Two Numbers That Looked Close Enough to Share a Formula, Until I Checked

The forecasting tool projected two things off the same underlying curve: how many contacts to expect by end of day, and how many of those contacts would turn into appointments. It had always used the same shape for both — reasonable on its face, since appointments obviously depend on contacts happening first.

I stopped to check whether that assumption actually held, instead of continuing to trust it because it sounded right. It didn’t. Appointments don’t track contacts on the same clock — they consistently lag behind by somewhere between seven and fourteen tenths of a percentage point through the middle of the day, and only catch back up to the contacts curve late, somewhere around the last couple hours before close. Small numbers, but a real, consistent, measurable gap, not noise.

The surprise wasn’t that the numbers were different — it’s that they were close enough, for long enough, that nobody had gone and checked. A gap of about a percentage point doesn’t look wrong on a dashboard. It looks like reasonable variance. It took actually isolating the two curves side by side to see that the small daily gap wasn’t random, it was structural — the same shape, the same size, showing up again and again.

The fix was straightforward once the gap was confirmed: train a second curve, specific to appointments, instead of reusing the contacts one. The harder part was the discipline of not assuming “close enough” meant “the same,” especially for a relationship that seemed obviously true on the surface — of course appointments follow contacts, so why would they need their own curve. Obvious and correct aren’t the same claim, and I’d been treating them as if they were.

The lesson: when two metrics are related but not identical, sharing a model between them is a convenience decision, not a correctness decision — and it’s worth checking which one you actually made.

Next: watching whether the new, separate curve holds up as more real days accumulate behind it, since one confirmed gap isn’t the same as a fully proven pattern yet.