Competitor Prices: Which Products Should You Really Keep an Eye On?
Fabrice Decroo
Consulting Director
August 16, 2026
Trying to monitor 100% of your catalog in the face of competition is not a sign of thoroughness—it’s an admission that you haven’t done the work of setting priorities. A small subset of SKUs—the Key Value Items ( KVIs ) and those with high-margin potential—accounts for the bulk of the stakes, while the rest of the catalog can be monitored less closely.
According to McKinsey, 2.5% of the items in a catalog would be enough to account for nearly one-third of a retailer’s perceived price—quantitative proof that comprehensive coverage is both unattainable and unnecessary.
A category manager who is introduced to their price monitoring system often asks the same question as a first reaction: “How many SKUs are we tracking?” That’s the wrong question. The right question is: “How many SKUs should we be covering?” Trying to monitor everything isn’t a sign of thoroughness—it’s an admission that you haven’t yet done the work of setting priorities. This guide explains why a small subset of the catalog accounts for the bulk of the challenge, how to identify it using a reproducible method, and how to create a monitoring segmentation that fits on a single page.
The classic mistake: trying to keep track of everything
The first reaction of a pricing team that adopts a price-tracking tool is almost always the same: to maximize coverage. Monitor 100% of the product catalog, across as many competitors as possible, at the highest possible frequency. The intention is commendable—to leave nothing out—but it’s based on a false assumption: that all SKUs carry the same weight in the customer’s purchasing decision, and therefore in the retailer’s price image.
This isn't true anywhere. In any retail catalog, the contribution of individual products to the perception of price competitiveness follows a highly uneven distribution, not a flat curve. A handful of products account for the bulk of the effect; the majority of the catalog has very little impact—regardless of the source used to produce this data, whether automated scraping, field surveys, or panels (see “Scraping, Field Surveys, and Panelists: What Are the Differences?”).
According to McKinsey, just a few key items would be enough to account for nearly one-third of a retailer’s price perception —and 10 to 20 percent of the product lineup would account for about 80 percent of that effect (McKinsey & Company, “How retailers can improve price perception—profitably,” 2016).
There are two direct consequences. First, comprehensive coverage is unattainable while maintaining a consistent level of quality: the broader the scope of monitoring, the more burdensome it becomes to maintain product matching, ensure the robustness of bots against anti-bot protections, and perform human verification of discrepancies—to the point where the collected data becomes too voluminous to verify, and thus too unreliable to act upon. Second, it becomes useless beyond a certain threshold: adding references with low impact on perception or margin does not change the final decision, but dilutes the teams’ focus on the discrepancies that truly matter.
So the question isn't "How many competitors and products can I track?" but "Which products, if their prices go out of line, actually affect my bottom line or the price image perceived by the customer?" That is the whole purpose of this guide.
The KVI concept: the handful of products that define the price image
Since the 1990s, the pricing industry has had a specific term for this handful of products: the KVI, short for Key Value Item —literally, “key value item.” A KVI is a product that customers notice, whose price they effortlessly remember, and which they use—consciously or unconsciously—as an indicator to judge whether a store is “expensive” or “a good deal.” In the food retail sector, these are typically products with high purchase frequency and high brand recognition—a carton of milk, a stick of butter, or a can of soda from a leading brand. In other sectors, the equivalent still exists: a loss leader whose price serves as a mental shortcut for judging the rest of the product lineup.
The key point is that what customers remember about a retailer’s prices is not based on its entire catalog, but on this handful of items. In this regard, McKinsey distinguishes between KVCs (key value categories)—categories whose prices are particularly closely scrutinized—and KVIs, which are the most visible individual items within those categories.
According to McKinsey, sales in a given category are typically driven by its Key Value Items—a handful of products, not the entire catalog (McKinsey & Company, 2016).
This focus has direct implications for price monitoring: a system that tracks a KPI and a category benchmark with the same reporting frequency and the same alert threshold wastes its data-collection capacity where the stakes are low and underinvests where they are high. The KPI is not just a marketing concept—it is a principle for allocating monitoring resources.
How to Identify Your Own KPIs: A Four-Criterion Method
KVI is neither a fixed nor a universal category: what serves as a benchmark for a discount retailer does not necessarily serve as a benchmark for a premium specialty retailer, and what matters to a grocery chain does not carry the same weight for a home improvement or appliance retailer. There is therefore no pre-established list of KVI metrics to simply copy and paste—but four interrelated criteria allow them to be identified systematically rather than based on instinct.
Purchase frequency
An item purchased every week leaves a stronger impression in terms of price than one purchased once a year, even if their unit prices are similar. Frequency shapes the memory of the price.
Shelf Visibility
A product that is highlighted—whether at the front of the shelf or as part of a recurring promotion—is more likely to be remembered by the customer. What catches the eye is more likely to be compared.
Online Comparison Rates
The recurring presence of a reference in price comparison sites or product searches indicates active comparison shopping. This is a directly measurable indicator.
Known price elasticity
A benchmark whose past campaigns demonstrate a strong price-volume sensitivity confirms that price does indeed influence purchasing decisions. The proof lies in actual consumer behavior.
None of these four criteria, taken in isolation, is sufficient to identify a KVI. An item with a high purchase frequency but that is rarely compared online (such as a convenience item bought on impulse) is not a KVI. A product that is frequently compared but purchased only once every two years (such as a small appliance) is also not a KVI, just as a recurring purchase is not. It is the combination of all four signals—not just one of them—that must guide the selection, category by category.
In practice, a senior pricing team can build this list by combining data already available internally (purchase frequency and turnover from point-of-sale data, elasticity already measured from past campaigns) with external data that is less frequently used (product presence rate on price comparison sites, search volume for the product name). It is precisely the integration of these two types of data that distinguishes an actual KVI list from a hypothetical one—and that subsequently informs a comprehensive competitor price monitoring strategy, going beyond the mere identification of KVIs.
Beyond the KVI: High-Margin Products
Limiting ourselves to KVI would be a mistake just as serious as monitoring everything. A second group of benchmarks deserves equally serious attention, for a different reason: not because customers compare them, but because an uncontrolled price discrepancy in this area has a direct and significant impact on the retailer’s margin—without this ever being reflected in price perception.
A retailer’s revenue may be driven by its key value categories (KVCs)—but only half of its profit, according to McKinsey. This discrepancy justifies dedicated monitoring of high-margin SKUs, beyond just key performance indicators (KPIs) (McKinsey & Company, 2016).
This figure illustrates a structural imbalance: the product categories that drive the price image are not the ones that drive profitability. A high-margin product with low customer visibility but significant volume (a technical product line, a high-value-added accessory, or a strategically positioned private-label product) may warrant monitoring as closely as a KVI—not to align with the market, but to detect any deviations that could silently erode profits. This is a common blind spot in monitoring systems built solely around the KPI framework: they protect price image but leave margins unprotected.
The pressure on retailer margins observed in recent years in Europe (Bain & Company) makes this trade-off all the more urgent: when margins are structurally tightening, every high-stakes product line with poor profitability oversight weighs proportionally more heavily on the bottom line. Mindlessly aligning prices with those of a competitor for this type of product—without discernment—can actually undermine the overall price image rather than support it—a risk detailed in “How to Monitor Your Competitors Without Damaging Your Price Image.”
What You Can Leave on Low Power Mode—and Why It's Not Laziness
At the other end of the spectrum, a large portion of the catalog can be monitored less frequently—or even excluded from active automated monitoring—without significant risk. These are items with a low purchase frequency, rarely or never compared with competitors, and with low price elasticity: customers buy them regardless of a price difference of a few percent compared to competitors, because other factors (habit, availability, convenience) drive their decision.
According to NielsenIQ, certain products have virtually no price sensitivity in terms of sales performance, and French consumers are among the least price-sensitive in Europe—a solid foundation for maintaining a light monitoring approach to the long tail (NielsenIQ, February 2025).
Deciding to keep this long tail on low-priority monitoring is not a failure: it is a strategic choice regarding the allocation of data collection resources and human attention, just as a company chooses to focus its sales efforts on its priority accounts without abandoning the others. The difference between a mature pricing team and a team that is simply at the mercy of its monitoring system lies precisely here: the former explicitly chooses to ignore part of the catalog, while the latter does so by default, simply because it has never asked the question.
This does not mean that no monitoring is needed in this area: an occasional survey, conducted monthly or quarterly, is sufficient to detect a structural deviation without tying up the data collection capacity reserved for priority segments.
Segmenting the catalog into four levels of monitoring
In practice, these three factors—price image, margin considerations, and low stakes—combine to form a four-tier segmentation model that fits on a single page and serves as a common reference point for both the category management and pricing teams.
| Category | Catalog Section | Reading frequency | Vigilance | Typical Example |
|---|---|---|---|---|
| KVI Showcase | 5 to 15% | Daily+ | Maximum | High-profile loss leaders with a high purchase frequency |
| Strategic Margin | 10 to 20 percent | Daily/Weekly | High | High-value-added accounts with low customer visibility |
| Standard volume | 30 to 40 percent | Weekly | Moderate | Regular rotation, with no significant differences |
| Long tail | Rest of the catalog | Monthly+ | Lightweight | Niche market, occasional purchases, low price elasticity |
To put it this way: this segmentation is by no means arbitrary—each level corresponds to a distinct reason for monitoring (or not monitoring). The “showcase” KVI is monitored for price image; the “strategic margin” is monitored for profit; the “standard volume” is monitored to prevent silent deviations on high-traffic SKUs; the long tail is monitored at a minimum to ensure nothing exceptional is overlooked. Once this framework is established, it becomes the direct input for configuring the data collection system—frequency, alert thresholds, level of human verification—rather than a uniform setting blindly applied across the entire catalog.
Reviewing your KVI list: a periodic exercise, not a one-time task
A KVI is not a permanent characteristic of a product. It evolves with changes in consumer behavior, the entry of new competitors into the market, shifts in purchasing habits (the rise of curbside pickup, a shift toward private-label brands), or simply the product’s own life cycle. A product that was a KVI three years ago may have lost that status without anyone in the organization noticing—because the initial list was never revised, but merely copied from one season to the next.
This is one of the most common methodological criticisms leveled at market monitoring systems that are otherwise technically sound: the initial segmentation was accurate, the survey frequency was well calibrated, and the product matching was reliable—but the reference list has not been reviewed since its creation. A best practice is to review the list of KPIs and the associated segmentation at least once a year, and whenever there is a significant market signal (such as the entry of a new, price-aggressive competitor or a change in purchasing behavior detected in internal data).
At Booper — Prioritization as a parameter of the system, not as a fixed setting
The GENIUS Price module allows you to configure business rules and filters by segment—KVI, strategic margin, standard volume, long tail—rather than applying a uniform policy across the entire catalog. Upstream, GENIUS Link ensures the reliability of the product matching required to trust these segments, and GENIUS Monitoring adapts the alert frequency to each level of vigilance. It is this prioritization approach that now enables Coopérative U to manage several million prices per year across more than 1,700 stores, continuously balancing price competitiveness, margin protection, and pricing tier management—a balance that only makes sense if the product catalog is segmented upfront.
It is this discipline of prioritization—which is continuously assessed and revised over time rather than set in stone when the system is first implemented—that distinguishes a proactively managed price monitoring system from a reactive one. Learn how Booper structures this approach on our price tracking & web scraping page.
A Checklist Before Finalizing Your Price Watchlist
- Did you identify your KPIs based on metrics (purchase frequency, visibility, online comparison, price elasticity) or on intuition?
- Are your high-margin accounts monitored separately from your KPIs?
- Is your long tail intentionally put on standby, or has it simply been forgotten?
- Does your data collection frequency vary by segment, or is it consistent across the entire catalog?
- Do you review your KVI list at least once a year, or do you just copy it over from one season to the next?
Frequently Asked Questions
Answers to the most frequently asked questions about selecting products to monitor in light of the competition.
A KVI (Key Value Item) is a product that customers notice, whose price they effortlessly remember, and that they use as a mental benchmark to judge whether a store is expensive or competitively priced—typically a product with a high purchase frequency and high brand recognition, such as a carton of milk or a block of butter in a supermarket.
What makes this concept work is its disproportion: according to McKinsey, 2.5% of SKUs would be enough to account for nearly one-third of a retailer’s perceived price, and 10 to 20% of the catalog would account for about 80% of this effect. In this regard, McKinsey distinguishes between KVCs (key value categories)—categories that are closely scrutinized—and KVIs, which are the most visible individual SKUs within those categories and the ones that should be monitored as a priority to gauge a retailer’s price image.
For a pricing team, this completely changes the nature of market monitoring: a system that tracks a key performance indicator (KPI) and a category benchmark with the same frequency wastes its data-collection capacity where the stakes are low and underinvests where it really matters.
No. The article makes it clear: comprehensive coverage is both unattainable at a consistent level of quality—the broader the scope, the more burdensome it becomes to maintain product matching and perform human verification—and unnecessary beyond a certain threshold, since the majority of SKUs have only a marginal effect on price perception or margin.
The right question isn’t “how many SKUs can I track?” but “which SKUs, if their prices go out of control, will actually affect my bottom line or the price perception of the customer?” It is this logic that structures the four-level segmentation presented in the article: showcase KPIs, strategic margin, standard volume, and long tail—each with its own monitoring frequency, an essential approach for scaling up competitive intelligence on a large scale.
At the other end of the spectrum, a large portion of the product lineup can be placed on low-priority hold without significant risk: according to NielsenIQ, approximately 30% of SKUs are said to have virtually no price sensitivity in terms of sales performance—a solid basis for making this choice rather than accepting it by default.
By cross-referencing four indicators—none of which is sufficient on its own, according to the article—the purchase frequency (a product purchased weekly carries more weight in price memory), shelf visibility (products that are visible are compared more often), the online comparison rate (recurring presence on price comparison sites), andthe known price elasticity measured based on past campaigns.
It is the combination of all four criteria that identifies a KVI, not just one of them: a product with a high purchase frequency but that is rarely compared online—such as a convenience item purchased on impulse—is not a KVI; a product that is frequently compared but purchased only once every two years is not a KVI either, just as a recurring purchase is not.
In practice, the article recommends combining data already available internally (purchase frequency, turnover, price elasticity calculated from the data) with external data that is less frequently used (presence rate on price comparison sites, search volume for the product name)—it is this integration that distinguishes a list of KPIs built from actual data from a hypothetical list.
A KVI is monitored because customers actively compare its price and draw conclusions about the image of the entire retail chain. A high-margin item is monitored for an entirely different reason: an uncontrolled price deviation for that item directly negatively impacts financial results, even if customers never compare its price across different retail chains.
The article illustrates this imbalance with a telling figure from McKinsey: key value categories (KVCs) may account for 80% of a retailer’s revenue but only half of its profit —the categories that drive the price image are not the ones that drive profitability. A high-margin product with low customer visibility, a technical department, or a strategically positioned private label may therefore warrant monitoring as closely as a KVI, but from a results-oriented perspective—not based on perception—and should be tracked alongside the retailer’s essential pricing KPIs.
This is a common blind spot in monitoring systems built solely around KVI logic: they protect the price image, but leave the margin unprotected against a silent decline.
At least once a year, and whenever there is a significant market signal—such as the arrival of a price-aggressive competitor, a measurable change in purchasing behavior (such as the rise in curbside pickup or a shift toward private-label brands), or a change in a product’s life cycle—signals that only up-to-date competitive data can detect in a timely manner.
The article highlights this as one of the most common methodological criticisms leveled at otherwise well-designed studies: the initial segmentation was accurate, and the survey frequency was well calibrated, but the reference list has never been updated since its creation—it has simply been copied from one season to the next.
A KVI is not a permanent characteristic of a product: a product that was a KVI three years ago may have lost that status without anyone in the organization noticing. Reviewing the list regularly is what distinguishes a proactive pricing monitoring program from a reactive one.
No. A product’s KVI status depends directly on the retailer’s positioning, its business category, and its local market—a best-seller at a discount retailer might be a KVI, while it could be a secondary product at a premium retailer in the same sector, precisely because the two retailers are not evaluated by their respective customers based on the same criteria.
The article is clear on this point: there is no universal list of KPIs that can be copied and pasted from one retailer to another. What serves as a benchmark for a food retailer does not carry the same weight for a home improvement or appliance retailer, and each retailer must build its own list based on its own data regarding purchase frequency, visibility, and price elasticity, consistent with how it has structured its pricing organization.
At Booper, this prioritization strategy is put into practice through GENIUS Price, which sets rules by segment rather than applying a one-size-fits-all policy—it is this approach that now enables Coopérative U to manage several million prices per year across more than 1,700 stores, continuously balancing price competitiveness with margin protection.
Also in this series
- Web scraping, field audits, consumer panels: what are the differences?
- How to build a competitor price monitoring strategy
- How to Monitor Your Competitors Without Damaging Your Price Image
Sources: McKinsey & Company, “How Retailers Can Improve Price Perception—Profitably,” November 8, 2016 · NielsenIQ, “NielsenIQ Unveils the 2024 Economic Review and 2025 Outlook for Retail and Consumer Goods,” February 11, 2025 · Bain & Company, “Beyond the Tail: How a Strategic Approach to Simplification Fuels Growth.”
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.