The Limitations of Excel for Monitoring Competitors' Prices

Fabrice Decroo

Director of Consulting

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 gets the job done—and does it well. The problem is never Excel itself; it’s when 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 as the system scales —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.

Table of Contents

H2 Headings

A spreadsheet grid with cracked edges, with an organized flow of data emerging from its center

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 spreadsheet (product reference, competitor price, date collected) 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.

30%

Some French retailers have still not invested in a dedicated pricing solution, even though nearly all of them already collect market data
(Diamart Groupe and PricingHUB, 2026 study).

The same study notes that nearly half of retailers still compare their products to those of competitors manually, without using a pricing matching tool. This is therefore not merely resistance to change on the part of a few laggards: it is a fundamental trend affecting retailers of all sizes, including some with dedicated pricing teams. The challenge isn’t a lack of willingness; it’s 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 that requires no additional licensing fees and 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 references, the number of competitors being tracked, and the stakes involved in 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.)

Limit 1, No real-time updates

An Excel file is a snapshot, never a live feed. Someone checks competitors’ pages, copies the listed prices, pastes them into cells, adds a timestamp to the row, and then the file remains static until the next manual data collection session. With about 20 product listings and a single competitor, a weekly update takes about 20 minutes and remains perfectly manageable.

The problem arises with volume: with 500, References Tracked s from 5 competitors, the same manual process would take several days of work. And the collection frequency does not remain stable under this workload; it deteriorates, shifting from daily to weekly, then to monthly—precisely at the time when categories with high promotional volatility (home appliances, markdown clothing, back-to-school season) 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 schedule 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 merely cosmetic. Every pricing decision made during that interval is based on information that is already out of date, and nothing in the database itself indicates just how outdated that information has become.

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Limit 2: No reliable product match found manually

Comparing your own product to a competitor’s involves deciding, for each line, whether a seemingly trivial—but actually not so trivial—question is true: Is it really the same product? Same packaging, same variant, same sales unit. Done manually, this assessment works reasonably well for up to a few hundred products. Beyond that, it becomes a source of error that goes undetected, because nothing in a spreadsheet indicates that a comparison is 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 is conflated 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 result in a visible error on the dashboard (the cell is filled in, the number looks normal); rather, it leads to a pricing decision based on the wrong reference. Undetectable without a dedicated audit, this error silently propagates 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 external audit) might one day ask: Who changed this competitor’s price, when, and on what basis? Each save overwrites the previous version, unless a strict manual version-naming convention is followed. When multiple contributors edit the same file, they create divergent 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—he’s the one who knows best. At the level of a multi-brand or multi-country network, with several negotiators feeding into the same system, this lack of clarity becomes a governance issue: there is no reliable traceability of decisions, no audit trail, and no separation of responsibilities between those who collect data, those who validate it, and those who set the final price.

This is not just an 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. At this level, 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 database—collecting, pasting, verifying, and reconciling duplicates across copies from multiple contributors—does not appear on any line of the P&L statement. Yet it is very real, and it directly competes with the time that the same team could devote toanalyzing competitors’ prices and making decisions rather than to repetitive data entry.

53%

Operations professionals encounter a spreadsheet error every week, and 22% encounter one every day; manual data entry is the leading cause (59%), followed by formulas and copy-and-paste operations
(DOSS, survey of 1,003 professionals, May 2026).

These figures do not specifically pertain to price monitoring files; they refer to operational spreadsheets in general. However, a competitive monitoring file specifically combines the top three causes of error: prices entered manually, copied and pasted from multiple sources, by multiple contributors, and under time pressure.

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. 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 apparent 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 non-linearly with volume.

To go beyond market monitoring and also guide pricing decisions, our comparison of retail pricing software presents the solutions available on the market.

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 framework for understanding this 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 approach 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 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 comprehensive method for structuring this seamless scaling process is the subject of another article in this series, which focuses onthe large-scale implementation of competitive intelligence.

At Booper, the structured alternative to Excel: governance, matching, and alerts all in one 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 trajectory—from Excel to rule-based logic, then to hybrid AI, and finally to automation—that Barbotteau Group, a leading retailer in the French Caribbean with over 70 companies and brands, has followed after years of relying on Excel 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 implemented.

So the question to ask isn't "Should we stop using Excel?" but "At what volume does my current system create more risk than it provides visibility?" Find out how Booper structures this entire process—from data collection to governed management—on our price reports & web scraping page.

A Checklist Before Switching from Excel to a Dedicated Tool

Do you track more than 300 to 500 product listings 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 between a competitor's price change and its detection?

Further reading:How much team time is spent on manual preparation?

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 is not straightforward: with 500, References Tracked s across five competitors, applying 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 failing, 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 expanding the number of References Tracked.

Because they are invisible in the file itself: the cell fills in, the number looks normal, but it compares 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 private-label brand confused with the equivalent national brand, or a promotional price recorded 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 error that goes undetected, 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—one that cannot be detected without a dedicated audit—and silently spreads through every pricing decision based on it. This is why it’s important to have an explicit method for ensuring the reliability of 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: when there are more than 300 to 500, References Tracked s spread across more than 2 to 3 competitors, the nature of manual logic changes structurally, regardless of the quality of the team performing it.

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 systems.

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 with the highest margin at stake, 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 justifies 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 product catalog to a new system, following a structured method for monitoring competitor prices—including objectives, scope, frequency, and 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 new entry 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 concrete 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.

These findings do not specifically pertain to price monitoring files, but to operational spreadsheets in general. Recent surveys are clear: according to DOSS (May 2026, 1,003 operations professionals), 53% encounter a spreadsheet error every week, and 22% encounter one every day.

Manual data entry is the leading cause (59%), followed by formulas (46%) and copy-and-paste (45%). A competitive intelligence file combines all three of these risks: it is populated by multiple contributors, draws on multiple sources, and is updated under time pressure.

The same survey raises a red flag: two out of every five incidents are never reported to management, which makes these errors all the more difficult to correct.

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