A customer bought last month. This month they did not come back. In between, you raised your price list 8%. The quick read is that the increase scared them off, and the team files that away for the next hike: “we need to go slower on price.” The problem is that customer might not have come back either way, increase or not, and nobody compared it against anything.
The price increase is the easy suspect, not always the right one
In August 2026, monthly inflation in Argentina was 1.7%, with a 33.5% year-over-year rate (INDEC). With numbers like that, repricing several times a year is not an aggressive pricing decision: it is normal operations for any local business. Which means that almost any month you look at churn, there is going to be a price increase nearby, whether or not it is the real reason a customer did not come back.
Every business loses customers every month for reasons that have nothing to do with price: they moved, they tried another brand, they stopped needing the product. That is baseline churn, and it exists with or without a price increase. The mistake is looking at the repurchase drop in the month of the increase and attributing the whole thing to price, without asking how much of that drop was going to happen anyway. It is the same underlying error that separates attribution from real incrementality when measuring a campaign: before counting a drop as the effect of something, you need to confirm it was not a drop that was already coming.
How to isolate the real effect of a price increase
- Log the exact date of every price increase, by product or category, not just today’s active price list. Without that date there is no way to build the comparison.
- Build a cohort of customers who bought that product right before the increase and measure their repurchase rate at 30 and 60 days.
- Compare that repurchase rate against the same product in a period with no price change, not against zero and not against a general average. The gap between the two, not the total drop, is the effect of the increase.
- Split by category: a frequently bought product shows the effect within a couple of weeks; one bought less often (appliances, seasonal apparel) can take months to show whether the increase mattered, because the customer’s next purchase in that category is further out.
With inflation running at 33.5% year-over-year, most Argentine businesses update prices more than once a quarter. Treating each increase as an isolated event, with no comparison, means measuring the same noise over and over and drawing a different conclusion each time. The same cohort logic already used to adjust LTV for inflation applies here: without a real baseline for comparison, any number in Argentine pesos tells whatever story you want to hear.
If the only signal that “the price scared off customers” is that you raised the list and repurchase dropped the following month, you are probably looking at the usual churn with a new explanation attached.