywhere.
On top of that we built a comparison toolkit so the same question ("how is this period doing against last year, on a real-customer basis?") could be run by anyone, on demand. Month-over-month, trailing twelve months, custom date ranges. All of it going through the same definition of who actually bought.
What the clean data revealed
With clean data, the actual story showed up. The decline wasn't uniform across all customer activity. A specific channel had degraded: the order desk's phone-order workflow, normally one of the highest-value channels in the business, had quietly contracted over several quarters. That was the leading explanation for the overall revenue trend.
The recovery was real too. As the phone channel's underlying issues were addressed, the return showed up in the data month after month. When EquipmentShare later deployed a major Shopify theme update, the same toolkit was already in place to answer the next question: which channels held up, which ones needed closer attention.
What stays useful
What EquipmentShare keeps
- A single, platform-level definition of "real customer" built into the data layer. It survives team turnover, vendor changes, and reporting-tool changes.
- A comparison toolkit any analyst can run, for any time window: month, quarter, trailing twelve months, custom range.
- A documented diagnostic-to-recovery story leadership can take to the board.
- A repeatable method for evaluating future channel and platform changes (like the Shopify theme transition) against the same baseline.