X-Ray of Liège E-Commerce 2026

Two hosting philosophies, not two quality tiers

The two leading platforms, WooCommerce (43% of the base, 251 sites) and PrestaShop (34%, 198 sites), are open-source solutions that the retailer hosts and maintains itself: together, 78% of the shops. Shopify and Wix sit on the other side, hosted services (SaaS) where the vendor takes on the infrastructure and the updates, in exchange for less control but less maintenance. Combined, they account for just 10% of the base here.

This autonomy handicaps nothing on the substance: on the angles that matter for acquisition, WooCommerce and PrestaShop handle natively, or through mature extensions, schema.org product markup (`Product`, `Offer`, `BreadcrumbList`), Google Merchant Center feeds and hreflang tags, just as a Shopify does. The gap, when it exists, lies in the hosting and the configuration, not in the engine.

PrestaShop's dominance is no local quirk: a French-born solution served by the regional agency network, it is the standard of the French-speaking e-commerce market. The contrast with Shopify's global weight betrays this French-speaking anchoring, not a Liège lag.

What the platform really decides: who carries the debt

The dominant CMS decides who carries the maintenance and optimization debt, for the next three to five years. The day you have to patch a flaw, migrate a major version or rebuild a payment funnel, the question of who holds the wheel becomes concrete.

When the operator is an SME with no dedicated technical team and no clear maintenance contract, this debt builds up silently: unpatched versions, abandoned extensions, performance that degrades. That is the real subject, not the name of the CMS, and it is the configuration the Liège retail base makes most likely.

Speed, the only criterion where the CMS weighs measurably

On raw performance, the gap is clear on desktop and goes in only one direction, hosted solutions ahead:

PrestaShop therefore brings up the rear of the panel's major CMSes on desktop, and Odoo at 53 on mobile. But the mobile order plays out within one to three points on small per-platform samples and flips from one measurement campaign to the next: presenting mobile as a verdict would amount to over-reading statistical noise. The hierarchy can only be stated honestly on desktop.

The desktop gap comes down to infrastructure, which SaaS optimizes by default. Self-hosting demands serious hosting (Redis or Varnish, OPcache), a configured cache and a connected CDN, three building blocks that many Liège retailers never switch on, for lack of an in-house technical profile or a provider. Properly served, a PrestaShop or WooCommerce closes most of it: what slows things down is the install, not the tool.

The platform name gives a tendency, it does not fix the result.

This speed weighs on two fronts: Google makes it a ranking signal via Core Web Vitals (LCP, INP, CLS), and every hundred milliseconds of latency erodes the conversion rate, paid as well as organic.

The hidden cost, quantified, and its flip side

The real cost of well-kept self-hosting, which the study does not measure, works out as an order of magnitude: performant hosting, dev time, modules on one-shot licenses of 50 to 100 euros apiece, so quickly 2 or 3 k euros at install. On the other side, a Shopify Basic starts around 32 euros a month, but the apps you actually need often add 50 to 150 euros monthly, plus transaction fees outside Shop Payments.

The flip side: open-source keeps a real structural advantage.

• Data ownership, no vendor lock-in.
• No Shopify Plus bill at roughly 2000 euros a month once at scale.

The real business divide is not played out on two Lighthouse points, but on the checkout funnel. Shopify ships a native Checkout (Shop Pay, one-click payment) maintained by the vendor, whereas WooCommerce and PrestaShop leave a hand-built funnel, weakened after every update. That is where conversion is won or lost. The detail reads in the checkout funnel layer.

The consequence is direct: the question is not "did I choose the right platform?", but "am I exploiting the speed and the funnel it lets me reach?". The most profitable room almost always sits in optimizing what already exists (hosting, theme, images, third-party scripts), not in a migration. The scores rely on Google's PageSpeed API (opens in a new window); our reading of the technical work is detailed on the expertises side.

Your pages drag exactly where it matters, on mobile, and every marketing tool you add deepens the hole. Slowness is paid for twice, in search rankings and in sales that never happen. The detail that follows is for your provider.

The mobile divide

The median mobile score sits at 60 out of 100, against 83 on desktop. Twenty-three points of gap, right where most retail visits happen. That 60 is a lab Lighthouse score, a simulation run on a deliberately throttled network and processor to reproduce a mid-range mobile, which makes it comparable from one site to another. Not to be confused with field Core Web Vitals, collected from real users via the CrUX base, which here covers only 186 sites, those receiving enough traffic to feed a reliable measurement. One nuance matters for search rankings: the lab Lighthouse score diagnoses, but does not weigh into Google ranking. Only CrUX field data feeds the Core Web Vitals used as an SEO signal.

The mobile distribution leans low. Just 3.7% of e-shops are in the green, against 32.9% on desktop, and 23.1% squarely in the red, that is 132 sites under 50. The base optimized for the big screen and let the small one slip.

LCP, the time it takes the largest visible element (often the product photo) to display, exceeds 4 seconds on 64% of sites, when Google recommends staying under 2.5 seconds, a threshold met by only 21% of the base. Visual stability, on the other hand, is healthy: CLS, which measures layout shifts during loading (a button that moves just as you tap it), stays good on 81% of sites. The design holds up, it is the technical weight that drags things down, exactly where a layout redesign would cost far more.

The more you equip, the more you slow down

Rank e-shops by the number of third-party tools detected (analytics tags, advertising pixels, chat widgets, consent banners, A/B testing scripts, remote fonts). The trend contradicts the intuition that "better equipped" rhymes with "better built": the ones most loaded with scripts are among the slowest. The mobile median declines monotonically, with no reversal from one tier to the next, from 63 for sites with no tools at all to 48 for a full stack. Fifteen points. The slope also holds on desktop (from 87 to 70), at constant platform and across all districts, from Liège to Waremme.

Whatever angle you slice the sample from, the direction never reverses, and that is what distinguishes a signal from a sampling artifact.

Each script adds, in varying proportions:

• network: a DNS request, a TLS handshake and a round trip to a domain that is not yours, outside your cache and your CDN. Multiplied by twenty third-party origins, the connection cost alone weighs down the start.
• a blocked main thread: most of these scripts run in JavaScript on the main thread, the one that has to paint the page and respond to clicks. It is the classic profile of a Total Blocking Time that swells as the tags pile up. Lab-measured, it foreshadows the INP that real users will experience, the interactivity signal Google now uses in the Core Web Vitals.
• the unpredictable: a third party, by definition, is beyond your control. Its latency, its outage of the day become yours.

Correlation, not causation

The direction of cause and effect calls for caution. Claiming "third-party scripts destroy performance, plain and simple" would be dishonest: the most loaded tier rests on n=8 sites only, too thin a count to make it a law. The correlation can run both ways: heavy scripts slow the page, but it is also the most commercially mature sites, therefore the most instrumented, that pile on the most tools without having invested in parallel in performance discipline. Tooling here is a marker as much as a cause.

One last safeguard. The study measured no conversion, no cart, no revenue. When you read that "an extra second of loading erodes the conversion rate," that is an external benchmark (the Google and Deloitte orders of magnitude on mobile retail), not a result from this Liège corpus: an observed slowness connected to a risk documented elsewhere, to be verified site by site.

The real fix: the right place, not fewer tools

The right reflex, then, is not to remove the tools, but to load them cleanly (see tracking). The tracking-versus-performance dilemma is largely solved in 2026 by server-side tagging. With an sGTM, a tag server that routes the measurement through your own server rather than through the browser (via Stape or Addingwell), you pull GA4, the Meta Pixel and Google Ads out of the visitor's browser: the client gets lighter, first-party cookies gain lifespan. Deferred loading of non-critical scripts and Consent Mode v2 remain, which conditions firing on consent instead of sending everything off at the first byte.

The order of magnitude is documented in an external benchmark, not measured here: taking a mobile LCP from 4 seconds to 2.5 seconds translates, according to Google benchmarks, into a double-digit conversion gain. The three goals, reliable measurement, fast page and compliance, then stop pulling against each other.

The measurements rely on the Google PageSpeed API (opens in a new window), which exposes in a single call both the lab side (Lighthouse) and the field side (CrUX). Our reading of the technical work is detailed on the expertise side.

A flattering score by absence of subject

Measured on the homepage, consent compliance splits into four grades. 41% of sites earn a grade A (235 sites), while 44% fall into the weak zone (grade C or D). Of those 235 grade A scores, 215 show no banner because they load no tracker to consent to: a good grade by absence of subject, not by control. Only twenty sites truly earn their A on active trackers.

The risk reads in the crossover between active trackers and the absence of a consent mechanism, not in the displayed grade. The point of exposure is the page that loads its trackers (Google Analytics, Meta Pixel, third-party tools) before any choice by the user, and without a CMP to capture it. That's the profile to isolate before talking about bringing anything into compliance.

Consent isn't fixed inside the CMS

Consent is not a WooCommerce or PrestaShop feature. It's a CMP (consent management platform, such as Cookiebot, Axeptio, Complianz), wired into the tag manager (GTM) and paired with Google's Consent Mode v2. You add it whatever the platform, and the real work happens in the wiring between the CMP, the tag manager and each tracker, not in dropping the widget.

Installing a banner via a CMS module or an all-in-one plugin handles the display, not the compliance. A banner that drops trackers before the click, or that doesn't make refusal as easy as acceptance, stays non-compliant with the GDPR and the ePrivacy directive. A CMP only becomes an asset if it genuinely blocks tags from firing until consent is given: layered over a GTM that fires unconditionally, the tags fire anyway. The proof is in the sample: 97 sites equipped with a CMP stay at grade C or D, for lack of real blocking before choice.

Dropping a OneTrust, Axeptio or Cookiebot banner isn't enough: the tool is only worth the wiring behind it, honest categorization of trackers, effective blocking before consent, clean transmission of the signal to the tags. Compliance is verified in the network, request by request, never by the presence of a vendor logo in the footer. The legal framework is detailed by the CNIL (opens in a new window).

Consent Mode v2: only advanced mode unlocks modeling

Google's Consent Mode v2 comes in two forms. In basic mode, Google tags stay blocked as long as refusal holds: no signal goes out, refused conversions are lost. In advanced mode, tags load from the start but stay throttled without consent, and send anonymous pings (`consent denied`) that let Google model the missing conversions.

This modeling, reserved for advanced mode, is subject to volume thresholds (traffic and conversions per country) below which Google reconstructs nothing, often out of reach for a small Liège e-commerce account. Across the sample, only 35 sites run a Consent Mode v2 in advanced mode: the only ones technically equipped to recover part of the conversions lost to refusal.

The figure of 83% of sites without Consent Mode calls for an honest reading. It includes 221 sites with no Google tag to steer (no Google Ads, no GA4 connected), for which the absence of Consent Mode is moot. Conversely, a site spending on Ads without a consent framework burns budget continuously.

Beyond Google: Meta and the server layer

Consent Mode v2 only steers Google tags. The Meta Pixel, present on 101 sites, keeps its own blind spot. Without a consent signal passed along and without a Conversions API (CAPI) server-side with an `event_id` to deduplicate, the match quality of events drops (EMQ, the score that measures how well Meta recognizes the user behind a conversion), and Meta doesn't model conversions lost to refusal either. Social budget then optimizes on holed data, like Google budget without Consent Mode.

The CMP plus client-side Consent Mode stays the legal minimum: it signals the choice to the tags without deciding what goes out. The mature answer is server-side tagging: a consent-aware sGTM only forwards the event to Google or Meta if consent allows, and the CAPI sent in parallel is deduplicated by `event_id` so the same conversion isn't counted twice. With 83% of sites without Consent Mode, the step is steep, but it's the server that holds the data at scale. A single tag flow decides both compliance and conversion reliability.

Half of Liège's stores have no idea where their sales come from, and some of those running ads pay against false numbers. Measuring accurately costs little and changes every decision that follows.

Google Analytics 4, the reference audience-measurement tool, shows up on only 293 sites, or 50.6% of the base. Nearly one retailer in two therefore ignores on which pages its visitors drop off and at which step they abandon their cart. At the other end of the spectrum, 196 e-shops (33.9%) carry no measurement tool at all: no analytics, no advertising pixel, no conversion tag. They run on guesswork, never knowing which channel produces their revenue.

Alongside, 15.2% already place a Google Ads tag to steer their spend on real acquisition cost. But placing a tag guarantees nothing: loaded without conversion wired, it counts visits but stays blind to the transaction, hence useless for steering a media budget.

Instrumentation that is rare and rickety

The most frequent marker remains the Meta Pixel, placed in pure client-side, with no server declaration to match. Yet a pixel alone, in 2026, is no longer a reliable measurement: between Apple's tracking prevention (ITP, which caps at seven days the lifespan of first-party cookies set client-side in JavaScript, while those set server-side escape it), ad blockers and the tightening of iOS, it lets slip, depending on the share of Safari/iOS traffic and of blockers, 20 to 40% of events before they even reach the ad network's servers, or one to two purchases in five.

The issue is not the load order, since the pixel often fires before the analytics; what is missing is the Conversions API (CAPI) to mirror collection server-side, and above all an `event_id` shared between browser and server to deduplicate. The CAPI, out of reach of the blocker and of ITP, catches what the browser loses. Without this pair, you choose between two evils: under-counting (pixel alone) or double-counting (both without deduplication).

The hierarchy of the stack speaks louder than any figure on its own

• Universal Analytics still present: 101 sites (17.4%) load this version, shut down by Google in July 2023 (opens in a new window). It has collected nothing for nearly three years, but no one has removed it, because no one looks at the data: tracking installed once and never audited. On these sites, the GA4 supposed to have taken over is almost never checked either.
• The tag manager in the minority: 121 sites (20.9%), two and a half times rarer than the analytics it is meant to orchestrate. Cross-referencing them with the GA4-equipped sites, we estimate, as a common-sense approximation, that roughly four GA4 installs in ten lean on this layer; the other six hard-code their tags into the theme. Without a tag manager, every fix goes back through a developer, whereas a container handles events, triggers and consent without touching the code: the layer that makes tracking maintainable, missing on eight sites in ten.
• Paid acquisition in the minority: Meta Pixel on 101 sites, Google Ads tag on 88 (15.2%). The same total of 101 for these two distinct tools is a coincidence, not a double count. That the advertising pixel is on a par with a dead Universal Analytics shows that the tag is sometimes installed before there is even a measurement foundation that holds. The brand pays for it, but without CAPI or `event_id`, it already sees only part of what it buys.

What emerges from the whole is an absence of architecture, not of tools: the gap comes down to method more than to technology.

GA4 present does not mean GA4 that measures

A loaded tag does not make a measurement plan: you still need to track business events (`purchase`, `add_to_cart`, `begin_checkout`), feed cart and revenue through enhanced e-commerce and filter internal traffic. The 293 GA4 in the base are overwhelmingly default GA4, which counts page views without ever reading the funnel. The real work goes beyond a simple « put GA4 in ».

The cost is not only down to the ghost GA4. The 88 sites equipped with Google Ads and the 101 carrying a Meta Pixel optimise their media budget on false numbers: lacking Consent Mode v2, the conversions lost when cookies are refused are no longer modelled; without Enhanced Conversions on the Google side or a CAPI paired with an `event_id` on the Meta side, the ROAS displayed understates real performance and degrades. To this is added a performance cost (see performance): the median mobile speed slides from 63 with no tool at all to 48 with a full stack of four tags. The correlation is strong, probably directional rather than strictly causal (the high tiers have small numbers, 8 sites with 4 tools), but the direction stays clear: you degrade the speed that conditions conversion while harvesting fragile data.

The 2026 answer, sequenced

Server-side tracking answers both ills in a single move.

It is the structural answer, to put in place after the foundations, never as a starting point: attempted too early on a rickety base, it adds complexity without fixing anything. The sequence reads as effort against impact.

• Remove the 101 dead Universal Analytics: five minutes per site, immediate weight savings, zero risk.
• GTM plus Consent Mode v2 on the 88 Google Ads and 101 Meta Pixel: this is where the leaky data costs cash. The step that secures compliance before investing more.
• A clean GA4 backed by a measurement plan: measure what you decide on, not everything.
• Server-side: for the volumes that justify it, last.

Our reading of this work is detailed on the expertises side.

The reading scale shifts here

We leave the 579 e-shops measured one by one and move down into the real buying journey, payment and delivery, read page by page on a sub-sample of 59 stores crawled in depth, the ones whose payment and delivery pages were actually readable. What follows therefore describes a frequency of presence, a ranking, a direction, not market shares: a method that ranks first was simply detected across more stores, with no way of knowing the weight it carries across the base. We read the stores' technical shop window, their till stays shut, and that is exactly the reading a conversion audit lays down before touching anything else.

This shift of scale comes with a trade-off to own plainly: what we gain in granularity we lose in representativeness. The rarer a method, the more its rank depends on the draw. The solid signals are the broad contrasts; the tight calls, we set aside. Three reading reflexes for what follows: a frequently detected method reflects a retailer's intent to offer, never proof of use; an absence weighs as heavily as a presence, most often a deliberate local choice rather than a technical oversight; the coherence of a stack matters as much as any single method taken in isolation, since a broad panel behind a single aggregator does not tell the same story as a panel cobbled together brick by brick.

A very local portrait, from payment to delivery

At the till, the landscape leaves no ambiguity. Bancontact and bank transfer dominate, two methods anchored deep in Belgian habits, followed by invoice payment, PayPal, then the aggregator Mollie. The signature of a business that serves a local customer base first, and that takes payment the way it always has.

This leading duo says a lot about the buyer being targeted. Bank transfer means opening your banking app, copying over an amount and a reference, then waiting for the funds to land before the parcel ships: a journey that assumes trust already in place, not an impulse buy. Bancontact is the quintessential Belgian domestic reflex, smooth for anyone who holds an account in the country, but off the table the moment you cross the border. These two methods quietly filter for the local buyer without slamming the door on anyone: they outline a commercial perimeter that is often chosen. Invoice payment confirms the reading: paying after receipt assumes a level of trust that few pure players extend to an anonymous first-time buyer.

Against this picture, one misreading to dismiss right away. Mollie leading the field betrays neither immaturity nor local retreat: it is the de facto standard of Benelux e-commerce, the aggregator stores plug in to offer Bancontact, cards, wallets and local methods behind a single integration. PayPal, alongside it, adds international reassurance and card payment without having to enter your details on the merchant's site.

On the delivery side, the same local tone, the same caution. Shipping handled directly by the retailer comes ahead of Bpost, ahead of in-store pickup, with the DPD and Mondial Relay pickup-point networks bringing up the rear. The study crawls delivery-option pages, without ever touching volumes or shipping history: it tells what stores offer, not how much they ship. To draw a scalability diagnosis from it would be an excessive inference.

The cheapest brake to release, and the real decider

The payment method goes beyond a technical step at the bottom of the funnel: it is a filter. Two families of stores stand apart on friction. On one side, those plugged into an aggregator like Mollie or Stripe: the buyer lands on a standardised line-up, Bancontact, card, sometimes PayPal or instant transfer, and pays in a few seconds within a journey already lived a hundred times elsewhere. Near-zero cognitive cost. On the other, those accepting only the classic bank transfer or Bancontact alone, with no aggregator behind: a quiet filter cut out for a buyer already in confidence, most often local and loyal to the brand.

This filter costs little to whoever targets only their regulars, and dearly to whoever would like to broaden out. For a lukewarm visitor, caught via a search or an ad, still with no tie to the brand, the absence of an immediate payment adds a step right when intent is at its most fragile. Without measuring sessions, we cannot claim they close the tab, but it is the most likely scenario. Still, of all the brakes observed, it is the cheapest to release: plugging in an aggregator touches neither the catalogue, nor the positioning, nor the logistics, and bank transfer stays an option for the regulars who prefer it.

Still, payment is only one step, and it is the whole funnel that decides the sale. Four points weigh as much as the payment method. Forcing account creation instead of a guest checkout pushes back the rushed buyer who just wanted to pay once. Every superfluous field, against a checkout condensed into a single page, offers one more chance to close the tab. Shipping costs dropped at the final screen are the most documented cause of cart abandonment, around 48% according to the Baymard benchmark, far more brutal than the absence of delivery info. Bancontact on mobile, finally, where more than 60% of traffic flows, opens a redirect to the app or a QR code: real friction the moment the integration is botched.

Where the store delivers, and for how much

• Nearly 60% of the 537 commerce profiles, almost three stores in five, state nowhere where or how the store delivers: no covered zone, no lead time, no price grid before adding to cart.
• When the info exists, it most often limits itself to Belgium, sometimes extended to neighbouring countries, rarely formalised into a standalone Delivery page that would carry weight.
• The visitor left in the dark leaves rather than writes in: a cart that never started, a sale that never fires. The study crawls pages, not sessions: this upstream abandonment remains an inference, but it has the signature of silent forfeit, invisible in the statistics since nothing happened there that analytics could count.

Three clear lines on the delivery page demand neither a rebuild nor a budget: the kind of job an expertises team settles in a few hours.

In CRO, everything the buyer has to guess, they count as a risk.

A stated lead time, a named carrier, a free-shipping threshold on display, a readable returns policy: the page stops asking a question, it answers. This signal ties back to the click-and-mortar finding, where two thirds of e-shops have a store behind them: part of the traffic probably comes from people ready to collect in store, provided the page says so.

Three sales models coexist, to consumers, between professionals, or both, and none has pulled ahead.

Across the 522 e-shops segmented by their customer model (a subset of the 537 profiles in the commerce layer, 15 profiles too ambiguous set aside for lack of clear signals, and a denominator still distinct from the study's global 579), three families coexist: 286 sell to consumers (B2C, 55%), 126 between professionals (B2B, 24%) and 110 mix both audiences (hybrid, 21%). None of the three models is marginal, each weighs at least a fifth of the panel: no way to tell the story of Liège e-commerce as a purely consumer affair.

Nobody is mature, and that is the lesson

The three models share the same local fabric and equipment of the same order. Median maturity index of 1.08 in B2C, 0.97 in B2B, 0.93 in hybrid: a single tool detected per segment. The gap between the best and the worst rated caps at 0.15 point, to read in relative terms, not as a hierarchy. Performance and compliance stay indifferent to the model too: median mobile perf of 60 to 61 everywhere, weak GDPR grades between 39% and 44%. The model changes neither the site's speed nor its compliance, which points to a common cause upstream, on the tooling and stack-choice side.

The best students tick two or three technical boxes, rarely more; the mass ticks zero or one. No leading pack, an entire fabric starting from the same place. Bad news for anyone looking for a local benchmark to imitate, excellent news for anyone wanting to get ahead.

The ground is flat, and the first real measurement infrastructure laid on this market will have no one to overtake, just a standard to set.

Three reading caveats

The index aggregates four unequal signals: analytics, advertising pixels, marketing tags and consent banner. The banner, most often placed by default by the CMS or the theme, inflates the score while saying nothing about real measurement: when a profile's median comes out at a single tool, that point is frequently automatic consent, not deliberate tracking. A ceiling of presence, never a floor of use; measuring and activating the data (GA4 configured, a pixel reporting conversions) is in practice rarer still.

Second reservation, the bridge between the profiles layer and the commerce layer. The 18% of sites exposing in-store pickup (n=522) and the 66% of establishments holding a physical store (n=537, commerce layer) do not rest on the same subset: the 18% includes B2C pure players devoid, by construction, of any pickup point. Figuring the "gap" on that scope would overstate the lag; the real dormant reserve sits in the intersection of store + site, not in the raw subtraction. The trend holds: a substantial share of retailers with a high-street presence does not expose pickup on their site.

Third reservation, the status of the signals. Everything the index captures is a detected presence, not a proven adoption nor a generated revenue. The "crumb of revenue" formula is an in-house thesis, proposed without being measured: we observe that a tag is loaded, never whether it feeds a decision nor the revenue it serves. To hold as a working hypothesis, not as an established result.

Choosing your audience is not enough, you have to choose your channel

Segmenting B2C, B2B or hybrid only has operational value once translated into an acquisition channel, otherwise segmentation stays a diagnosis with no grip on action.

• Local B2C: non-brand Search on purchase intent, Meta for latent demand, web-to-store to bring people back in store, consistent with the two thirds of physical stores.
• B2B: tight intent-driven Search, LinkedIn to target the job function, lead generation rather than a cart. Pickup stays an outlier between professionals (only 2%); the return comes from leads and quotes: quote form, lead tracking, scoring, integration with the CRM and invoicing.
• Hybrid: at 21% of the base, the trap is serving both audiences the same way. The consumer wants a simple cart, a displayed price, a payment in three clicks; the professional expects a quote on account, negotiated terms, deferred invoicing. A generic site calibrated for neither imposes a permanent splits stance: every trade-off (a button's wording, the order of the blocks, the placement of the price) assumes a clear-cut target to make sense.

The strategic framing goes down to the choice of channel: that is the work our expertises steer.

Where to start, segment by segment

You attack where the effort-to-impact ratio is highest.

• B2C (286 sites, 55%): the volume quick win. In-store pickup is exposed by only 18% of them while the majority has a high-street presence. Plugging in Click & Collect connects two assets already paid for, with no rebuild and no media budget.
• Hybrid (110 sites, 21%): a heavy strategic project, a repositioning ahead of any technical optimisation.
• B2B (126 sites, a quarter of the base): the most profitable job stays lead and quote generation.

Clarifying the target before optimising execution reads on the expertise side.

The physical anchoring of Liège e-commerce

Across the 537 complete profiles analyzed, 354 stores, or 66%, lean first on a very real point of sale. These are click-and-mortar businesses: the click of the web grafted onto the brick of the high-street store.

If two thirds of the base are first stores, the gaps measured elsewhere, mobile slowness, compliance endured, the absence of measurement, reread as traditional retail at the start of its digitalization, not as negligence. What's mostly missing is time: running a store and a website at once, often alone, doesn't leave the room a pure player has.

Click & Collect, the asset asleep in the back room

That same physical root points to the most telling missing piece, Click & Collect, the in-store pickup of an order placed online. The most natural bridge between the store and the site, the one that demands the least reinvention since it builds on a store already there, equips only 13% of Liège e-shops, all profiles combined (18% for B2C alone, detailed below). A retailer who already runs a counter, stock and opening hours owns the essentials: what they mostly lack is the online reservation and the promise kept at pickup.

The contrast with HTTPS, the basic encryption of a connection, near-universal at 577 sites out of 579, says a lot. When the foundation activates by default, asking nothing of the retailer, the base follows en masse; when you have to decide and wire it yourself, like Click & Collect, it drops off.

Click & Collect, by contrast, stays a project to carry out, whereas HTTPS never asked for anything.

The realistic potential doesn't climb to 100% for all that, since a poorly run Click & Collect, stock out of sync, fuzzy availability times, disappoints more than it serves. The gap therefore doesn't read as a mechanical jump from 13% to a theoretical ceiling, but as a margin open to brands that have a point of sale, reliable stock and the rigor to keep the promise.

A reading by segment, with clean boundaries

Crossed by customer typology, the contrast holds. Of the 537 complete profiles, 522 split into three models, the remaining 15 escaping the breakdown for lack of a usable signal, which we flag rather than smoothing over.

• B2C: Click & Collect at 18% (286 players). The most natural ground for quick pickup: a local B2C basket bears shipping fees and delays poorly, which in-store pickup wipes out.
• Hybrid: Click & Collect at 14% (110 players). Torn between two audiences with opposite expectations: the consumer wants to pick up fast, the professional wants an invoiced, tracked delivery.
• B2B: Click & Collect at 2% (126 players). The invoice and the delivery remain the norm. But for a local B2B reseller, pickup answers a real need, the spare part a tradesperson comes to collect urgently rather than waiting for a delivery round. Where that use exists, the low rate owes less to sector fate than to a missed opportunity.

A hypothesis to measure, a link upstream

A methodological caution remains. That Click & Collect is the most natural bridge for a physically rooted base is solid; that its installation mechanically generates growth remains a hypothesis. The study observes equipment rates and typologies, not a single euro of store-by-store revenue. The link between the option and the revenue is plausible and documented elsewhere in retail, but it's verified case by case.

A second safeguard is in order. Installing a Click & Collect without measuring the store visits it generates shifts the problem: a customer who spots a product online then buys it on the shelf stays a black hole. That points straight back to the measurement layer: one e-shop in two is already flying blind, with no GA4. Without reconciliation from online to offline, the impact of pickup stays invisible, and therefore can't be arbitrated.

Pickup, though, only plays at the bottom of the funnel. The most profitable work often happens earlier, in local discovery: Google Business Profile listing, local SEO, store locator, store pages that rank on geo-targeted queries. Reducing web-to-store to Click & Collect alone means missing the top of the funnel, where digital truly brings the customer into the store. That's exactly the ground of our missions in web-to-store, where digital feeds the store instead of replacing it.