In telecom, automotive leasing, energy, travel, and insurance, there is no barcode to fall back on. The same is true for a large group of physical products that are technically manufactured items but sold and specified more like custom products: industrial hoses, timber and wood products, cabling, fasteners, and countless other made-to-order or configure-to-order goods where the exact specification, not a brand name or article number such as an EAN, is what actually defines the product.
Two competing propositions can look similar on the surface and still differ in a dozen ways that genuinely change what a customer is paying for. If you've tried to benchmark this kind of market, you already know that "just compare the price" isn't a meaningful instruction until you've answered a much harder question first: compared to what, exactly?
EAN-based matching starts from a shared reference point: an identifier that both sides, at least in principle, agree refers to the same product. Configurable products and services have no such anchor.
A mobile plan, a lease contract, or an energy tariff is really a bundle of independently variable attributes, and two competitors rarely bundle those attributes the same way. One telecom provider's "comparable" plan might include more data but fewer roaming minutes; one leasing company's rate might look cheaper until you account for trim, included services, powertrain, or current mileage on a used-vehicle ("occasion") lease. Matching in these industries isn't about finding the same thing twice. It's about defining, explicitly and in advance, what "comparable" actually means for the specific market you're in, and then applying that definition consistently at scale.
Whether two configurations are comparable depends on which attributes genuinely drive value and price in that category, and these vary enormously across industries. In automotive leasing, that typically includes contract duration, mileage allowance, trim level, transmission, drivetrain, and, for used-vehicle leases specifically, current mileage and build year or month. In telecom, it's data allowance, roaming, combination discounts across mobile and broadband, included TV packages, home internet speed tiers, and contract length. In energy, it might be contract duration, fixed versus variable rate structure, solar systems, and green-energy premiums. None of these are optional details to add later — leaving one out doesn't simplify the comparison, it just makes it wrong in a way that's hard to notice.
Spec-driven physical products follow the same logic, even though they don't look like configurable services at first glance. An industrial hose isn't comparable on price unless pressure rating, inner diameter, length, and material (rubber, PVC, PTFE, stainless steel) all line up, since two hoses that look identical in a product photo can be rated for completely different pressures or media. Timber and wood products work the same way: species, dimensions, grade, moisture content, and treatment (kiln-dried versus air-dried, pressure-treated versus untreated) each change what the product actually is, not just what it costs. Fasteners, cabling, and most other made-to-order goods follow the same rule — the specification sheet is effectively the product's identity, and matching on category name or headline description alone will happily compare a 10-bar hose to a 40-bar one, or construction-grade timber to a decorative grade, if the underlying specs aren't captured and enforced as attributes.
A useful way to structure this is to separate attributes into two categories: attributes that must match for a comparison to be valid at all, and attributes that contribute to a weighted similarity score once the mandatory ones are satisfied. For a lease comparison, transmission type or fuel type might reasonably be a hard filter — comparing an automatic electric vehicle against a manual diesel one isn't a pricing comparison worth making, no matter how close the monthly rate looks — while contract duration or mileage allowance might be scored on a sliding scale, with closer values contributing more to the match confidence than far-apart ones. For industrial hose, pressure rating and material might be the hard filters, since a hose rated for the wrong pressure or the wrong medium is a different product regardless of price, while length and diameter within a reasonable tolerance can be scored on a sliding scale instead. This combination of hard filters plus a weighted score, evaluated against a defined threshold, is the same underlying logic used in attribute-based matching systems across other industries, adapted to whatever the specific category actually cares about.
Configurable industries are especially prone to strategic bundling: a mobile plan folded into a broadband package, maintenance or insurance folded into a lease rate, an add-on marketed as "free" that's really priced in elsewhere. Comparing headline prices without unbundling what's actually included rewards whoever packages things more cleverly, not whoever is genuinely more competitive.
The practical fix is normalizing every proposition to a common basis before comparing it, based on how customers actually use the product: total cost per month for an equivalent bundle of inclusions, price per GB or per minute where relevant, or all-in monthly cost including maintenance and insurance for a lease. This requires deliberately decomposing each competitor's offer into its component parts first, rather than comparing the headline number on the page — which is exactly the number most likely to have been engineered to look attractive in isolation.
In several industries, the same proposition can carry different pricing depending on region, city, or even postcode — particularly in telecom and energy, where local infrastructure, competitive intensity, or regional campaigns can shift pricing without it being obvious from a single reference point. A monitoring setup that checks pricing from one location only risks systematically missing regional promotions or local pricing strategies, understating or overstating the real competitive picture depending on which market happens to be sampled.
Getting this right generally means either monitoring from multiple representative locations or explicitly capturing location as an attribute in the comparison itself, the same way mileage allowance or contract duration would be captured, rather than treating a single snapshot as representative of the whole market.
Everything described so far — the mandatory attributes, the weighting logic, the unbundling rules, the location handling — starts as domain expertise: someone who understands the specific market has to define what actually counts as a fair comparison in it. The harder part is translating that expertise into a scoring model that can be applied consistently and automatically across an entire competitive assortment, rather than redone manually every time someone needs an updated view.
In practice this means encoding the attribute rules, weights, and thresholds explicitly, so that new configurations from either side can be scored the moment they're collected rather than requiring a person to eyeball them. It also means treating that model as something that needs periodic recalibration, not a one-time setup: providers restructure their bundles, introduce new plan tiers, or shift which attributes they compete on, and a scoring model that reflected the market accurately a year ago can quietly stop being accurate as the market around it moves.
Defining the right comparison logic for your own market is something you're genuinely well positioned to do — you understand your customers and your competitors better than any outside party starting from scratch. What's harder to sustain internally is turning that understanding into a rules engine that runs reliably at scale, keeping it current as competitors restructure their offers, and doing this simultaneously across categories that each need their own version of the same logic.
That combination — domain-specific rule definition plus the engineering discipline to keep it running and current — is precisely the gap between "we know how we'd want to compare these" and "we have a benchmark we can actually trust month after month."
At Competify, this is how we approach every non-EAN market we work in: we sit down with the client to define exactly which attributes matter and how they should be weighted for their specific category — whether that's transmission and mileage in automotive leasing, data and minutes in telecom, or pressure rating, diameter, and material for industrial hose, and species, dimensions, and grade for timber — then turn that into a scoring algorithm that runs at scale and gets revisited as the market evolves. If defining that logic once and then keeping it accurate indefinitely is the part you'd rather not own yourself, please plan an introduction meeting.
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