The limitations of Excel for tracking competitor prices
Fabrice Decroo
Consulting Director
August 16, 2026
Excel isn't the problem as long as the volume of references, the number of competitors tracked, and the update frequency remain limited: the problem arises when this volume exceeds what a shared spreadsheet can handle without silently deteriorating, in four specific areas— data freshness, product matching, governance, and the hidden cost of human time.
91% of the complex operational spreadsheets audited contain at least one significant error—a figure that should serve as a warning whenever a price monitoring file is populated by multiple contributors working under tight deadlines.
“We have a shared file—that’s enough” is a phrase we still hear in most pricing committees. It’s not wrong at the outset: to compare the prices of three competitors across about fifty SKUs, a spreadsheet does the job—and does it well. The problem is never Excel itself—it’s the point at which the volume of SKUs, the number of competitors being tracked, and the frequency of updates exceed what a shared file can structurally handle without silently breaking down. This guide details the four practical limitations that arise at scale— data freshness, product matching, governance, and the hidden cost of human time —and provides an operational threshold to help you determine whether you’ve already crossed them.
Why Excel Remains, Even Today, the Default Tool for Price Monitoring
Excel dominates price monitoring because it’s already there—it’s free with the Office license that’s already in use, familiar to every employee, and flexible enough to get started without an IT project or a dedicated budget. For a category manager launching their first price comparison across one or two categories, a shared file—containing product references, competitor prices, and the date of the price check—is a perfectly rational choice: data is collected manually by browsing competitors’ product pages, copying the listed prices, and pasting them into columns that are manually timestamped.
This tendency is neither an isolated phenomenon nor statistically insignificant—it is, with a few minor exceptions, the market norm.
According to the4th edition of the Global Pricing Maturity Study (EPP & Vendavo, results published in January 2023, 2022 data), some companies still manage their pricing using Excel rather than a dedicated pricing tool.
The same survey shows a decline rather than progress: 41% of companies manage their pricing at a basic maturity level (“level one”), which is 25 percentage points higher than in 2019. This is therefore not merely a resistance to change limited to a few laggards: it is a fundamental trend affecting retailers of all sizes, including some with structured pricing teams. The challenge is not a lack of willingness—it is the absence of a clear enough catalyst to justify investing in a dedicated tool as long as the spreadsheet “still works.”
This is therefore not a call for the systematic replacement of Excel—a flexible tool with no additional licensing costs that everyone already knows how to use. That is precisely the purpose of this article: to identify, without dogmatism, the four structural limitations that arise when the volume of data points, the number of competitors being tracked, and the stakes of the decision exceed what a spreadsheet can handle without silently breaking down. (If the concepts of price tracking and web scraping are not yet clearly defined, a dedicated article in this series explains them in detail.)
Limitation 1 — No real-time updates
An Excel file is a snapshot, never a live feed. Someone checks competing websites, copies the listed prices, pastes them into cells, and adds a timestamp to the row—then the file remains static until the next manual data collection session. With about 20 products and a single competitor, a weekly update takes about 20 minutes and remains perfectly manageable.
The problem arises with volume: with 500 SKUs tracked across 5 competitors, the same manual process takes several days of work. And the data collection frequency does not remain stable under this workload—it declines, shifting from daily to weekly, then to monthly, precisely at the time when categories with high promotional volatility (home appliances, clearance clothing, back-to-school sales) would require the opposite.
Let’s consider a concrete example: a retailer informally tracks a competitor’s prices in a promotional category. The competitor launches a 20% off promotion on a Monday morning. If the retailer’s Excel spreadsheet isn’t updated until the following Friday, virtually the entire sales week has been based on a price assumption that was already incorrect as of Monday at noon. This is not a rare occurrence: it’s simply how a system fixed to a weekly cycle operates when applied to a market that is constantly changing.
The growing gap between the moment a competitor changes its price and the moment your database reflects that change is never just a minor issue. Every pricing decision made during that interval is based on information that is already out of date—and there’s nothing in the database itself to indicate just how outdated that information has become.
Constraint 2 — No reliable manual product matching
Comparing your own product to a competitor’s involves deciding, for each line, whether a seemingly trivial but actually significant question is true: Is it really the same product? Same packaging, same variant, same sales unit. When done manually, this assessment works reasonably well for up to a few hundred products. Beyond that, it becomes a source of errors that go undetected, because nothing in a spreadsheet flags a comparison as incorrect.
The risk isn't in the obvious cases—it's in the borderline cases, the ones that a hurried eye overlooks:
- A 6-pack was mistakenly compared to a 4-pack of the same product—the apparent price difference does not exist; it is simply a difference in packaging.
- A private-label brand confused with the equivalent national brand, distorting a comparison that makes no economic sense.
- A promotional price that has been raised and displayed as the regular price, making a competitor appear structurally cheaper than it actually is.
That is where the real danger of manual matching at scale lies: it does not produce a visible error in the dashboard—the cell fills in, the number looks normal—but it does result in a pricing decision based on the wrong reference. Undetectable without a dedicated audit, this error silently spreads through every arbitrage calculation that relies on it, without triggering any alerts.
Limit 3 — No governance or traceability
A shared Excel file does not answer any of the questions that an internal audit—or an external audit—might one day ask: Who changed this competitor’s price, when, and on what basis? Each new entry overwrites the previous version, unless a strict manual version-naming system is in place. When multiple contributors edit the same file, they produce conflicting copies that are sent via email, and no one knows which one is the authoritative version.
At the level of an individual category manager, this lack of clarity remains manageable—it’s up to them to figure it out. At the level of a multi-brand or multi-country network, with multiple negotiators feeding into the same system, this lack of clarity becomes a governance issue: there’s no reliable traceability of decisions, no audit trail, and no separation of responsibilities between those who collect, validate, and apply the final price.
This is not a mere administrative detail. A pricing decision that is contested internally—or reviewed as part of a regulatory audit—must be traceable: the input data, the date, the author, and the rule applied. On this scale, an organization should be able to answer three simple questions without hesitation:
- Who changed this competitor's price, and when exactly?
- On what basis was the decision to align—or not to align—made?
- Was this change approved by the right person with the appropriate access rights?
A spreadsheet program does not build in any of these safeguards by default; they must be enforced manually, and discipline breaks down as soon as the first copy is sent outside the process.
Limit 4 — The Hidden Cost of Human Time
The time spent maintaining a price monitoring file—collecting, pasting, verifying, and reconciling duplicates across copies from multiple contributors—does not appear on any line of the P&L. Yet it is very real, and it directly competes with the time that the same team could devote to analysis and decision-making rather than repetitive data entry.
Of the actual operational spreadsheets audited since 1997, at least one contained a material error, compared with 24% of all audits recorded between 1987 and 2000—the difference can be attributed to more rigorous audit methodologies in recent years (Raymond R. Panko, University of Hawaii, EuSpRIG, 2000).
These figures do not specifically pertain to price monitoring files—they pertain to operational spreadsheets in general. However, a competitive monitoring file maintained by multiple contributors, cross-referencing multiple sources, and updated under time pressure fits the exact profile of the complex, multi-author spreadsheet where these error rates are most concentrated, according to the same research: the same study shows that spreadsheet developers systematically underestimate their own error rates, regardless of their level of experience.
The hidden cost, therefore, is not just the time spent updating the file—it is that time, plus the errors that this limited time prevented from being detected. It is this dual cost—time and reliability—that must be factored into the calculation, not just the listed price of a software subscription compared to an Office license that has already been paid for.
Excel vs. a Dedicated Solution: A Comparison
| Criterion | Excel | Dedicated Solution |
|---|---|---|
| Freshness | Still photo | Real-time + alerts |
| Matching Reliability | Human Judgment | Confidence Score |
| Governance & Traceability | No native audit | Complete audit trail |
| Scaling | Reach the limit quickly | Millions in prizes |
| Total Actual Cost | High hidden costs | Measurable ROI |
Put simply: Excel’s stated cost—zero, or the marginal cost of an Office license that’s already been paid for—masks its true cost, which includes hours spent on manual data collection, time spent reconciling different versions, and the rarely quantified but very real cost of decisions made based on approximate matches or outdated data. A dedicated solution like the one Booper offers for price tracking and web scraping has a visible and immediate subscription cost; Excel has a hidden cost that grows nonlinearly with volume.
It's not a problem with the tool, but with the threshold
Nothing in this article suggests that Excel is a bad tool in and of itself. The correct frame of reference is not “Excel versus dedicated software”: it’s a threshold of volume and complexity beyond which manual processing changes in nature. Operational benchmark: tracking a single competitor across fewer than 100 SKUs remains reasonably manageable manually. Beyond 3 to 4 competitors, a few hundred SKUs, or whenever a fast-moving category requires daily updates, a manual system becomes structurally the wrong choice—not due to team negligence, but because of the nature of the medium.
Transition doesn't happen by making a sudden switch. The approach that works involves moving forward in stages:
Identify the priority category
The one where the margin at stake is highest—high promotional volatility or a significant impact on revenue—not the entire catalog all at once.
Automate this segment first
Data collection, matching, and alerts within this limited scope, to validate the system's reliability before expanding it more broadly.
Expand gradually, category by category
By keeping the rest of the catalog in Excel for now, as long as its size does not yet warrant a complete transition.
The complete method for structuring this seamless scaling process is covered in another article in this series, which focuses onthe large-scale industrialization of competitive intelligence.
At Booper — The Structured Antithesis of Excel: Governance, Matching, and Alerts in a Single Workflow
The GENIUS Admin module provides precisely what a spreadsheet cannot offer by design: granular management of users and permissions, centralized business rules, and a comprehensive audit log for every pricing decision. Upstream, GENIUS Link automates product matching using NLP with a confidence score for each SKU, and GENIUS Monitoring centralizes alerts rather than sending a raw stream that needs to be sorted. It is this path—Excel, then rule-based logic, then hybrid AI, then automation—that Barbotteau Group, a leading retailer in the French Caribbean with more than 70 companies and brands, has embarked upon after years of relying on Excel, in order to regain visibility into its prices and product assortments. On an entirely different scale, it is this same governance approach that now enables Coopérative U to organize several million prices per year across more than 1,700 stores—where the sheer volume made any analysis or simulation unmanageable before the dedicated tool was introduced.
So the question to ask isn't "Should we stop using Excel?" but "At what volume does my current system pose more risk than it provides visibility?" Find out how Booper structures this entire process—from data collection to governed management—on our price listings & web scraping page.
A Checklist Before Switching from Excel to a Dedicated Tool
- Do you track more than 300 to 500 products across more than 2 to 3 competitors?
- Does the time spent updating your file exceed the time spentanalyzingit?
- Can you specify exactly who changed which competitor's price, when, and on what basis?
- Is your product matching based on unquantified human judgment, without a confidence score?
- In a fast-moving category, does it take more than 24 hours from the time a competitor changes its price until that change is detected?
Frequently Asked Questions
In practice, as long as there are no more than a few hundred SKUs for a single competitor, a well-maintained shared file remains manageable: with about 20 SKUs and one competitor, a weekly update takes about 20 minutes and remains perfectly manageable.
This article establishes a more precise operational benchmark: when there are more than 300 to 500 product listings or 3 to 4 competitors being tracked simultaneously, the workload involved in manual updates and monitoring exceeds what a team can handle without compromising the timeliness or reliability of the data.
The problem isn't straightforward: with 500 SKUs tracked across five competitors, the same manual process would take several days of work, and the frequency of data collection would inevitably decline—from daily to weekly, then to monthly—at the very moment when the most volatile categories would require the opposite.
This threshold is therefore not arbitrary: it corresponds to the point at which the volume structurally exceeds what a spreadsheet can handle without silently breaking down, not to a judgment on the competence of the team managing it—hence the importance of carefully selecting which products to monitor as a priority before increasing the number of items tracked.
Because they are invisible in the file itself: the cell fills in, the number looks normal, but it is comparing two references that are not identical—packaging, variant, or unit of sale.
This article details three specific cases that might be overlooked by a casual observer: a 6-pack mistakenly compared to a 4-pack of the same product, a store brand confused with the equivalent national brand, or a promotional price listed and displayed as the regular price, which makes a competitor appear structurally cheaper than it actually is.
This risk increases with volume: when done manually, this approach works reasonably well for up to a few hundred items; beyond that, it becomes a source of errors that cannot be detected, due to the lack of a confidence score for each comparison.
The error never shows up in the dashboard: it manifests itself in a pricing decision made for the wrong product—a decision that cannot be detected without a dedicated audit—and silently spreads through every pricing decision that relies on it. Hence the importance of an explicit method for ensuring reliable product matching, with or without EAN codes.
When at least one of these three indicators arises: the update time exceeds the analysis time; a fast-moving category requires daily updates that Excel can no longer handle; or a pricing decision must be traceable —who, when, and on what basis—for an internal or external audit.
This article also establishes a benchmark for volume: once the number of SKUs being tracked exceeds 300 to 500 across more than 2 to 3 competitors, the nature of manual processes changes fundamentally, regardless of the quality of the team carrying them out.
The most telling sign is often the hidden cost of time: when collecting, compiling, and reconciling duplicates across contributors takes more hours than the analysis itself, the pricing team spends its time maintaining the file rather than making decisions.
This is never a definitive judgment on Excel: it’s a threshold of volume and complexity beyond which the platform structurally becomes the wrong choice—not a matter of individual skill. The guide for transitioning from Excel to a dedicated pricing solution details how to cross this threshold without disrupting existing processes.
Yes, and that's often the best way to make the transition without disrupting everything all at once: this article recommends first identifying the priority category—the one where the margin at stake is highest, whether due to high promotional volatility or a significant impact on revenue.
The method consists of three steps: first, automate this portion to verify the system’s reliability; then gradually expand, category by category, while keeping the rest of the catalog in Excel until its volume warrants a complete transition.
This is precisely the path taken by the Barbotteau Group, the leading retailer in the French Caribbean with more than 70 companies and brands: Excel, then rule-based logic, then hybrid AI, then automation—a gradual ramp-up following years of management historically conducted using spreadsheets.
This phased approach limits project risk: value is demonstrated within a controlled scope before exposing the entire catalog to a new system, following a structured method for monitoring competitor prices —objective, scope, frequency, governance.
It is the ability to know, at any given time, who made which pricing decision, when, based on what input data, and according to what rule—with differentiated access rights for those who collect, validate, and implement the decisions.
This article raises three questions that an organization should be able to answer without hesitation: Who changed this competitor’s price and when? On what basis was the decision to match (or not match) the price made? And was this change approved by the right person with the appropriate access rights?
A shared spreadsheet does not provide any of these safeguards by default: each record overwrites the previous version—unless users follow a strict naming convention—and when multiple contributors edit the same file, they create divergent copies, and no one knows which one is the authoritative version.
The GENIUS Admin module described in this article illustrates the tangible benefits of governance: granular management of users and permissions, centralized business rules, and a comprehensive audit log for every pricing decision—a cornerstone of a pricing organization with clearly defined roles.
They do not specifically address fare monitoring files, but rather complex operational spreadsheets in general: this article cites the seminal research on the subject, by Prof. Raymond Panko (University of Hawaii), which has documented these error rates since the 1990s.
The figure is striking: 91% of actual operational spreadsheets audited since 1997 contained at least one significant error, compared with 24% of all audits recorded between 1987 and 2000—a discrepancy that the research attributes to more rigorous audit methodologies in recent years.
A competitive intelligence file fits the spreadsheet profile exactly where these rates are most concentrated: maintained by multiple contributors, cross-referencing multiple sources, and updated under tight deadlines—the typical profile of the complex, multi-author spreadsheet identified by this research.
The same study raises another red flag: spreadsheet developers consistently underestimate their own error rates, regardless of their level of experience—a bias that therefore affects even the most rigorous pricing teams and aligns with a broader observation about the true glass ceiling in pricing data.
Also in this series
- What is price monitoring or web scraping in retail?
- Web scraping, field audits, consumer panels: what are the differences?
- How to Scale Up Competitive Intelligence on a Large Scale
Sources: 4th Global Pricing Maturity Study, EPP & Vendavo, 2022 · Spreadsheet Errors: What We Know. What We Think We Can Do., Raymond R. Panko, EuSpRIG, 2000 · Bringing Discipline to Pricing, McKinsey Quarterly, 2000.
Seven criteria distinguish a competitor pricing monitoring tool that simply generates a table of price differences from one that actually drives decisions: coverage, recency, product matching, alerts, governance, integration, and compliance. The listed cost is only part of the total cost: manual reclassification, maintenance of in-house development, and the opportunity cost of a poorly informed decision often outweigh the subscription fee.
A comprehensive competitor pricing monitoring system is built on five inseparable components: data collection, matching, alerts, reporting, and governance—if even one of these components is missing, the system becomes ineffective. The retail sector revises its prices more frequently than any other (ranging from monthly to daily, depending on the category), which requires a system capable of keeping pace with this frequency.
Many organizations receive a report on competitor price gaps every morning—but few have a genuine strategy. The difference lies in three questions asked before implementing the system: Why collect this data? What exactly should be tracked? And what decisions should be made once a price gap is identified?
Key point: Key value items (KVI) —the products for which customers remember the price—typically account for 15 to 25 percent of a category’s sales. Focusing monitoring efforts on this small core group is more cost-effective than trying to track everything with the same intensity.