Illustrative example. This case study describes a typical engagement to show how the work unfolds. It does not name a client and does not use real client figures. Specific results vary by retailer, category and competition.
A US retailer was pricing its catalog off a spreadsheet that a team member updated by hand - a few products, a few competitors, once in a while. Most of the catalog was never checked at all. A daily competitor price feed across all their key retailers replaced the guesswork with a complete, current market view the pricing team could actually act on.
Background: pricing off a stale spreadsheet
The retailer in this example is a mid-sized US business selling across several consumer categories, competing against a handful of well-known retailers on overlapping products. Pricing decisions sat with a small team that genuinely cared about getting prices right.
The problem was the tool they had. Competitor pricing lived in a spreadsheet that someone updated manually - opening competitor sites, copying prices, pasting them in. It worked, in the sense that it produced numbers. But it could never keep up with a real catalog in a real market.
The problem: manual checking does not scale
Manual price-checking fails for a simple, structural reason: a person can only check so many products across so many competitors before the data is already old. The retailer faced this directly.
Three specific pains came out of it:
- Only a fraction of the catalog was covered. The team checked their top products against one or two competitors. The long tail of the catalog was priced on instinct.
- The data was always stale. By the time a price was copied into the spreadsheet, competitors may have already moved. Decisions were made on yesterday's market, or older.
- It consumed real hours. The manual checking itself was a recurring time cost - hours each week spent collecting data instead of analysing it.
The hidden risk was not the products the team checked - it was the ones they did not. A large share of the catalog was being priced with no competitor reference at all, simply because manual checking could never stretch that far.
The solution: a daily multi-retailer price feed
The goal was to give the pricing team one daily dataset covering their entire catalog against all their key competitors - not a sample, the whole picture. That turns pricing from a guessing exercise into an evidence-based one.
We set up a managed feed matched to their catalog. Each day it captured competitor prices for every tracked product across the retailers they cared about, normalised into one clean, consistent dataset. It arrived every morning, ready to use - the approach behind our Retail Price Intelligence solution and delivered as a managed Data as a Service feed.
How the engagement worked
The project followed the same four-step path we use for most retail price intelligence engagements - structured so the team could validate the data before relying on it.
Scope catalog and competitors
We confirmed the product set, the list of competing retailers and the exact fields the pricing team needed.
Pilot dataset in 3-7 days
We delivered a validated pilot across a sample of products, so the team could check accuracy against what they already knew.
Scale to the full catalog
Once approved, we expanded to the complete catalog and competitor list, on a daily refresh schedule.
Ongoing managed feed
We monitor and maintain the feed as competitor sites change, so the team only ever works with the finished daily dataset.
The outcome: pricing on evidence, not instinct
The biggest change was not a single metric - it was that the pricing team stopped collecting data and started using it. Every morning they opened one dataset covering the whole catalog, instead of spending hours building a partial one.
In this illustrative engagement, that produced three practical shifts:
The pricing decisions themselves stayed with the retailer - the feed never set a price. What changed was the input: every decision now started from a complete, current view of the market instead of a partial, dated one.
"We always trusted our pricing instincts. We just never had the data to check them against. Now we do - for the whole catalog, every day."
Illustrative summary of the pricing team's perspective in this example engagement.The takeaway for US retailers
The lesson here applies to most US retailers competing on price: manual price-checking is not a small problem you can out-work - it is a structural ceiling. A person will always check too few products, too few competitors, too late.
A managed price feed removes that ceiling. It does not make pricing decisions; it makes sure every decision is made with the full market in view. That is the difference between pricing on instinct and pricing on evidence.
Frequently asked questions
Competitor price intelligence is the practice of regularly collecting competitor prices for the products you sell, so pricing decisions are based on the current market rather than guesswork or outdated checks.
Manual price-checking does not scale. A person can only check a handful of products across a few competitors, it is slow, and the data is already stale by the time it is collected. Most of a catalog ends up never being checked.
This is an illustrative example written to show a typical engagement. It does not name a client or use real client figures. Specific results vary by retailer, category and competition.
A managed price feed can cover a full catalog across many competing retailers at once. We confirm the exact product set and competitor list during scoping, then scale the feed to match.
For most retail price intelligence projects the feed refreshes daily, and a faster cadence is possible for fast-moving categories. We deliver a validated pilot dataset within 3 to 7 days before scaling.
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We build and run managed competitor price feeds for US retail and ecommerce teams - so pricing decisions start from a complete, current view of the market.