Solutions / Price monitoring

Competitor & price monitoring

Competitor prices, stock and assortment are collected every day, matched to your own SKUs, and turned into a short list of the products where your price is actually wrong.

E-commerce Pricing Data
Daily refresh
not a spreadsheet from last quarter
Matched SKUs
so comparisons are real
Alerts, not dashboards
only where price is off
Who it is for

E-commerce and retail teams whose category managers still rebuild a competitor price sheet by hand every week.

Short answer

We collect competitor prices, availability and assortment on a schedule, match their listings to your catalog so you are comparing the same product rather than a similar name, and surface the small set of SKUs where you are leaving money on the table or losing the sale. The output is a ranked action list and alerts, not another dashboard nobody opens.

The problem

The price sheet is out of date the day after it is built, and nobody has time to rebuild it.

01

Manual checks cover a fraction of the catalog

A person can check the top hundred SKUs. The other nine thousand drift, and the drift is invisible until margin or conversion moves for reasons nobody can explain.

02

Names do not match, so comparisons lie

The same product sits under four different titles across four sites. Comparing by title produces confident numbers about the wrong pairs, which is worse than having no numbers.

03

Data arrives without a decision attached

A daily export of ten thousand competitor prices is not information. Someone still has to find the twenty rows that matter, and that someone rarely has the time.

How it works

From trigger to result, step by step.

01

Pick the competitors and SKUs that decide margin

Not the whole market and not the whole catalog. The set that actually moves revenue is usually small, and starting there gets a working system in weeks instead of quarters.

02

Collect on a schedule, resiliently

Structured feeds and marketplace APIs where they exist, careful scheduled collection where they do not, with retries and change detection so a layout change is noticed rather than silently returning stale numbers.

03

Match products, not titles

Barcode, MPN and brand plus model first, then attribute and text similarity for the rest, with a confidence score. Low-confidence matches go to a person once and are remembered afterwards.

04

Turn prices into rules you agree with

Your rules — floor margin, MAP, position against named competitors, category exceptions — decide what counts as wrong. The system flags violations; it does not invent a pricing strategy.

05

Alert where the work happens

A daily ranked list in Slack or email, an export into your ERP or pricing tool, and a weekly view of who moved. Prices are changed by your team, or automatically only inside limits you set.

Before / after

What changes on the ground.

Today, by hand
×A hand-built competitor sheet, already stale
×Only the top SKUs ever checked
×Comparisons made by product title
×Ten thousand rows, no decision
With the automation running
Prices, stock and assortment refreshed daily
The whole tracked catalog covered
SKU-level matching with a confidence score
A ranked list of what to reprice today
What you get

Delivered, not demoed.

A collection setup for your chosen competitors and marketplaces
SKU matching by barcode, MPN and attributes with confidence scores
Your pricing rules encoded, including floors and MAP
Daily ranked alerts into Slack or email and export to your ERP
Change history so you can see who moved first and by how much
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

Python Playwright PostgreSQL n8n Metabase Slack Shopify 1C
Time to production4–8 weeks
Build priceFixed price
First stepFree mini-audit
Honest limits

When this is not the right solution.

·If you sell products nobody else carries, there is nothing to compare and the honest answer is elasticity testing, not monitoring.
·If a marketplace forbids collection in its terms and offers an API, we use the API; where neither is possible we will say the source is out of reach rather than build something fragile.
·If nobody owns pricing decisions, better data changes nothing. The bottleneck is the decision, not the number.

Questions we get about this one

Publicly displayed prices are generally collectable, and rules differ by country and by a site’s terms. We prefer official feeds and APIs, respect robots and rate limits, and tell you plainly which sources we consider safe to include.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.