One price per competitor is not a price
Price zones, dealer-owned stores and local flyers mean the same SKU carries different prices across one city. A national read hides the stores where you are actually losing.
Every competitor's shelf price for every SKU you carry, at the stores that compete with yours, matched like for like. Lightfeed reads them on your schedule and catches the matching mistakes before they reach your index.
Competitor price monitoring software collects competitor prices for the products you sell and turns them into a competitive price index you can act on. For an ecommerce retailer that means one price per product per competitor website. For a chain of stores it means a price per product per competitor store, because zone pricing, dealer-owned locations and local flyers put different prices on the same shelf tag across one city.
Lightfeed is retail price monitoring built for that second case. It finds the listings from your product file, reads each store the competitor lets a shopper select, matches exact and comparable products, and keeps the evidence and history behind every number in the index.
Retail price monitoring built for ecommerce reports one price per competitor and only the SKUs you both sell. Neither holds for a chain of stores.
Price zones, dealer-owned stores and local flyers mean the same SKU carries different prices across one city. A national read hides the stores where you are actually losing.
Competitors sell a different pack, a different brand or their private label. Matching identical UPCs only leaves most of the shelf, and most of the price gap, out of the index.
Someone pastes URLs, the retailer redesigns its site, the links break, and the same pairs get re-checked every quarter. The analyst becomes the pipeline.
Item codes, UPCs, supplier codes, descriptions, your shelf price. A zone or banner column if you price by zone. CSV or API.
By store number or address. Every store a shopper can select on the competitor's site is a store Lightfeed can read.
Exact by UPC, model or supplier code. Otherwise up to three comparables with a quantity ratio, judged from images and specs. Only uncertain pairs reach your reviewer; decisions persist.
Price, promotion and its end date, stock, per store. Index by competitor, category, brand and zone. Alerts on what moved since the last run.
A 42-SKU beauty catalog read across 12 US stores on public catalogs. 71% of the SKUs were priced differently by store, and the expensive store was not the one the team would have guessed.
| Product | Store | Shelf price |
|---|---|---|
| Neutrogena Hydro Boost Water Gel 1.7 oz | Target · Chelsea, New York | $22.49 |
| Target · Streeterville, Chicago | $19.99 | |
| Target · Westwood, Los Angeles | $25.49 |
327 of 460 items on the shelf at the three Target stores. A national feed gives you one number; there were three.
The whole assortment across 12 stores, 283 matched like for like, the rest kept for the assortment view.
Bundles, wrong sizes and refills were turned away before they reached the index. 27 uncertain pairs went to a person.
| View | What it answers |
|---|---|
| Price index | Where you stand against each competitor, by store, zone, category and brand. KVI-weighted if you want, shelf or member price as the basis, out-of-stock listings excluded and counted separately. |
| What changed | Price up or down, promotion started or ended, out of stock, back in stock, since the last run, with the previous value next to the new one. |
| Comparable products | Up to three per competitor when the exact SKU is not sold, with the quantity ratio and the evidence, so private label and alternate packs are in the index at the right coefficient. |
| Assortment gaps | What a competitor carries that you do not, what only you carry, brand and private-label share by category, for line reviews. |
| Evidence | The listing, price, store and timestamp behind every number, so a buyer can take it into a supplier negotiation. |
| Delivery | The web app, CSV exports, a JSON API for your warehouse or BI tool, and an MCP server for Claude or your own agents. Enterprise adds Snowflake, BigQuery and Power BI delivery. |
The same engine for grocers: competitor banners store by store, plus the price your shoppers see on Instacart, DoorDash and Uber Eats.
Loblaw, Sobeys and Metro banners, Walmart, Costco, Kroger, Safeway and Whole Foods, read at the stores around yours, with flyer promotions and their end dates.
The marked-up price on Instacart, DoorDash and Uber Eats beside the in-store price, per store, so you see the gap your own delivery partner creates.
Daily reads for known value items during flyer weeks, weekly for the long tail, size-normalized so a 1.5 L compares to a 2 L correctly.
URL trackers such as Prisync and Price2Spy, enterprise suites such as Wiser, DataWeave and Competera, and syndicated panels such as Circana and NielsenIQ each solve a different problem.
| Syndicated panels | URL price trackers | Enterprise suites | Lightfeed | |
|---|---|---|---|---|
| Store-level prices | Sales data by market, not shelf prices by store | One national price per URL | Some, as a paid add-on or field audit | Every store a shopper can select, on every run |
| Finding the listings | Not applicable | You supply every URL | Onboarding project | From your product file, by identifier and search |
| Comparable products | No | Manual | Rules you maintain | Up to three per competitor, AI-judged, reviewed once |
| Promotions and stock | Lagged, aggregated | Price only, sometimes stock | Yes | Promo and its end date, stock on hand, per store |
| Time to first data | Months | Days, after you collect the URLs | Weeks to months | Days |
| Price | Six figures a year | Per URL, per frequency | Custom, usually five figures a month | $699 a month for 100,000 products |
Send us 50 SKUs and three competitors. We run them store by store, match them, and walk you through the index on a 30-minute call. No contract, no setup fee.
Book a 30-minute demoPrefer email? support@lightfeed.ai