Analytics
Do ads move your sales — or is it price?
Everyone has a theory about what drives the numbers. Regression settles it: it measures how much each factor moves your outcome, and puts a p-value on each so you know which effects are real and which are noise.
Get this in Google Sheets →The model
Fit Sales = b0 + b1·(Ad spend) + b2·(Price) across your history. The full output includes R², coefficients, p-values, and confidence intervals.
| Outcome (Y) | Sales |
| Possible drivers (X) | Ad spend, Price |
| History | 8 periods |
| Fits | Y = b0 + b1·X1 + b2·X2 |
What Sortia tells you
What the regression finds:
Fit (R²)0.99
Ad spend+5.5 /$1 · p<.001
Price−15.6 /$1 · p=.003
Both real?Yes
Both are genuine drivers here: each extra $1 of ad spend adds about 5.5 in sales (p < 0.001), and each $1 of price costs about 15.6 (p = 0.003), together explaining 99% of the variation. A p-value under 0.05 means it is signal, not luck. Replace the columns with your own outcome and suspects — sales, tips, sign-ups, anything.
Try it in your own sheet
- Open Sortia in Google Sheets and choose Start from a template.
- Pick What Actually Drives Your Sales? — the model loads with the inputs filled in.
- Change the assumptions to fit your situation and press Run.