Scrapers watch 1,200 competitor SKU pages overnight, diff every price against yesterday's, and land a 6:30am digest with repricing suggestions ranked by revenue impact. The team now responds to competitor moves in about four hours instead of two weeks.
Typical results
4 hrs
Time to reprice
1,200
SKUs watched nightly
+6%
Revenue on watched categories
6:30am
Daily digest delivered
Figures reflect typical results for this kind of automation.
The challenge
In consumer electronics, being 5% above a rival kills the sale. The team checked competitor prices by hand — a category per week when things were calm — so price cuts on fast-moving SKUs went unnoticed for two weeks. Sales dipped, nobody knew why, and blanket discounts burned margin on products that were already competitive.
Before and after
Time to reprice after a competitor move
−99%SKUs monitored
8×Manual price checking
−90%The automation
We deployed scrapers that visit competitor SKU pages every night between 2am and 5am, matching each product by model number. A pipeline diffs every price against yesterday's and against the store's own listing, flags SKUs that fell out of competitive range, and calculates a suggested price that respects each category's margin floor. At 6:30 every morning the team gets a digest ranked by revenue impact, with one-click approval pushing new prices straight to the store.
How it works
- 01
Nightly crawl
Scrapers visit competitor SKU pages between 2am and 5am, matching each product by model number.
- 02
Diff the prices
Every price is compared against yesterday's snapshot and against the store's own listing.
- 03
Flag the outliers
SKUs that fell out of competitive range get a suggested price that respects the category's margin floor.
- 04
Morning digest
At 6:30am the team receives a digest of overnight moves, ranked by revenue impact.
- 05
One-click reprice
Approved suggestions push straight to the store platform — no retyping, no spreadsheet.
Starts from
- Competitor SKU pages
- Own price list & margins
Delivers
- 6:30am morning digest
- Repricing suggestions
- Price position dashboard
The impact
Repricing that used to take two weeks now happens in about four hours, across 1,200 monitored SKUs instead of 150 spot-checked ones. Revenue on watched categories rose 6% while blanket discounts disappeared, and the weekly manual price-checking ritual shrank from ten hours to one.
Questions about this automation
How are competitor products matched to our own SKUs?
Each product is matched by model number, which stays the same across stores selling the same electronics. Scrapers visit competitor pages between 2am and 5am, and the pipeline compares every price to yesterday's snapshot and to the store's own listing. Products without a clear model match go to a short review list.
Can a suggested price fall below our margin?
No. Every suggestion respects a margin floor set for each category, so the system never proposes a price that loses money. When a SKU can't match a competitor within its floor, the digest flags it, and the team decides whether to hold the price or accept a thinner margin.
Do prices change on the store automatically?
Only after approval. The 6:30am digest ranks overnight moves by revenue impact, and the team approves suggestions with one click, which pushes the new price straight to the store platform. This is a multi-step build, usually rolled out in stages, starting with a few key categories.