Last updated: June 2026

Digital wallets accounted for 56% of global eCommerce value in 2025, per the Worldpay Global Payments Report 2026, and the gap between cards and wallets keeps widening at different speeds in Europe, the UK, and the US. The single biggest avoidable loss at checkout in 2026 is offering the wrong mix for the buyer’s region. iDEAL captures two-thirds of Dutch shoppers. Bancontact captures nearly three-quarters of Belgian ones. Klarna and Pay in 3 now run a quarter of UK BNPL volume. FedNow has turned account-to-account into a real US conversation. None of that shows up on a “global best practices” checklist; it appears in the country tables below.

Every region asks for a different mix of cards, wallets, and pay-later rails, and getting that mix wrong is one of the most common sources of payment friction we see at checkout across the eCommerce sites we work with.

Don’t let a buyer reach the payment step and bounce – shopping cart abandonment climbs fast when the payment methods on offer don’t match the ones shoppers trust in their market.

Popular payment methods in Europe, the UK, and the US

The right answer is country-specific. Inside each region, the patterns are split by checkout type (B2C vs B2B, low-ticket vs high-ticket) and by category (fashion runs BNPL, electronics and travel run cards and A2A). Below, we cover the share each method holds today and where the 2026 trends are pulling the mix.

Europe

Digital wallets and cards are the top two pools for eCommerce in Europe, but local rails decide regional outcomes. The European Central Bank’s SPACE study covering 2024 (released December 2024) puts online cards at 48%, e-payment solutions (wallets, PayPal, mobile apps) at 29%, direct debit at 5%, and instant payments at 5% across the euro area for eCommerce checkouts. By value, online payments reached 36% of total day-to-day euro-area spending.

The Worldpay Global Payments Report 2026 reads the same shift more sharply: digital wallets reached 52% of online value in Germany in 2025 – the highest in Western Europe – and PayPal is cited by 70% of German online shoppers. BNPL accounts for 18% of German eCommerce, driven by a longstanding consumer preference for invoice-based payments that predates the modern installment lending category.

Wero (the European Payments Initiative wallet), Bizum in Spain, and Vipps in Norway are launching in-store payments in 2026, pulling traditionally card-heavy POS markets toward digital wallets. That same wallet rail is what now decides most checkout outcomes online too.

Country-by-country trends in Europe

Each row below shows the share of online shoppers in that country who used the method in the past 12 months, per Ecommerce News Europe (updated March 10, 2026).

What this means in practice:

• In the Netherlands, iDEAL plus PayPal plus cards covers more than 80% of stated checkout preferences. Skip iDEAL, and a third of your buyers will not see a method they trust.
• In Germany, PayPal, purchase on account, and cards cover ~85%. Purchase on account is the line item most foreign merchants underestimate.
• In France and the UK, cards still lead, but PayPal sits second in both at 57% (FR) and 66% (UK) – wallet support is no longer optional.
• In Belgium, Bancontact is non-negotiable at 73% – it is the single biggest payment-method decision in the country.

Implementing iDEAL, Bancontact, Multibanco, or Klarna natively in Adobe Commerce typically falls under our Magento development work, where the regional gateway and the checkout flow are wired together as a single unit.

United Kingdom

The UK runs heavier on cards and mobile wallets than continental Europe, and BNPL adoption has accelerated faster than UK Finance predicted in 2020. UK Finance’s Payment Markets Report 2025 (covering 2024 data, published October 2025) lays out the live picture:

• Debit, credit, and charge cards (physical plus mobile) accounted for 64% of all UK transactions in 2024.
• 26.1 billion debit card payments were made in 2024 – the most-used single method overall.
• 57% of UK adults are registered for mobile wallets in 2024, up from 42% in 2023.
• 50% of UK adults used mobile contactless payments at least once a month in 2024.
• One in four UK adults (25%) used BNPL services in 2024, up from 14% the year before.
• Fashion led BNPL at 46% of transactions, average spend £114. The top three BNPL providers were Klarna, Clearpay, and PayPal Pay in 3.

For online checkouts specifically, Ecommerce News Europe (March 2026) puts the UK at cards 73%, PayPal 66%, Direct Debit 45%, Apple Pay 21%, open banking / bank transfer 20%, Google Pay 19%, Klarna 8%, other BNPL 5%.

What changes between now and 2034

UK Finance projects cards will still lead at roughly 67% of payments in 2034 – the share rises slightly because mobile contactless replaces cash, not because cards displace wallets. The rails that will pull the most volume away from cash between now and 2034 are Apple Pay, Google Pay, and open-banking-powered bank transfers (which already account for 20% of UK online shoppers). The BNPL ceiling depends on the timing of FCA regulations – the methods are not going away, but the marketing and risk treatment will tighten.

United States

US shoppers lead the world in online card use, but the BNPL and wallet trajectory is the bigger story. Per the Worldpay Global Payments Report 2026, digital wallets like Alipay, Apple Pay, and PayPal now account for over half of online and a third of in-person transaction value globally – the US sits ahead of that curve on the wallet side and behind Europe on local rails.

The BNPL numbers (Statista, Capital One Shopping, Chargeflow 2025-2026 syntheses):

• BNPL purchase volume in 2025 is expected to total $122.3 billion, up 10.9% YoY.
• BNPL reached 5% of total eCommerce payments worldwide in 2024 – and 6% in the US specifically.
• 91.5 million American consumers will use BNPL in 2025, up 5.78% YoY.
• Klarna’s US payment volume is expected to reach $25.77 billion in 2025, up 17.2% YoY.
• Klarna holds roughly 35% of the global BNPL market share.
• 52,330 US online retailers use Afterpay.

The implication for US checkout design: Affirm, Klarna, PayPal Pay in 4, and Afterpay should be treated as standard wallet tiers alongside Apple Pay and Google Pay, not as extras. For high-ticket categories (electronics, travel, home goods, B2B), the cost of card interchange is now high enough that A2A rails (RTP, FedNow) are showing up in serious checkout RFPs.

Trends in the US

Three signals to design for through 2027:

• Digital wallets continue to absorb card share at POS and online. Apple Pay, Amazon Pay, Google Pay, and PayPal are the four to support in the default order. Worldpay projects payment apps will account for 46% of global POS value by 2030 – or $15.6 trillion.
• BNPL has moved from a niche to a standard tier, with $122.3 billion projected for 2025. Risk-adjusted pricing into the merchant fee is the design question.
• Account-to-account is the high-ticket and B2B layer. FedNow and RTP enable instant settlement without card networks, which matters for invoices, marketplaces, and any cart over ~$500 where interchange is the largest line item.

How to choose payment methods by region

The shortest decision framework that actually works:

1. Start with the top three local methods in each country you sell to. Use the country table above as the anchor – if a method sits above ~20%, it has earned the slot.
2. Layer cards plus the leading wallet (Apple Pay + Google Pay in most markets, PayPal everywhere PayPal is offered). This handles roughly 50-70% of cross-border buyers in any single market without local rails.
3. Add BNPL where category fits: fashion, electronics, home goods, beauty in the UK and US, categorized credit and purchase-on-account in Germany, Klarna and Pay in 3 in the UK, Affirm, Klarna, and Afterpay in the US.
4. Add A2A for high-ticket and B2B baskets, where interchange savings of 1.5-2.5% per transaction compound into real margin. EU SEPA Instant and US FedNow are the relevant rails.
5. Pressure-test the gateway, not just the method. The same “PayPal” can mean five different gateway integrations depending on the platform, the one your acquirer offers, the one your platform’s marketplace ships, or the one your local payment service provider wraps. Reconciliation, refund flow, and dispute handling differ.

For Adobe Commerce and Magento stores specifically, our breakdown of Magento payment gateways maps each gateway to the regional methods it natively supports.

Account-to-account (A2A) and real-time payments in 2026

A2A rails (Wero in Europe, SEPA Instant across the euro area, FedNow and RTP in the US, PIX in Brazil) are pulling high-ticket and B2B transactions out of card networks. J.P. Morgan Payments’ 2026 trends note carries the key risk framing: “With account-to-account (A2A) payments, once money leaves an account, it is gone for good, so pre-transaction ID controls are essential.” That risk is why the EU is rolling out a digital ID wallet in 2026 for cross-border authentication.

What this means for eCommerce checkouts:

• B2C low-ticket (< $100): A2A is competing with wallets on UX, not on cost. Card or wallet wins on speed.
• B2C high-ticket ($100-1,000): A2A starts to make sense for one-tap, no-3DS-friction flows, especially in Europe (SEPA Instant) and the UK (Faster Payments / Open Banking).
• B2B and marketplaces ($500+): A2A is now the default for repeat invoices, supplier settlements, and any flow where the buyer is willing to authenticate once and authorize a recurring rail.

Account-to-account rails matter most for high-ticket and B2B eCommerce, where card interchange erodes margin, and invoice settlement is already the norm.

FAQ

Which payment methods should I support in Europe in 2026?

For most European markets, the answer is cards plus the top one or two local rails plus PayPal plus a wallet (Apple Pay or Google Pay). The local rails matter most: iDEAL in the Netherlands (66% of shoppers), Bancontact in Belgium (73%), purchase on account and SOFORT in Germany (40% and 15%), Multibanco in Portugal, Przelewy24 / BLIK in Poland. PayPal is universal across the region (52% of German online value in 2025 per Worldpay GPR 2026, 47% in NL, 57% in FR, 66% in UK, 39% in BE).

What are the most popular payment methods in the UK in 2026?

Cards lead at 73% of online shoppers, then PayPal at 66%, Direct Debit at 45%, Apple Pay at 21%, open banking / bank transfer at 20%, Google Pay at 19%, Klarna at 8%, other BNPL at 5% (Ecommerce News Europe, March 2026). Across all UK transactions (online plus offline), cards held 64% in 2024 (UK Finance Payment Markets Report 2025). One in four UK adults uses BNPL, fashion-led.

What share of US eCommerce is BNPL in 2026?

BNPL reached 6% of US eCommerce in 2024 and is projected at $122.3 billion in purchase volume in 2025 (up 10.9% YoY). 91.5 million Americans will use BNPL in 2025. Klarna’s US payment volume is projected at $25.77 billion in 2025, and Afterpay is live on 52,330 US online retailers (Statista / Capital One Shopping / Chargeflow 2025-2026 syntheses).

Which payment methods do B2C versus B2B buyers prefer in Europe?

B2C buyers in Europe split across cards, PayPal, and a country-specific rail (iDEAL, Bancontact, SOFORT, Multibanco). B2B buyers strongly prefer purchase on account / invoice settlement and direct debit, plus SEPA bank transfer for higher-ticket orders. Purchase on account is the single biggest category B2C-to-B2B payment difference in Germany at 40% of online shoppers (per Ecommerce News Europe 2026 data) – and it is the only category where most non-German merchants underprovision their checkout.

What payment methods should I support for cross-border eCommerce?

Cards (Visa, Mastercard) plus PayPal cover the broadest cross-border tail. Layer Apple Pay and Google Pay for mobile-heavy traffic. For each market where you exceed ~5% of revenue, add that market’s top local rail (iDEAL for NL, Bancontact for BE, SOFORT / purchase on account for DE, Klarna for fashion in the UK and US). Cross-border A2A (SEPA Instant for the euro area, Faster Payments/Open Banking for the UK) is the rail that will matter most for high-ticket cross-border in 2026-2027.

How does account-to-account (A2A) payment differ from a card payment for eCommerce?

A2A moves money directly from the buyer’s bank account to the merchant’s bank account, without the card network in the middle. Settlement is instant on real-time rails (SEPA Instant, FedNow, RTP, PIX, Faster Payments). For the merchant, the upside is lower fees (typically 0.2-0.5% vs 1.5-3% for cards) and no chargeback risk after settlement. The downside is that “once money leaves an account, it is gone for good” (J.P. Morgan Payments 2026), so pre-transaction ID controls and authentication carry more weight. A2A is becoming the standard for high-ticket and B2B checkouts in Europe and the US.

Will cards still dominate UK payments in 2034?

Yes – UK Finance’s Payment Markets Report 2025 projects cards at roughly 67% of all UK payments in 2034, up slightly from 64% in 2024. The rise is driven by mobile-wallet-on-card flows replacing cash transactions, not by cards displacing wallets. Cash share will keep falling, and open banking and BNPL will keep climbing inside eCommerce specifically.

Get your checkout and payments stack tuned for the region you actually sell in. Talk to our checkout team and we will map your live data, your platform, and your country mix to the payment methods that move your conversion rate, not your wishlist.

The post eCommerce Payment Methods in Europe, the UK, and the US (2026 Guide) appeared first on scandiweb.

If you are watching new users sign up and then disappear before the product ever clicks for them, you already know onboarding is where the leak is. People do not churn because your product is bad. They churn because they never reached the moment it became useful, and onboarding is the path to that moment.

This applies just as much to an eCommerce account or a checkout flow as it does to a mobile app: the first session decides whether someone comes back. Below are nine onboarding best practices that hold up in 2026, each with a real example and the principle underneath it, so you can apply them to a web app, a mobile app, or a store account flow.

Overview

• The job of onboarding is one thing: get a new user to first value as fast as possible. Everything else serves that.
• The numbers are unforgiving. Around 77% of users stop using an app within three days, and in Q2 2025 only about 8.4% of users completed onboarding within 30 days.
• Good onboarding can lift retention by up to 50%, which is why it is one of the highest-return fixes a product or store team can make.

What is user onboarding?

User onboarding is the guided first experience that takes someone from “just signed up” to “actively getting value.” It covers the welcome, any setup, and the first meaningful action, the point where the user understands what the product does for them. Good onboarding is not about showing every feature. It is about getting the user to one clear win quickly.

Why does user onboarding matter?

Because most people leave fast, and onboarding is the one lever that changes that. Around 77% of users abandon an app within three days of downloading it, and retention keeps falling from there: 2025 benchmarks put average day-7 retention near 6.9% on iOS and 5.2% on Android, dropping to roughly 3% by day 30. The longer since install, the fewer users remain active.

The cause is rarely the product itself. It is that users never saw the value clearly enough to come back. Fix the onboarding and the curve bends: studies have found proper onboarding can increase retention by up to 50%, and product teams that reworked their first-run experience have moved week-1 retention from 60% to 75% and week-10 from 10% to 25%.

How long should user onboarding take?

As short as it can be while still delivering the first win. Survey data has long shown most users expect to finish onboarding in under a minute, and that expectation has not softened. With completion rates as low as 8.4% within 30 days, every extra step is a place to lose people. Aim for the shortest sequence that reaches first value, and let everything else be learned in context later.

The 9 user onboarding best practices

1. Get users to first value in under a minute

Do not turn onboarding into an essay. Keep it short and pointed at the first win. The longer the sequence, the more people drop before they ever reach it. If your setup genuinely needs more steps, defer the optional ones and front-load only what is required to reach value.

2. Always let users skip

A real share of users prefer to explore on their own, by trial and error. Do not trap them. Let them skip onboarding at any step. Trip.com does this cleanly, with a skip option in the top corner of every screen, so confident users get out of the way and into the product.

3. Show what users can do, not what the product offers

Onboarding copy shapes how people perceive the product before they have used it. Keep it user-centered, not product-centered. Write about what the user can accomplish, not what the company built. The MUST app (for building movie libraries) does this well, framing onboarding around you creating your collection rather than around its feature list.

4. Lead with the value, fast

Use onboarding to make the value obvious, not to bury it under instructions. Show what the user can accomplish and why it is worth their time. Revolut’s onboarding is a strong example: it lists clear benefits, uses social proof, and keeps a visible call to action, so by the end you want to explore the app rather than escape it.

5. Engineer the “aha” moment

The aha moment is when the user first feels the product’s value for themselves. Design onboarding to reach it deliberately, not by accident. Identify the single action that makes your value click, then build the first session around getting users to it. Facetune does this by letting users feel how quickly they can edit a photo, the value lands before any commitment is asked.

6. Onboard progressively, not all at once

You do not need to teach everything in the first session. Progressive onboarding reveals features when they become relevant, instead of front-loading a carousel nobody remembers. Teach the first action now, surface the next capability the moment the user is ready for it, and let the product teach itself in context. This keeps the first run short while still helping users go deeper over time.

7. Reduce sign-up friction before you optimize the flow

The fastest onboarding is the one with fewer obstacles in front of it. Offer social or single sign-on, ask for only the data you truly need up front, and let people experience value before forcing account creation where you can. For eCommerce, this is the same logic as guest checkout: every required field before the first win is a reason to leave. Collect the rest progressively, once the user has a reason to stay.

8. Show progress with checklists and indicators

People finish what they can see themselves finishing. A short setup checklist or a progress bar turns an abstract process into a goal with a visible end, which pulls users through it. Keep the list short, order it by value, and celebrate completion. This works as well for a store account or a B2B portal setup as it does for a SaaS product.

9. Measure activation and keep iterating

Onboarding is never done. Define your activation metric, the specific first action that predicts retention, and track how many new users reach it. Then test changes against that number. This is where onboarding meets conversion rate optimization: the same discipline of measuring, hypothesizing, and testing that improves a checkout improves a first-run flow. Our A/B testing framework is a practical way to run those tests without guessing.

Onboarding beyond the app: web and eCommerce

These principles are not mobile-only. An eCommerce account flow, a first purchase, or a B2B portal setup is onboarding too, and it follows the same rules: reach first value fast, reduce friction, show progress, and measure activation. The “aha” moment for a store might be the first successful order or the first saved cart, and the same first-session design that retains app users retains shoppers. If you want the experience studied end to end, that is what a structured UX design and CX audit process is for.

FAQ

What is user onboarding?

User onboarding is the guided first experience that takes a new user from sign-up to actively getting value from a product. It covers the welcome, any necessary setup, and the first meaningful action, and its goal is to reach that first win as quickly as possible.

What makes onboarding effective?

Effective onboarding reaches first value fast, lets users skip, leads with what the user can do rather than a feature list, engineers an early “aha” moment, and is measured against an activation metric. It removes friction instead of adding instructions.

How long should user onboarding take?

As short as possible while still delivering the first win. Most users expect to finish in under a minute, and with onboarding completion rates near 8.4% within 30 days, every extra step costs you users.

Why do users abandon apps so quickly?

Around 77% of users stop using an app within three days, usually because they never saw its value clearly, not because the product is poor. Onboarding that communicates value early is the main lever for changing that.

Does onboarding apply to eCommerce and not just apps?

Yes. A store account flow, a first purchase, or a B2B portal setup is onboarding too. The same principles apply: reach first value fast, reduce sign-up friction, show progress, and measure activation.

How do you measure onboarding success?

Define an activation metric, the first action that predicts a user will stay, and track the share of new users who reach it. Then test onboarding changes against that metric using a structured A/B testing process.

If your activation rate has stalled and you are not sure which onboarding step is losing people, that is a measurable problem, not a mystery. Book a UX working session and we will map your first-run flow, find where users drop, and test the fixes that move retention.

The post User Onboarding Best Practices: 9 That Work in 2026 appeared first on scandiweb.

It is 9 a.m., your sale email goes out in three hours, and the template still looks like a newsletter from 2015. You know the copy is fine. What you are not sure about is whether the layout, the buttons, and the way it renders on a phone will actually get someone to click and buy.

That is the real job of an eCommerce email template: not to look pretty, but to move a reader from inbox to checkout. This guide covers both halves of that job, the design rules that make a template convert and the lifecycle templates that drive the most revenue, plus the sender requirements you now have to meet to reach the inbox at all.

Overview

• A converting email template is built like a mini landing page: one clear path from a scannable header to a single, obvious call to action.
• The templates that earn the most revenue are automated lifecycle emails, with abandoned-cart and welcome flows leading by a wide margin.
• In 2026, Gmail and Yahoo actively enforce the bulk-sender rules they introduced in 2024: authenticate your domain, offer one-click unsubscribe, and keep spam complaints under 0.3%, or your emails stop reaching the inbox.

What makes an eCommerce email template convert?

A converting template guides the eye down one clear path to a single action. Treat every email like a mini landing page with a defined header, body, and footer, then remove anything that competes with the click you want.

Structure

Keep a well-defined header, central body, and footer. The header carries your brand identity, a short menu, and a link to the browser version. The footer holds contact details, social icons, and the unsubscribe option. A single-column layout reads best and adapts to mobile far more reliably than multi-column grids.

Headings

Skip decorative captions. Clear, direct headings guide attention and break up the message so it can be skimmed in seconds, which is how most people read email.

Call to action

One email, one primary action. Make the main button bold, high-contrast, and written as a verb the reader can act on. Segment your list and match the call to action to each group, since a returning customer and a first-time subscriber are not deciding the same thing.

Visuals

Images, GIFs, and product shots carry the message when text alone would lose the reader. Use them to show the product in context, not as decoration. Stacked paragraphs with no visual break are the fastest way to lose attention.

Color

Color sets the emotional tone before a word is read, and the right palette depends on the occasion. A Black Friday email and a spring launch should not look the same. Match the palette to the campaign and to your brand, and use a single accent color to make the call to action unmistakable.

Typography

The typeface sets the tone and has to stay readable on every screen. A few rules hold up well:

• Use distinct fonts for headings and body text.
• Avoid ornate display fonts in the body.
• Set body text at 14 to 16px and headings at 20 to 30px.
• Keep spacing and styles consistent across the email.

Email design best practices

Width and length

A template width of 600 to 640px renders well across email clients, including the ones that frame your message next to other panels. Length depends on content: text newsletters can run longer, but image-heavy emails risk being clipped by Gmail, so front-load the message and the call to action.

Brand consistency

Use the same tone, colors, and fonts you use everywhere else. A reader should recognize the email as yours before they read the sender name. Carry your logo, link to your site and social profiles, and keep the call to action in your brand voice.

Layout patterns

Two layouts reliably guide the eye:

• Inverted pyramid: start with a wide header, narrow the content, and end on the call to action. It funnels attention straight to the click.
• Zigzag: alternate text and image blocks side to side. It shows products well on desktop but can break awkwardly on mobile, so test it before sending.

Video

Video lifts engagement, but most email clients do not play it inline. The reliable workaround is a clickable thumbnail, a static image or a GIF with a play icon, that opens the video in the browser. You get the attention without the rendering risk.

The eCommerce email templates that drive revenue

Design gets the click. Template type decides how often you get to ask. The highest-return emails are automated lifecycle flows triggered by behavior, not one-off campaigns. According to industry benchmarks, automated flows like abandoned cart and welcome series consistently outperform broadcast sends, with abandoned-cart emails reaching open rates near 45% and welcome emails often hitting 50 to 60%. These are the templates worth building well:

• Welcome series: the highest open rates you will ever see. Introduce the brand, set expectations, and make a first offer.
• Abandoned cart: the single highest-revenue automation for most stores. Show the cart, handle the objection, and keep the first email simple.
• Browse abandonment: for shoppers who viewed but never added to cart. Lighter touch than a cart email.
• Post-purchase: order confirmation, shipping updates, and a follow-up that sets up the next purchase or a review request.
• Replenishment: for consumable products, timed to when the customer is likely to run out.
• Win-back: for lapsed customers, often paired with an incentive. This is where a connected loyalty program does a lot of the work.
• Review request: turns a recent buyer into social proof for the next one.

Each of these is a reusable template once it is built. The work is getting the structure, timing, and trigger right once, then letting it run.

Make your emails mobile-responsive and dark-mode ready

More than half of email opens happen on a phone, so a desktop-only design leaves money on the table. Build one email that adapts cleanly between screens rather than hoping a fixed layout holds up.

Mobile best practices

• Set body text at 14 to 16px and headlines at 20 to 22px.
• Make sure any landing page you link to is also mobile-optimized.
• Hide non-essential elements on small screens to reduce clutter.
• Repeat the main call to action in longer emails so the reader never has to scroll back.

Dark mode

A large and growing share of readers use dark mode, which flips your palette to light text on a dark background. Design for it rather than against it:

• Use transparent PNGs so images do not sit on a clashing white block.
• Add a thin light outline to dark logos and icons so they do not vanish.
• Preview in a tool like the GetResponse or Litmus email tester, since dark-mode rendering varies by client.

Which Gmail and Yahoo sender rules apply in 2026?

Reaching the inbox in 2026 means meeting the bulk-sender rules Gmail and Yahoo introduced in February 2024 and now actively enforce, because a beautiful template does nothing if it never arrives. Per Google’s sender guidelines, senders mailing 5,000 or more messages a day to Gmail must meet three rules:

• Authenticate your domain with SPF, DKIM, and DMARC.
• Offer one-click unsubscribe in every promotional email, and honor it within two days.
• Keep your spam-complaint rate under 0.3%. At 10,000 sends, that is only 30 people marking you as spam.

Gmail ramped up enforcement through late 2025, so in 2026 non-compliant senders see real delivery failures, not just warnings. The practical takeaway: the unsubscribe link and your authentication setup are no longer optional polish. They decide whether the inbox accepts you at all.

How scandiweb approaches eCommerce email marketing

We build email as a system, not a series of one-off sends. That means a template library tied to the lifecycle flows that earn the most, design that holds up on real devices, and a deliverability setup that keeps you in the inbox as volume grows. It is the same retention thinking behind our broader email marketing best practices, and it connects to the on-site work in conversion rate optimization, since the email and the landing page have to tell one story.

Across more than 2,100 eCommerce projects since 2003, the pattern is consistent: the brands that win on email are not the ones with the prettiest single send. They are the ones whose templates, timing, and triggers work together, especially around peak moments like Black Friday.

Frequently asked questions

What is an eCommerce email template?

An eCommerce email template is a reusable, branded layout for a specific type of marketing email, such as a welcome, abandoned cart, or promotional send. A good template defines structure, typography, and the call to action once, so every email stays consistent and renders correctly across devices.

What size should an eCommerce email be?

Use a template width of 600 to 640px, which renders well across email clients. Keep the message focused: front-load your main content and call to action so it is visible before any clipping by clients like Gmail.

Which email templates make the most money in eCommerce?

Automated lifecycle flows outperform one-off campaigns. Abandoned-cart and welcome emails lead, followed by post-purchase, browse abandonment, replenishment, and win-back flows. These run continuously once built, so they compound over time.

How do I make an email template dark-mode friendly?

Use transparent images, add a light outline to dark logos so they stay visible, and test in a dark-mode preview tool. Dark mode flips your color scheme, so anything that relies on a white background needs to be checked.

What are the Gmail and Yahoo sender rules in 2026?

The rules, introduced in February 2024, are fully enforced in 2026. Bulk senders to Gmail and Yahoo must authenticate their domain with SPF, DKIM, and DMARC, include one-click unsubscribe in promotional emails, and keep spam complaints under 0.3%.

How long should a marketing email be?

There is no fixed length. Text-led newsletters can run longer, while image-heavy promotional emails should stay short to avoid clipping. Match length to the single action you want the reader to take.

Staring at a template that looks fine but is not converting? That gap is usually structure, timing, or deliverability, not copy. Book a working session with our email team and we will look at your templates and the flows behind them together.

About this guide

Maintained by the scandiweb Growth team. Reviewed by the scandiweb email marketing specialists. Last updated May 2026.

The post eCommerce Email Templates That Convert in 2026 appeared first on scandiweb.

You uninstalled MageMonkey, cleared the cache, hit Connect, and the sync still throws an error. Or every store view synced except the one that actually matters for abandoned-cart emails. If that is where you are right now, you are not doing anything wrong, the Mailchimp connector for Magento (Adobe Commerce) has a handful of well-known failure points, and the documentation for them is scattered across forum threads and old release notes.

We have set this integration up many times across client stores, hit the same walls, and worked out what reliably clears them. This guide is the short version: what the integration is in 2026, why the sync fails, how to connect a multi-store setup cleanly, and when the free connector stops being enough.

Overview

• The integration is now called Mailchimp for Adobe Commerce (formerly Mailchimp for Magento 2), a free connector that syncs customers and order data to a Mailchimp audience.
• Most “sync failed” errors trace back to one of three causes: a leftover MageMonkey install, an orphan Mailchimp store, or a multi-store setup on subdirectories rather than subdomains.
• You can only fully connect one store view per Mailchimp audience, so the real decision is which store view earns the full feature set.

What is the Mailchimp for Magento (Adobe Commerce) integration in 2026?

Mailchimp for Adobe Commerce is a free integration that adds your store’s customers and their order information to a Mailchimp audience, so you can run targeted campaigns, product retargeting, and abandoned-cart automations from purchase data. It used to be branded “Mailchimp for Magento 2” and you will still see both names in the wild, the connector and the underlying behavior are the same.

You install it from the Adobe Commerce Marketplace (it is free), or via Composer, then connect it to your Mailchimp account from the store’s admin. The setup itself is short. The friction is almost always cleanup from a previous tool or a multi-store quirk, which is what the rest of this guide covers.

Before you connect: remove MageMonkey completely

If your store has been live for years and you used Mailchimp before, you were probably running MageMonkey, the older extension. As the Mailchimp documentation states, you must uninstall MageMonkey before you can use Mailchimp for Adobe Commerce.

Here is the part the documentation skips: removing the extension files is not always enough. We have seen connections keep failing after developers removed MageMonkey, because not every path and database table was cleaned up during removal, and the leftovers caused errors. Ask your developers to confirm the database is clean, not just that the module folder is gone.

Why does the Mailchimp Magento sync fail?

When the sync fails, you usually see a generic connection error rather than a clear cause. Based on the extension developers’ guidance, that error points to one of three scenarios:

1. You have a parent scope configured.
2. You are using multi-store on the same domain under one Mailchimp account.
3. You have an orphan Mailchimp store left over from a previous connection.

The next two sections handle the orphan store and the multi-store cases, since those are the ones that send most people in circles.

An orphan store is blocking the connection

If you previously connected Magento to Mailchimp through MageMonkey, uninstalling it leaves an orphan store inside Mailchimp. That orphan blocks the new connector from claiming the store.

To clear it, log in to Mailchimp and open the Connected Sites page. You will see the connected store, and a Disconnect button in the lower-left corner that removes the orphan. If you do not see any connected account but the connection still fails, contact Mailchimp support and ask them to check for orphan stores that are blocking the connection, they can see ones the interface hides.

How do you connect a multi-store Magento setup to Mailchimp?

Mailchimp can connect several store views, but the layout of your store decides how cleanly it works. If your store views sit on separate subdomains, you are fine, you can connect all of them to your Mailchimp account. If your store views are separated by subdirectories, you cannot fully connect all of them.

In a subdirectory setup, one store view connects completely and shows up under Connected Sites. The others will still sync subscriber and order data, but they will not appear in Mailchimp and will not support Connected Site features like product retargeting, abandoned-cart emails, or Google Ads. So the data flows, but the revenue-driving automations only run on the one fully connected store view.

That makes the setup a decision, not just a configuration: pick the store view that earns the full feature set. Usually that is your highest-revenue or highest-traffic market, the one where abandoned-cart and retargeting automations pay for themselves fastest.

One more rule that trips people up: each store view (each subdirectory) must connect to a separate Mailchimp audience. Create a new audience in Mailchimp for every store view you plan to connect, then point each store view’s backend configuration to its own list.

Connection steps after the errors are resolved

Once you have cleaned up MageMonkey and disconnected any orphan stores, connect in this order:

1. Uninstall the MageMonkey extension correctly, with developer help to confirm the database is clean.
2. Open Connected Sites in Mailchimp and disconnect the old sites.
3. Go to the Magento (Adobe Commerce) backend and clear the cache.
4. In the Mailchimp extension settings, save the configuration at the default scope, do not enable the extension for the default scope, just save the configuration.
5. Switch to the store view you want to connect, enable the extension, and save the configuration there. Assign a separate audience to each store view you connect.

If you still hit an error after these steps, it is almost always an orphan store the interface is not showing, go back to step 2 and have Mailchimp support confirm it on their side.

Free connector or custom integration: which do you need?

The free connector is the right call for most stores. It syncs customers and orders, powers the standard automations, and costs nothing. You should look beyond it when one of these is true:

• You run a subdirectory multi-store and need full feature parity across markets, not just one fully connected store view.
• You want data the standard sync does not pass, such as custom attributes, loyalty tiers, or product-level segmentation rules.
• You are evaluating whether Mailchimp is even the right platform. Stores chasing predictable email revenue often move to a platform like Klaviyo, our Klaviyo migration case study covers a 4.8x email-revenue increase after one such move, and our email marketing best practices guide covers what to fix before you blame the platform.

If you need the connector to carry data it does not support out of the box, that is custom Magento integration services territory rather than a settings problem.

FAQ

Is the Mailchimp Magento integration free?

Yes. Mailchimp for Adobe Commerce (formerly Mailchimp for Magento 2) is a free connector available through the Adobe Commerce Marketplace. You only pay for your Mailchimp plan based on audience size and features, not for the integration itself.

Why is my Mailchimp Magento integration not working?

The most common causes are a partially removed MageMonkey install, an orphan Mailchimp store left from a previous connection, or a multi-store setup on subdirectories. Clean up the old install and disconnect orphan stores in Mailchimp’s Connected Sites before reconnecting.

Can I connect a multi-store Magento setup to Mailchimp?

Yes, but with limits. Store views on separate subdomains connect fully. Store views split by subdirectories will sync data, yet only one can be fully connected with retargeting and abandoned-cart features. Assign a separate Mailchimp audience to each store view.

Does the integration work with Adobe Commerce and Magento Open Source?

Yes. The same connector supports Adobe Commerce and Magento Open Source on supported 2.4.x versions. “Mailchimp for Magento” and “Mailchimp for Adobe Commerce” refer to the same tool after the Adobe rebrand.

What data does the integration sync?

It syncs customers and their order information into a Mailchimp audience, which powers segmentation, product retargeting, and abandoned-cart automations on the fully connected store view.

Should I use Mailchimp or move to another email platform?

Mailchimp’s free connector is fine for getting started. If email is becoming a serious revenue channel and you need deeper segmentation or automation, it is worth comparing platforms, our email marketing services team can scope that against your current setup.

Still staring at a sync error, or unsure which store view to fully connect? Send us your setup and the scandiweb team will tell you exactly what is blocking the connection. Tell us about your setup, and we will get your Mailchimp data flowing.

The post Mailchimp Magento Integration: A Pain-Free 2026 Setup appeared first on scandiweb.

Last updated: May 2026

If your eCommerce store uses AI – product recommendations, chatbots, pricing tools, fraud detection – the EU AI Act likely applies to some part of your technology stack.

The regulation entered into force in 2024, with rules rolling out between 2025 and 2027. Companies using AI in the EU will need to understand how their systems are classified and what obligations come with them.

You’re probably asking: “How does the EU AI Act actually affect my store?”

We’re here to answer the key questions about this regulatory act and explain what it means for your business.

How the EU AI Act classifies AI systems

To begin with, it helps to understand how the EU AI Act classifies AI systems. The Regulation (EU) 2024/1689 uses a risk-based model, meaning not all AI is regulated in the same way. Instead, AI systems are grouped into four categories:

1. Prohibited AI
2. High-risk AI
3. Limited-risk AI
4. Minimal-risk AI

Most retail AI tools – recommendation engines, AI search, demand forecasting, and merchandising algorithms – fall into the limited-risk or minimal-risk categories. That means businesses can continue using them, though certain transparency or documentation requirements may apply.

Still, there are grey areas. Dynamic pricing, customer profiling, and AI chatbots are all common features in modern online stores, and each of them interacts with the regulation in slightly different ways.

Below are ten questions eCommerce teams ask most often when trying to understand how the EU AI Act affects their business.

EU AI Act for eCommerce: 10 key questions answered

Does the EU AI Act apply to my Shopify or Magento store?

Yes, the EU AI Act applies to your Shopify or Magento store if you serve customers in the EU, regardless of where your business is based. The Act applies based on where the AI’s output is used, not where the AI is built or hosted. A store selling into Germany or France using a US-based recommendation engine is still subject to the Act’s rules for that system.

The EU AI Act applies to companies that use AI systems affecting customers in the EU. That includes eCommerce stores running AI-powered tools.

For a Shopify or Magento (Adobe Commerce) store, the Act becomes relevant when the store uses AI features such as:

• Product recommendation engines
• AI-powered search
• Chatbots or virtual assistants
• Fraud detection systems
• Dynamic pricing tools
• Personalization algorithms.

In these cases, your business is responsible for how the AI system is used, even if the technology comes from a SaaS vendor.

The good news is that many of these AI eCommerce tools fall into low regulatory categories. In practice, this usually means basic transparency or documentation, not heavy compliance procedures. To understand what compliance may be required, ask these quick questions about each AI tool used in your store:

1. Does the AI interact directly with customers?
2. Does it make automated decisions about people?
3. Does it rely on personal data?

If the answer to any of these is no, it will usually fall into a low-risk category and require little additional compliance.

Does the EU AI Act apply to non-EU eCommerce businesses?

Yes, the EU AI Act applies to non-EU businesses, including US-based stores, whenever their AI’s output is used in the EU. A US store using AI to power its European storefront, send personalized emails to EU customers, or operate a chatbot accessible to EU users falls under the Act. The trigger is where the system has effects, not where the company is headquartered.

Is my AI-powered recommendation engine classified as high-risk?

A typical recommendation engine is in the limited- or minimal-risk tier of the EU AI Act and does not trigger the high-risk obligations. It moves into high risk when it crosses into prohibited territory, such as manipulating purchase decisions based on a customer’s emotional state or exploiting vulnerabilities, such as financial distress. The line is set by Article 5 prohibitions, not by the recommendation function itself.

So, in most cases, no. Recommendation engines used in eCommerce are typically not considered high-risk AI under the EU AI Act.

High-risk systems mainly involve areas where automated decisions can significantly affect people’s rights or opportunities. For example, hiring decisions, credit scoring, biometrical identification etc.

Typical retail personalization tools do not fall into these categories.

However, there are a few situations worth reviewing. Regulators may look more closely if an algorithm:

• Excludes certain groups from seeing offers
• Targets vulnerable users with manipulative recommendations
• Makes significant automated decisions without oversight.

Even then, these issues usually fall under consumer protection rules or GDPR profiling requirements, not the high-risk AI category itself.

Can I still use AI for dynamic pricing in the EU?

Yes, dynamic pricing remains legal under the EU AI Act, but with limits. Pricing that responds to inventory, demand, time of day, or location is permitted. Pricing that targets an individual customer based on profiling, especially when that profiling exploits a known vulnerability or operates without their knowledge, risks falling under Article 5 prohibitions. The Act does not ban dynamic pricing; it bans the manipulative use of it.

Problems may arise if an algorithm:

• Targets individuals with higher prices based on sensitive personal data
• Pressures users to buy through manipulative behavioral signals
• Hides discriminatory pricing practices.

These situations can trigger scrutiny under consumer protection law or GDPR.

What does “profiling clients” mean under the Act, and why is it prohibited?

Profiling under the EU AI Act means automated analysis of personal data to predict or evaluate an individual’s behavior, preferences, or vulnerabilities. The Act prohibits profiling that exploits vulnerabilities (age, disability, social or economic situation) to materially distort behavior or cause harm. General preference profiling for product recommendations is allowed, provided it does not cross into manipulation or the exploitation of vulnerabilities.

In simple terms, profiling means using personal data to predict or evaluate a customer’s behavior.

In eCommerce this happens all the time. Stores analyze browsing history, past purchases, and engagement signals to personalize recommendations or marketing messages.

This type of profiling is not automatically prohibited under the EU AI Act Article 5 prohibited practices.

Regulators become concerned when AI systems manipulate users or exploit vulnerabilities. Examples include systems that:

• Target vulnerable users based on age, disability, or financial situation
• Push people toward decisions they would not otherwise make
• Rank or score individuals based on behavior.

The key difference lies in intent and impact. Personalization that helps customers discover relevant products is generally acceptable. Systems designed to manipulate behavior or discriminate against certain groups can trigger regulatory scrutiny.

Do I need to disclose when customers are talking to a chatbot?

Yes. The EU AI Act’s transparency obligation requires you to inform users when they are interacting with an AI system, including chatbots. A short, clear disclosure at the start of the conversation is sufficient. The rule applies even to chatbots in the limited-risk tier and exists to help users make an informed choice about whether to continue.

If users could reasonably assume they are speaking with a human, the system must clearly indicate that the interaction is automated.

Compared with other parts of the EU AI Act, this obligation is relatively light. A compliant implementation for AI chatbots and virtual assistants is usually simple:

• Label the assistant clearly as an AI chatbot
• Show a short disclosure when the conversation starts
• Mention AI usage in the help center or privacy notice.

This rule applies regardless of whether the chatbot is built internally or provided by a third-party vendor. If your store deploys the chatbot, your business is responsible for the disclosure.

What are the actual penalties for non-compliance?

The EU AI Act sets penalties at up to 35 million euros or 7 percent of global annual turnover, whichever is higher, for prohibited-practice violations. Other breaches carry lower but still significant fines (up to 15 million euros or 3 percent of turnover). Enforcement is led by national authorities in each EU member state, coordinated through the AI Office.

The EU AI Act penalties are similar in scale to GDPR and depend on the type of violation.

The maximum fines are:

• Up to €35 million or 7% of global annual revenue for prohibited AI systems
• Up to €15 million or 3% of global annual revenue for violations involving high-risk AI systems
• Up to €7.5 million or 1.5% of global annual revenue for providing incorrect information to regulators.

Regulators apply the higher of the fixed fine or the revenue percentage.

The good news is that most eCommerce AI tools typically fall into minimal-risk or limited-risk AI. In these cases, compliance mainly involves transparency and responsible data use. However, the bigger risk is not the fine itself. It is not knowing which AI systems are running in the business and how they fit into the EU AI Act risk categories.

How does the Act affect my use of US-based AI tools like OpenAI or Google?

You can still use US-based AI tools like OpenAI or Google in the EU, but responsibility for compliance often falls to you, the deployer, as well as the provider. You need a lawful basis under GDPR for the data sent to the tool, a documented assessment of the AI’s risk tier, and disclosure to users when AI is involved. The Act applies to the output used in the EU, regardless of where the model runs.

So, using AI tools from US vendors like OpenAI is still allowed. What matters is whether the AI system is used in the European market.

That means that you are responsible for how the AI tool is used in your store, even if the technology comes from a third-party vendor.

When using external AI tools, it is worth checking:

• What data the AI system processes
• How the system generates outputs
• Whether any transparency or disclosure obligations apply.

Many major vendors are already preparing documentation for EU AI Act compliance. Retailers should still confirm how their providers handle data sources, model behavior, and transparency requirements.

For organizations that would rather keep AI compute and personal data entirely within Europe, our guide to sovereign AI covers the three deployment models and when each is appropriate.

What is a “chain of custody” for data, and do I need one?

A chain of custody for AI is the documented record of where personal data and AI outputs come from, how they are processed, and who is responsible at each stage. The EU AI Act does not name it as a chain of custody, but high-risk systems require comparable record-keeping. For eCommerce, even outside high-risk, building this documentation early makes any future audit, GDPR, or AI Act much easier.

In simple terms, chain of custody means knowing where the data used by an AI system comes from and how it moves through the system.

This usually involves tracking:

• Where the input or training data originates
• How the data is processed by the AI model
• Who has access to the data
• How outputs are generated and stored.

Under the EU AI Act, these traceability requirements mainly apply to high-risk AI systems. For most eCommerce use cases, the requirement is relatively light. Retail AI tools typically rely on store data that businesses already control.

The practical step is to keep basic documentation of:

• Which AI systems are used in the store
• What data feeds those systems
• Which vendors provide the technology
• How the AI outputs affect customer interactions.

Many companies already maintain similar documentation through GDPR compliance and vendor reviews.

My AI is purely internal with no personal data – am I still affected?

Internal-only AI with no personal data is in minimal-risk and carries no specific EU AI Act obligations. However, two cautions apply: the moment a model touches employee data, candidate data, or customer support transcripts, it can move into limited- or high-risk territory; and AI that informs decisions about people, even indirectly, may attract scrutiny if outputs are not documented.

So, usually no. Internal AI systems typically fall into the minimal-risk category under the EU AI Act.

These are use cases such as:

• Demand forecasting
• Inventory optimization
• Warehouse routing
• Supply chain predictions
• Internal analytics models

Because these tools support internal decision-making and do not directly influence customers, they are generally considered minimal-risk AI. These are largely unregulated under the EU AI Act. Businesses can continue using them without certification or strict transparency requirements.

That said, companies should still keep a basic record of where AI is used internally. Internal systems sometimes evolve into customer-facing features, such as automated pricing or product recommendations, which can change the regulatory requirements.

Where do I start if I want to become compliant?

Start with an inventory: list every AI system in use across your store, name the purpose, the data inputs, and the risk tier each one likely falls under. Then prioritize by risk and by EU customer exposure. For most eCommerce brands, the fastest practical wins are chatbot disclosure, recommendation-engine review for Article 5 risk, and a documented data-handling map. Larger or higher-risk systems need a formal conformity assessment, which is where bringing in expertise helps.

The fastest path is to bring in a partner to audit AI use, document the risk-tier mapping, and implement technical controls at the code level. scandiweb’s EU AI Act compliance service handles the work end-to-end, from initial inventory through conformity assessment and ongoing monitoring, so internal teams stay focused on the storefront.

The EU Commission’s AI Act page is the authoritative source for both the legal text and the dates each obligation takes effect.

Typical places to check include:

• Recommendation engines and personalization tools
• Search and merchandising algorithms
• AI chatbots or support assistants
• Fraud detection systems
• Dynamic pricing tools
• Marketing automation platforms using predictive models.

Once these systems are identified, classify them according to the EU AI Act risk categories: prohibited, high-risk, limited-risk, and minimal-risk.

Most retail AI tools fall into limited-risk or minimal-risk, which usually require transparency or documentation rather than strict regulation.

After classification, review three areas:

• Transparency – customers should know when they interact with AI
• Data usage – understand what data feeds the system
• Vendor responsibilities – confirm how third-party AI tools handle compliance.

Final thoughts

The EU AI Act does not aim to stop companies from using AI in eCommerce. They want companies to understand where AI influences people and to be transparent about it.

For most retailers, the real work is not removing AI tools or slowing innovation. It is knowing which systems you run, what data they rely on, and where automated decisions affect customers. Once that visibility exists, compliance becomes part of normal governance – much like GDPR did a few years ago.

If you are working out where your store stands across the EU AI Act risk tiers, talk to our team, and we will run a short compliance review against your live AI systems before you commit to a full conformity program.

The post EU AI Act for eCommerce: 10 Questions Every Business Is Asking in 2026 appeared first on scandiweb.

Across 30 million Shopify sessions, Deloitte and Google found that a 0.1-second speed gain lifts sales by 8.4%. If your Shopify store loads in 4 seconds on mobile, that is the gap between the revenue you have and the revenue the same traffic could earn at 2 seconds.

This guide walks through what is actually slowing Shopify stores in 2026, how to diagnose it, and the structural fixes that move a real store from a PageSpeed score in the 20s to the high 80s. Every tip below has been used by the scandiweb Shopify team on live client work.

Overview

• The single biggest cause of slow Shopify stores in 2026 is third-party app sprawl, not the platform itself.
• A good mobile PageSpeed score for a real Shopify store sits between 60 and 80, not 100.
• The fastest wins come from auditing apps, compressing images, and deferring non-critical scripts, in that order.

What slows Shopify stores in 2026

The average Shopify store now installs 15 to 20 apps, and each one can add between 100 and 500 milliseconds of JavaScript to every page load (Easy Apps eCom, 2026). The result is predictable: a default Shopify theme without customisation often scores around 70 on mobile, while the same store with a typical app stack drops into the 20s.

The four root causes the scandiweb Shopify team sees most often:

• Render-blocking app scripts that load synchronously on every page
• Hero images and homepage carousels with unoptimised file sizes
• Third-party tracking pixels firing before the main thread is free
• Legacy theme code with deprecated patterns from earlier Online Store versions

None of these are platform limitations. They are configuration problems with concrete fixes.

How do I check my Shopify store speed?

The right way to audit Shopify performance in 2026 is to run three tools side by side and triangulate. No single tool gives the full picture.

• Google PageSpeed Insights for Core Web Vitals (LCP, INP, CLS) and mobile/desktop scores
• Shopify’s own performance dashboard in the admin for the store-wide trend
• Google Lighthouse in Chrome DevTools for the detailed render trace and the unused-JavaScript report

Mobile results will always look worse than desktop. That is normal. The threshold to aim for: above 60 on mobile is a healthy ceiling for a feature-rich store, above 80 is excellent. Read our guide on reading PageSpeed Insights for the nuance most teams miss.

9 ways to speed up your Shopify store

These nine fixes are ordered by impact. Apply them in sequence, retest after each step, and you will see the cumulative score climb meaningfully within a single sprint.

1. Audit and remove unused apps

App bloat is the single biggest speed killer on Shopify (PageSpeed Matters benchmarks, 2026). Go to your Shopify admin, list every installed app, and remove every one that is not actively earning its keep. Then disable any leftover app embeds under Online Store, Themes, Customize, App embeds. Stopping the script from loading is faster than optimising it.

2. Compress images

Hero images and product photography in PNG or non-optimised JPEG are a common cause of long LCP times. Use a tool like TinyPNG or a Shopify-native image optimiser to bring file sizes down before upload, then verify by re-running Shopify Analyzer.

3. Implement lazy loading

Make sure the hero image above the fold loads eagerly, and every image below the fold uses loading="lazy". Shopify themes from 2024 onward generally do this by default, but custom theme work often breaks it.

4. Minify JavaScript and CSS

Use a tool like JavaScript and CSS Minifier to remove whitespace, comments, and dead code from theme files. The savings per file are small individually but compound across a real-world theme.

5. Reduce HTTP requests

Each request is a round-trip your browser pays for. Run Shopify Analyzer, identify the requests that fire on every page (often analytics, chat widgets, A/B test tools), and either consolidate them through Google Tag Manager or remove the ones that no longer have a business case.

6. Defer or remove third-party scripts

Tracking pixels for Meta, TikTok, Klaviyo, and similar services should not block the initial render. Move them behind a tag manager and load them after the main thread is free. If a script cannot be deferred, ask whether it is paying for itself in attribution value.

7. Optimize theme code

Reduce HTML parent elements where possible. Look for scripts that execute twice on the same page (common when a theme update introduced a duplicate call). Tighten the CSS structure. If your store uses custom JavaScript, profile it in Lighthouse and refactor the worst offenders.

8. Use a lightweight theme

Themes built on Shopify Online Store 2.0 with section groups load faster than older customised themes carrying years of legacy code. For a store with significant performance debt, a controlled theme rebuild often pays back faster than tip-by-tip optimisation.

9. Monitor with Web Vitals

Speed is not a one-time fix. Install a Web Vitals monitoring tool, set up alerts when LCP or INP regresses, and review monthly. New apps, new theme code, and seasonal traffic all push the numbers around.

scandiweb case study: From PageSpeed 20 to 89

The scandiweb Shopify team recently launched a store on Online Store 2.0 for a client who arrived with a mobile PageSpeed score of 20. Within one optimisation sprint, the score climbed to 59. After a second pass with the full nine-tip stack above and a controlled theme rebuild, the same store now sits at 89 on mobile.

The order of operations that produced the 20-to-89 climb:

• App audit and removal first, dropping nine non-essential apps. Mobile score climbed from 20 to 38.
• Image compression and lazy-loading pass next. Score moved from 38 to 51.
• Third-party tracking script consolidation under Google Tag Manager. Score climbed to 59.
• Theme refactor and unused-CSS removal. Final score of 89 on mobile, 96 on desktop.

The store’s add-to-cart rate climbed by 14% in the 30 days after the launch, and mobile bounce rate dropped by 11 percentage points. The 0.1-second-equals-8.4% Deloitte benchmark held up almost perfectly in practice.

How fast should a Shopify store load in 2026?

The honest answer in 2026 is that Largest Contentful Paint under 2.5 seconds on mobile is the target. Most Shopify stores sit between 3.2 and 5.1 seconds, which is above the Core Web Vitals threshold. A PageSpeed Insights mobile score above 60 is healthy for a real-world store with apps and product photography. Above 80 is excellent.

For comparison, the SERP top-three results on most commercial queries load in 1.8 to 2.4 seconds and score above 75 on mobile. That is the bar your store needs to clear to compete for the same traffic.

When should you hire a Shopify performance specialist?

If your store’s mobile PageSpeed score is below 40, or LCP is above 4 seconds, the in-house fixes above will move the number but rarely past 60 without theme-level work. That is the point to bring in a Shopify partner with Liquid optimisation experience and a track record of measurable speed gains on production stores.

The scandiweb Shopify team has shipped Shopify performance work across stores doing seven and eight figures in annual revenue. The pattern is consistent: app audit, image pipeline, theme rebuild, monitoring. The order matters more than the tactics.

Frequently Asked Questions

Why is my Shopify store so slow?

The most common cause is third-party app sprawl. The average Shopify store installs 15 to 20 apps, each loading 100 to 500 milliseconds of JavaScript on every page. Image weight and render-blocking tracking pixels are the next two most common causes.

What is a good PageSpeed score for Shopify?

For a real-world Shopify store with apps and product photography, above 60 on mobile is healthy and above 80 is excellent. Chasing 100 is rarely a good use of engineering time once you are past 80.

Does Shopify performance affect SEO?

Yes. Core Web Vitals are a confirmed Google ranking factor, and Largest Contentful Paint correlates strongly with mobile conversion. A faster store ranks better and converts more of the traffic that arrives.

How long does Shopify speed optimization take?

A first-pass app audit and image compression sprint typically takes one to two weeks and moves the score by 15 to 30 points. A full theme refactor for a store with significant performance debt takes four to eight weeks, depending on the catalog size and integrations.

Can I optimize Shopify performance without a developer?

Partly. The app audit, image compression, and lazy-loading setup can be done in the admin. Theme code optimisation, JavaScript profiling, and the deeper Liquid refactor work need a developer with Shopify experience.

Does Shopify Plus load faster than the standard plan?

Not directly. Shopify Plus and the standard plan run on the same infrastructure. What Plus unlocks is access to checkout extensibility, multi-storefront architecture, and theme customisation depth, all of which can be used to build a faster store but do not deliver speed automatically. See our Shopify vs Shopify Plus comparison for the full breakdown.

About this guide

Maintained by the scandiweb Shopify team. Reviewed by Rolands Popovs, COO. scandiweb is a Shopify Plus Partner with 20+ years of eCommerce delivery and 2,100+ projects shipped across Shopify, Adobe Commerce, and BigCommerce.

If your store is still slow after working through this playbook, the bottleneck is usually in the theme code or in an app that hides its impact behind a lazy load. Get in touch with the scandiweb Shopify team for a performance audit and a concrete fix plan.

The post Shopify Performance Optimization: A 2026 Playbook for Faster Stores appeared first on scandiweb.

Building an MCP server means deciding what parts of your business to expose to AI assistants, wiring those to your live systems, and choosing how an assistant reaches them safely. The work is approachable to start and easy to underestimate in production, which is the part most guides skip.

This article is part of our series on the Model Context Protocol. The first explained what MCP is, the second covered ChatGPT apps for eCommerce, and this one is about the layer underneath both: the server you build so assistants can actually access your catalog, inventory, and orders. 

You will not need to write code to follow this. There is one short example, so the shape of the work is concrete, and your engineering team can take it from there.

What an MCP server is, in one minute

An MCP server is a program that exposes your data and actions to AI assistants through the Model Context Protocol, the open standard that lets clients like Claude and ChatGPT connect to external systems. The server publishes a set of capabilities, the assistant calls them during a conversation, and your systems stay behind a single consistent interface. Building one means deciding what to expose, wiring it to your data, and choosing how the assistant reaches it.

The reason this is one standard rather than a dozen integrations matters for budgeting. Before MCP, connecting your store to each assistant meant a separate, bespoke integration for every one. MCP replaced that with a single interface that many assistants already know how to use. You build the server once, and Claude, ChatGPT, and other clients can all reach it. 

The standard is also no longer a single-vendor project. MCP was introduced by Anthropic in November 2024 and, in December 2025, was donated to the new Agentic AI Foundation under the Linux Foundation, with Anthropic, Block, and OpenAI as co-founders. By that point, it counted roughly 97 million monthly SDK downloads and around 10,000 active servers. That governance move is the de-risking signal that the interface you build against is an industry standard.

MCP server vs MCP client

An MCP server exposes capabilities, your tools, data, and prompts, while an MCP client is the application that consumes them, such as Claude Desktop, ChatGPT, Cursor, or an agent you write. The client connects to one or more servers and decides when to call them. When you build an MCP server, you are building the side that holds your systems, and the assistant is the client that talks to it.

This distinction keeps a project scoped correctly. You are not building the AI, and you are not building a chat interface. You are building the connector that lets an assistant someone else operates reach the things only your business can provide: your real products, your real stock levels, your real order status. That is a smaller and more controllable piece of work than teams often assume when they first hear “build an MCP server.”

The building blocks: tools, resources, and prompts

An MCP server exposes three kinds of capability. Tools are functions the assistant can call to take an action, such as searching a catalog or checking stock, usually with user approval. Resources are read-only data the assistant can pull in, like a product record or a document. Prompts are reusable templates that guide the assistant through a task. Most commerce servers lean on tools and resources.

For a store, the mapping is intuitive once you see it:

• Tools are the actions: search products, check availability, get order status, start a return.
• Resources are the reference data: a product detail record, a sizing guide, a returns policy.
• Prompts are the guided flows: a “help me find a gift” template that walks the assistant through the right questions.

It helps to picture one real exchange. A shopper tells an assistant they need running shoes for wide feet under a set budget. The assistant calls your product-search tool with those constraints, your server queries the live catalog and returns matching products with current prices and stock, and the assistant presents them in the conversation. If the shopper then asks where an existing order is, the assistant calls your order-status tool. Each of those is a capability you chose to expose, and nothing happens that you did not define.

Deciding which capabilities to expose, and which to hold back, is the first real design conversation. A good early scope is narrow: two or three high-value tools that map to questions customers actually ask, rather than an attempt to expose the whole platform on day one.

How to build an MCP server, step by step

Building an MCP server takes five stages: pick an SDK, define the server, register the tools and resources you want to expose, choose a transport, then connect a client and test. For a store, the useful version exposes real capabilities, product search, stock checks, order status, rather than a toy example. The official Python and TypeScript SDKs handle the protocol, so most of the work is wiring tools to your own systems.

1. Pick an SDK. Official SDKs exist for TypeScript, Python, C#, and Go, among others. Most teams choose the language their commerce systems already use, so the server can talk to existing code directly.
2. Define the server and register tools. This is where you name the capabilities. A product-search tool, a stock-check tool, an order-status tool.
3. Wire each tool to your real data. This is the bulk of the work and the part no tutorial can do for you: connecting each tool to your catalog, inventory, and order systems.
4. Choose the transport. Streamable HTTP for anything customer-facing, as covered above.
5. Connect a client and test. Point Claude or ChatGPT at the server and confirm each tool returns correct, current data.

A single product-search tool in Python with the FastMCP framework looks like this:

from fastmcp import FastMCP

mcp = FastMCP("store-catalog")

@mcp.tool()
def search_products(query: str) -> list[dict]:
    """Search the live catalog and return matching products."""
    return catalog.search(query)

That is the entire pattern. The framework turns the function and its description into a tool the assistant can call, and your team repeats the pattern for stock checks and order status. The hard, valuable work is behind catalog.search: making sure it returns accurate, current, well-structured data. When scandiweb built tools like the Claude Blog Factory, the protocol layer was quick, and the systems integration was where the real effort and care went. A store-specific server follows the same balance.

Choosing a transport: stdio vs Streamable HTTP

MCP servers talk to clients over JSON-RPC using one of two transports. Use stdio when the server runs on the same machine as the client, such as a local developer tool. Use Streamable HTTP when the server is remote and reached over the network, which is the case for anything customer-facing. The older HTTP and SSE transport is deprecated, so new servers should not start with it.

If the person using the AI client also controls the machine the server runs on, use stdio; for anything your customers or a hosted assistant reach, use Streamable HTTP. 

Almost any commerce use case is the second kind, because the server lives on your infrastructure and assistants reach it over the internet. There is one technical gotcha worth knowing so it does not surprise your team mid-build: a stdio server must never write logs to standard output, because that channel carries the protocol messages and stray output corrupts them.

Connecting your server to Claude and ChatGPT

The same MCP server works across assistants, so you build once and connect many. Claude Desktop reads a local config that points at your server command for stdio, or a URL for a remote server. ChatGPT consumes MCP through its Apps SDK and requires an HTTPS endpoint registered as a connector in developer mode. Building to the protocol, rather than to one assistant, is what makes the server reusable across Claude, ChatGPT, and others.

Taking your MCP server to production

A production MCP server needs more than a local script. Host it behind HTTPS on a platform such as Cloudflare Workers or Google Cloud Run, containerize it for repeatable deploys, and add rate limiting because AI clients can call tools in tight loops. Add logging and error handling per tool so a single failure does not break the conversation. The jump from a local demo to a hosted service is where most of the real work is.

This is the line item to plan for. A weekend prototype that answers questions in Claude Desktop is encouraging, and it is also perhaps a fifth of the way to something customers can touch. The remaining work is the standard discipline of any service that faces the public: reliable hosting, encryption in transit, sensible limits so an automated client cannot hammer your systems, and monitoring so you know when a tool starts failing. Budgeting for that gap up front is the difference between a demo that impresses leadership once and a service that earns its place in the customer experience.

On timeline, a narrow first server, a few read-only tools against a clean catalog, is a matter of weeks rather than months for a capable team. What stretches a timeline is usually messy product data that has to be cleaned before a tool can return it reliably, approval cycles for anything that touches orders or payments, and the security review a customer-facing endpoint deserves. 

Securing your MCP server

Any MCP server that touches customer data needs authentication. The MCP spec treats remote servers as OAuth resource servers and calls for OAuth 2.1 with PKCE and Resource Indicators, while local development can use API keys or environment variables. Validate every input, because the assistant will send unexpected calls, and watch your dependencies: a 2025 vulnerability in the popular mcp-remote package showed how a weak link exposes the whole chain.

Security is the section the generic tutorials skip, and it is the one a decision-maker should ask about first. An MCP server is a door into your live systems, opened to an assistant acting on a customer’s words. That door needs the same rigor as any other public endpoint: proper authentication on anything that reaches customer or order data, validation on every request, and current dependencies. The mcp-remote vulnerability, disclosed in 2025 and rated critical, affected a package with hundreds of thousands of downloads and is a useful reminder that the protocol being standardized does not make every tool around it safe. This is also where the PPC audit agent and other production builds taught us to treat the connector as carefully as the systems behind it.

Should you build your own MCP server or use a prebuilt one?

Build your own MCP server when your systems or logic are specific enough that no prebuilt server fits, which is common for custom stacks and bespoke workflows. Use a prebuilt server, such as Shopify’s official Storefront or Catalog MCP servers, when you are on a supported platform and need standard commerce capabilities. Use a managed MCP platform when you want hosting, auth, and monitoring handled and prefer to focus only on your tools.

It helps to know the prebuilt options are real and growing. Shopify, for example, ships official MCP servers: a Storefront server that exposes a single store’s catalog, cart, and policies, a Catalog server for cross-merchant product discovery, and a Customer Accounts server for order tracking and returns. If you are on a platform like that and need standard commerce capabilities, a prebuilt server can put you live quickly with far less to maintain. You build your own when your data or logic is specific enough that no prebuilt server fits.

Build your own if:

• Your data or actions are specific to your stack and no prebuilt server exposes them
• You need fine control over which tools and resources are exposed, and how
• You have engineering capacity to maintain it as the spec and your systems change
• You need custom authentication or compliance rules a generic server will not enforce.

Use a prebuilt server if:

• You are on a platform that ships official MCP servers, such as Shopify
• You need standard capabilities, catalog, cart, order status, rather than bespoke logic
• You want to be live quickly with less to maintain.

Use a managed MCP platform if:

• You want hosting, HTTPS, and authentication handled for you
• You prefer to write only the tools and leave the infrastructure to the platform
• You are testing demand before investing in your own deployment.

For many stores, the right answer is a mix of a prebuilt server for the standard parts and a custom one for what makes your business specific. If you are deciding what to build versus buy, our team can scope an MCP server against your stack using the same approach behind the MCP servers we run in production.

Frequently asked questions about building an MCP server

How do I build an MCP server?

To build an MCP server, pick an official SDK such as Python or TypeScript, define a server, register the tools and resources you want to expose, choose a transport (stdio for local, Streamable HTTP for remote), then connect a client like Claude or ChatGPT and test. Most of the work is wiring your own data and actions to the tools, since the SDK handles the protocol itself.

What is the difference between an MCP server and an MCP client?

An MCP server exposes capabilities, your tools, data, and prompts, while an MCP client is the application that uses them, such as Claude Desktop, ChatGPT, or Cursor. The client connects to the server and decides when to call its tools. When you build an MCP server, you build the side that holds your systems.

What are tools, resources, and prompts in MCP?

They are the three capabilities an MCP server can expose. Tools are functions the assistant calls to take an action, like searching products. Resources are read-only data the assistant can read, like a product record. Prompts are reusable templates that guide a task. A typical commerce server uses mostly tools and resources.

What language should I use to build an MCP server?

Use whichever official SDK fits your stack. The tier-one SDKs are TypeScript, Python, C#, and Go, with more languages supported at lower tiers. Python with FastMCP is the fastest path for many teams, and TypeScript is common when the server lives alongside a web codebase. The protocol is the same across all of them.

What is the difference between stdio and Streamable HTTP?

They are the two transports an MCP server uses. Stdio runs the server as a local process on the same machine as the client, which suits developer tools. Streamable HTTP exposes the server over the network for remote and customer-facing use. The older HTTP and SSE transport is deprecated, so new servers should use Streamable HTTP for remote access.

How do I connect an MCP server to ChatGPT?

ChatGPT consumes MCP servers through its Apps SDK. You host your server behind an HTTPS endpoint, then register that endpoint as a connector in ChatGPT developer mode. During development you can expose a local server over HTTPS with a tunneling tool. The same server also works with Claude and other MCP clients without changes.

How do I secure an MCP server?

Any MCP server that touches customer data needs authentication. The spec treats remote servers as OAuth resource servers and calls for OAuth 2.1 with PKCE, while local development can use API keys or environment variables. Validate every input the assistant sends, and keep dependencies patched, since a 2025 vulnerability in a popular MCP connector package showed how one weak link exposes the chain.

Do I need to build my own MCP server?

Not always. If you are on a platform that ships official MCP servers, such as Shopify, a prebuilt server may cover standard commerce needs. Build your own when your data or logic is specific enough that no prebuilt server fits, or when you need control over exactly which tools and resources are exposed and how they are secured.

If you are weighing whether to build your own MCP server or connect a prebuilt one, talk to our team and we will map it to your catalog, your assistants, and your security needs before your engineers write much code.

The post How to Build Your Own MCP Server appeared first on scandiweb.

If your team keeps forwarding you headlines about shopping inside ChatGPT and asking whether the brand needs to do something about it, this guide gives you the answer and the reasoning behind it. 

ChatGPT apps for eCommerce are third-party services that run inside ChatGPT, so a shopper can find products and act on them without leaving the conversation. They are built with OpenAI’s Apps SDK, launched in October 2025, and they are a different thing from the ChatGPT chatbot, from custom GPTs, and from the older plugins.

The reason this matters now, rather than next year, is timing. ChatGPT reached around 800 million weekly active users by late 2025, and OpenAI has spent the months since turning it into a place where people find and buy things, not only a place where they write. Most of the content ranking for this topic is either a product page from OpenAI or a vendor pitch telling you to build a custom app today. Our guide explains what a ChatGPT app is, how it works, what changed through 2026, and how to decide whether your store should build one, prepare for it, or wait.

We write this from a specific vantage point. scandiweb has put apps built on the same underlying standard into production, including a content agent and a PPC audit agent, so the guidance here comes from systems we build and maintain ourselves.

What a ChatGPT app is

ChatGPT apps are third-party services that run inside ChatGPT, so people can use them in a normal conversation instead of leaving for a separate site. In eCommerce, that means a shopper can browse your catalog, get a recommendation, and in some cases complete a purchase without opening a browser tab. Apps are built with OpenAI’s Apps SDK, launched in October 2025, and they differ from the ChatGPT chatbot, custom GPTs, and older plugins.

The distinction matters because the word “app” is doing a lot of work here. This is not your mobile app reskinned for ChatGPT, and it is not a marketing chatbot bolted onto your storefront. A ChatGPT app is a connected service that ChatGPT can call mid-conversation, render inside the chat with its own interface, and use to take real actions against your live systems. When a shopper asks ChatGPT to find a winter coat under a set budget, an app can return your actual products, in stock, with a way to act on them right there.

Three properties make a ChatGPT app different from anything merchants have plugged into before:

• It runs inside the conversation, so the shopper never context-switches to a new tab or site.
• It reads live data from your systems, so prices, stock, and variants are current rather than scraped or stale.
• It can take actions, from adding to a cart to starting an order, depending on how far you build it.

For a merchant, the practical model is straightforward. ChatGPT is the place the customer already is. The app is how your store shows up there with real data and real functionality, rather than as a static link the model might happen to mention.

ChatGPT apps vs GPTs, plugins, and Instant Checkout

A ChatGPT app is built on the Apps SDK and connects ChatGPT to your live systems through an MCP server, with an interactive interface inside the chat. A custom GPT is a configured version of ChatGPT with instructions and files, not a connected service. Plugins and connectors were the earlier integration model. Instant Checkout is a separate payment feature that lets shoppers buy from supported merchants in chat without a full app.

This is the single most common point of confusion, so it is worth a clear comparison. Merchants are being sold “ChatGPT apps” by some vendors and “custom GPTs” by others, and the two solve different problems at very different cost.

A custom GPT is a quick experiment a marketing team can stand up in an afternoon. It can hold your tone of voice and a few documents, but it does not see your live catalog or take orders. A ChatGPT app is a connected channel that does. Instant Checkout works alongside both as a payment path you can switch on through a supported platform, even before you commit to building an app of your own.

The reason this confusion is expensive: a vendor who pitches “a custom ChatGPT app” for the price of an app build, but delivers a custom GPT, has sold you a brochure when you needed a storefront. Knowing which of the four you actually need is the first real decision.

How ChatGPT apps work, the Apps SDK, and MCP

A ChatGPT app has three parts: tools that let ChatGPT take actions, structured data the model can read, and widgets that render an interface inside the chat. Underneath, the Apps SDK runs on the Model Context Protocol, the open standard that lets ChatGPT connect to external tools and data. For a store, that means your catalog, inventory, and order systems can be reached through one MCP server that the app talks to.

If that last part sounds familiar, it should. The Model Context Protocol is the same standard we covered in depth in our guide to MCP. The Apps SDK builds directly on it, which is why a ChatGPT app is, underneath, an MCP server with an app layer on top. MCP is the plumbing that lets an assistant reach external tools and data in a consistent way. The Apps SDK is what OpenAI layered on top so those connections can run, and render, inside ChatGPT. 

Walk it through in the order a request travels:

1. A shopper asks ChatGPT something your app can answer, such as a product search or an availability check.
2. ChatGPT calls one of your tools, the named actions you have exposed, like search_products or check_stock.
3. Your MCP server receives that call, queries your live commerce systems, and returns structured data the model can read.
4. A widget renders the result inside the chat, a product grid or a configuration panel, so the shopper can act without leaving.

The engineering reality comes down to one component you have to own and maintain: an MCP server that exposes your commerce systems in a way ChatGPT can use safely. The widgets and conversation flow are on top of that server. This is why the build question is really an infrastructure question, and why stores with clean, well-structured catalog and inventory data have a real head start. A store whose product data is scattered across spreadsheets and a tired PIM will spend most of the project getting that data fit to expose, before a single widget is drawn.

Why ChatGPT apps matter for eCommerce

ChatGPT reached around 800 million weekly active users by late 2025, which makes it a discovery surface, not only a writing tool. As shopping shifts toward agentic commerce, where an assistant helps choose and buy, brands that are reachable inside ChatGPT can be recommended at the moment of intent. For eCommerce, the value is presence in the conversation where a purchase decision is forming, rather than waiting for a separate site visit.

This is a meaningful change in where discovery happens. For two decades, the contest was for position in a list of blue links. In an assistant-led model, the contest is for being the answer the assistant reaches for when a customer describes what they want. That is the heart of agentic commerce – the assistant does more of the searching and comparing, and sometimes the buying, on the customer’s behalf.

There are two implications worth considering. A customer who completes a purchase inside ChatGPT may never touch your site, which changes how you think about owned traffic, analytics, and the first-party data you have relied on. And the brands that are structured to be reachable, with clean feeds and a working app, are the ones in the consideration set at all. The brands that are not structured for it simply will not appear, the same way a store with no SEO never showed up in organic search.

That second point is the one to take to leadership. This is less about a new marketing channel and more about whether your products are legible to the systems that are starting to mediate demand. A brand can have the better product and still lose the recommendation to a competitor whose data an assistant can actually read and act on.

Shopping and Instant Checkout inside ChatGPT

Customers can already buy inside ChatGPT in some cases. OpenAI’s Instant Checkout, announced in September 2025 and built with Stripe, lets US shoppers purchase from supported merchants in the chat, starting with Etsy sellers and expanding toward Shopify merchants. It runs on the Agentic Commerce Protocol, an open standard that handles how a merchant’s products, promotions, and orders move through the conversation.

Instant Checkout is the lower-effort entry point. You do not have to build a full app to be purchasable, provided your platform supports the integration. The difference is between being buyable inside ChatGPT and owning a richer, branded experience there. Many stores will start by being buyable through Instant Checkout while they weigh the decision about a full app.

How to get your products discovered in ChatGPT

Being buildable is not the same as being found. Before a ChatGPT app or Instant Checkout can sell anything, your products have to be legible to the assistant, which comes down to the quality and structure of your product data. This is the work that almost every store can start now, regardless of whether they ever build an app.

A few things carry most of the weight:

• Clean, structured product feeds. Titles, attributes, variants, availability, and pricing that are accurate and machine-readable. If you sell through Shopify, the Shopify Catalog route is one way merchant products reach ChatGPT users.
• Rich attributes. Material, fit, compatibility, use case. An assistant matching “a waterproof jacket for commuting” needs the attributes that let it match, not only a product title.
• Consistent identifiers. GTINs, SKUs, and category mappings that line up across your systems, so the same product is not described three different ways.

This is familiar territory for any team that has done serious product information management, and it is why a clean PIM and a disciplined feed strategy pay off twice. Once for traditional channels, and again for assistant-led discovery.

What changed in 2026, the shift to brand-owned apps

Through early 2026, OpenAI shifted its commerce focus away from checkout inside the chat and toward brand-owned ChatGPT apps, giving merchants more control over the buying experience. Around 100 companies had apps by then, Instacart integrated in December 2025, and Walmart launched a standalone app. The signal is that owning your own app, rather than relying only on in-chat checkout, is becoming the more durable path.

This is the part that most ranking content has not caught up with. The early story, in late 2025, was “buy directly in ChatGPT.” The 2026 story is more layered: OpenAI appears to be steering high-intent commerce toward apps the brand controls, where the merchant owns more of the experience, the data, and the customer relationship. The App Directory, which opened to third-party submissions in December 2025, is the front door for that model, and the early movers are large retailers who can see where this is heading.

If you are setting a strategy, treat the direction of travel as the planning input, rather than any single week’s announcement. The platform is moving toward brand-owned apps as the serious commerce surface, with feed-based discovery and Instant Checkout as the lighter-weight ways to participate in the meantime. A merchant who plans only around in-chat checkout is planning around the version OpenAI is already moving past.

How to build a ChatGPT app for your store

Building a ChatGPT app requires a paid OpenAI plan with developer mode enabled, then an app made of tools, structured output, widgets, and an MCP server. Apps are submitted through OpenAI’s developer platform for the App Directory, which opened in December 2025. At launch, apps were unavailable to logged-in users in the EEA, Switzerland, and the UK, which matters for European brands planning rollout.

In practice, a build moves through a handful of stages:

1. Connect your systems through an MCP server. This is the foundational piece and usually the largest share of the work. The server exposes your catalog, inventory, and order operations as tools ChatGPT can call.
2. Define the tools. Decide what ChatGPT is allowed to do, such as search the catalog, check availability, or start an order, and set the guardrails on each.
3. Build the widgets. The interface that renders inside the chat, the product grid, the configurator, the order summary.
4. Submit to the App Directory. Package the required metadata, testing, and country availability, then submit through the developer platform for review.

The work resembles other production agent builds more than it resembles front-end web work. When scandiweb built the Claude Blog Factory and a PPC audit agent, the hard and valuable parts were the same: connecting the model to real systems through MCP, defining safe actions, and maintaining the connection as the platform changed underneath. A store-specific ChatGPT app follows that shape, with your commerce stack in place of the marketing systems.

On cost, there is no single number, and any vendor quoting one without seeing your data is guessing. The real drivers are the state of your product data, the number and complexity of the actions you want to support, and whether you build in-house or with a partner. The ongoing cost is maintenance: an app that talks to your live systems needs care as both your stack and OpenAI’s platform evolve.

One caution for European merchants! Regional availability has lagged, and at launch, apps were not available to logged-in users in the European Economic Area, Switzerland, and the UK. If those are your primary markets, confirm current support before committing to a build timeline, and use the wait to get your product feeds in order so you are ready when availability widens.

Should your store build a ChatGPT app now?

For most stores, the honest answer in 2026 is to prepare, not rush. Building a full ChatGPT app makes sense when you have the engineering capacity and a clear in-chat use case. Preparing your product feeds and data makes sense for almost everyone because it is the low-cost way to be discoverable. Waiting is reasonable if you sell mainly in the EEA or UK, where availability still lags.

Build a ChatGPT app now if:

• You have in-house or partner engineering capacity to maintain an MCP server and app
• You have a clear, repeatable in-chat use case such as reorder, configure, or check availability
• Your buyers are US-based, where the commerce features are live
• You can commit to maintaining the app as OpenAI’s platform changes
• You want a defensible early position before competitors fill the directory.

Prepare your feeds and data instead if:

• You want ChatGPT discoverability without owning an app yet
• Your catalog and inventory data are not yet clean or well-structured
• You sell through Shopify and can use the Shopify Catalog route
• You want to test demand before committing to an engineering budget.

Wait if:

• Your primary market is the EEA, Switzerland, or the UK
• You have no engineering capacity and no development partner
• Your category sees little assistant-driven discovery today.

If you are weighing where your store falls, our team can run a readiness assessment using the same MCP stack we have already put into production, so the decision rests on your catalog, your markets, and your capacity rather than on a vendor’s timeline.

Frequently asked questions about ChatGPT apps for eCommerce

What are ChatGPT apps for eCommerce?

ChatGPT apps for eCommerce are third-party services that run inside ChatGPT, letting shoppers discover products, ask questions, and sometimes buy without leaving the conversation. They are built with OpenAI’s Apps SDK, which connects ChatGPT to a store’s catalog and systems through an MCP server. They differ from the ChatGPT chatbot, from custom GPTs, and from the separate Instant Checkout payment feature.

What apps are available in ChatGPT?

Early ChatGPT apps include Booking.com, Canva, Coursera, Expedia, Figma, Spotify, and Zillow, with Uber, DoorDash, Instacart, OpenTable, Target, and Tripadvisor following. After the App Directory opened in December 2025, third-party developers began submitting their own. The mix is growing quickly, and a notable share are shopping-oriented, including major retailers.

How do I use apps in ChatGPT?

In a supported ChatGPT plan, you can call an app by name in a conversation, or ChatGPT may suggest one when it fits your request. The app then runs inside the chat, showing an interface where you can browse, configure, or act without opening a separate site. Availability depends on your plan and your region.

Is the Apps SDK the same as MCP?

No, but they are closely linked. MCP, the Model Context Protocol, is the open standard that lets ChatGPT connect to external tools and data. The Apps SDK builds on MCP and adds the parts needed to run an app inside ChatGPT, such as interactive widgets. In practice, building a ChatGPT app means running an MCP server plus the app layer on top.

Can customers buy products directly in ChatGPT?

In some cases, yes. OpenAI’s Instant Checkout, announced in September 2025 and built with Stripe, lets US shoppers buy from supported merchants inside the chat, starting with Etsy sellers and expanding to Shopify merchants. Through 2026, OpenAI also shifted toward brand-owned apps, where the merchant has more control over the purchase experience.

How much does it cost to build a ChatGPT app?

There is no fixed price. Cost depends on the complexity of your use case, the state of your catalog and inventory data, and whether you build in-house or with a partner. The main ongoing investment is engineering time to build and maintain an MCP server connected to your commerce systems, plus a paid OpenAI plan with developer mode enabled.

Are ChatGPT apps available in the EU and UK?

At launch, ChatGPT apps were unavailable to logged-in users in the European Economic Area, Switzerland, and the UK, with availability rolling out over time. European brands should confirm current regional support before planning a launch, and may prioritize preparing product feeds while waiting for fuller availability.

Should an eCommerce brand build a ChatGPT app or use Instant Checkout?

For most brands in 2026, the practical path is to prepare product feeds and data first, then decide between a full app and Instant Checkout based on use case and market. Build an app when you have engineering capacity and a clear in-chat use case. Rely on Instant Checkout or feed-based discovery when you want presence without maintaining an app.

Where ChatGPT apps fit in your commerce roadmap

ChatGPT apps for eCommerce have moved from a developer demo to a discovery and buying surface that 800 million weekly users already touch. The sensible first move for most merchants is getting your catalog and data clean enough to be discoverable, then choosing between a brand-owned app and Instant Checkout based on your markets and your engineering capacity. The brands that prepare now will be in the consideration set when assistant-led shopping becomes routine. The brands that wait for full certainty will be wondering why a competitor got there first.

If you are trying to work out whether a ChatGPT app belongs on your roadmap this year or next, talk to our team and we will map it to your catalog, your markets, and the MCP stack we already run in production.

The post What Are ChatGPT Apps for eCommerce? A Merchant’s Guide appeared first on scandiweb.

If you searched for Adobe Launch, here is the first thing to know: Adobe does not really call it that anymore. The product most people still know as Adobe Launch is now Tags, a capability inside Adobe Experience Platform Data Collection. The core idea has not changed – manage your tracking tags in one place without redeploying code every time – but the name, the home screen, and the scope have all moved on. This guide explains what Adobe Launch became, what it does today, and how to use it.

The category itself is still growing. The global tag management market was worth about $1.85 billion in 2025 and is projected to reach roughly $3.57 billion by 2030, a compound annual growth rate near 14% (Mordor Intelligence). Knowing the current shape of Adobe’s offering matters more than ever.

What is Adobe Launch, and what is it called now?

Adobe Launch is a tag management system – a central place to deploy and manage the tracking tags, scripts, and snippets that power analytics and marketing on a website or app. In July 2021, Adobe folded Launch into Adobe Experience Platform and renamed its parts. “Experience Platform Launch (Client Side)” became Tags, “Launch Server Side” became Event Forwarding, and “Edge configurations” became datastreams. The interface itself is now the Data Collection UI.

In practice, that means the standalone launch.adobe.com product has been migrated. You now access Tags through the Data Collection UI inside Adobe Experience Platform at experience.adobe.com. Tags is no longer a separate service bolted onto Adobe’s stack – it is a capability of the wider platform. The name “Adobe Launch” survives mostly in conversation and search, which is exactly why it pays to know what it maps to today.

What Adobe Data Collection includes

Tags

Tags is the client-side core, and it is built from a few moving parts. Rules define when something should fire. Data elements capture the values you want to collect, like a product price or page name. Extensions are pre-built or custom integrations that add functionality, and libraries bundle your configuration for deployment across environments (development, staging, and production). Together they let marketers add, change, or remove tags without leaning on a developer for every edit.

Event Forwarding (server-side)

Event Forwarding is the server-side counterpart, and it is the biggest addition since the Adobe Launch days. Instead of loading more vendor code in the browser, you collect data once and forward it server-side from Adobe’s Edge Network to any Adobe or non-Adobe destination, with low latency and no client-side implementation code. The payoff is a lighter, faster page and tighter control over where data goes – both a performance and a privacy win.

Web SDK, Edge Network, and datastreams

The modern collection path runs through the Web SDK (and Mobile SDK), a single library often referred to as alloy.js. It sends data to a datastream, which routes it through the Edge Network – Adobe’s globally distributed set of servers – and on to the right destinations. This replaces the older pattern of each Adobe solution shipping its own library, and it is the foundation that Event Forwarding builds on.

What you use Adobe Launch for

The practical job is collecting clean, reliable data and acting on it. A few common use cases:

• Analytics implementation – deploy and maintain Adobe Analytics tracking across a site without hard-coding it. See our guide to implementing Adobe Analytics.
• Marketing tags – manage advertising pixels, A/B testing tools, and third-party scripts from one interface.
• Personalization and testing – feed consistent data into tools like Adobe Target so experiences are based on accurate signals.
• Server-side collection – move data flows off the browser with Event Forwarding for speed and governance.

For eCommerce specifically, a clean data layer is what makes downstream reporting trustworthy – our guide on Adobe Analytics for eCommerce shows what that looks like in practice.

How tag management helps your site

Done well, tag management improves three things at once:

• Data accuracy – centralized rules and data elements reduce the broken or duplicate tracking that creeps in with manual code.
• Site performance – fewer client-side scripts, plus the option to forward events server-side, means less weight slowing the page down.
• Speed of change – marketers can adjust tracking in hours instead of waiting on a development release.

The result is faster pages, more reliable analytics, and a team that can adapt tracking as campaigns change.

Privacy, consent, and data governance

Consent handling is now built into the collection layer rather than bolted on. The Web SDK ingests consent signals from consent management platforms using Adobe standards and the IAB Transparency and Consent Framework (TCF) 2.0, so tools like OneTrust and Sourcepoint can drive what fires. A setConsent call propagates preferences into Adobe Experience Platform, and GDPR defaults to opt-out while CCPA defaults to opt-in. Moving collection server-side with Event Forwarding also keeps more of the data flow off the client, which helps with both compliance and control. If consent is a priority for your build, our guide to GDPR compliance covers the wider picture.

How Adobe Launch (Tags) works

Setup follows a consistent path. You create a property in the Data Collection UI and install its embed code on your site or app – this is the foundation that loads your tags. From there you build rules and add extensions.

Rules, data elements, and extensions

Rules set the conditions under which tags fire, giving you precise control over when and where. Data elements define the values you want to capture and reuse. Extensions add functionality, whether a pre-built integration with a third-party tool or custom code for your own collection needs. This is the day-to-day work of running Tags.

Debugging and testing

Before anything goes live, Adobe’s debugging and testing tools let you validate that tags fire correctly, inspect the data they send, and troubleshoot issues. The Adobe Experience Platform Debugger browser extension is the standard way to confirm data is flowing into Adobe Analytics before you publish. Verifying in a development library first is what keeps inaccurate data out of your reports.

Adobe Launch vs other tag managers

Adobe Launch (Tags) and Google Tag Manager solve the same problem, and the right choice usually follows your analytics stack. If you run Adobe Analytics, Target, and the wider Experience Cloud, Tags integrates natively and is the natural fit. Teams on Google Analytics tend to standardize on Google Tag Manager. The concepts transfer either way – if you work across both, our notes on JavaScript variables for GTM are a useful reference.

Frequently asked questions

Is Adobe Launch still called Adobe Launch?

No. Since July 2021, the client-side product is called Tags, part of Adobe Experience Platform Data Collection. “Adobe Launch” remains common in conversation and search, but it is not the current official name.

Where do I access Adobe Launch now?

Through the Data Collection UI inside Adobe Experience Platform at experience.adobe.com. The standalone launch.adobe.com product has been migrated into the platform.

Is Adobe Launch free?

There is no additional charge for Tags. It is included as a value-add capability for Adobe Experience Cloud customers.

What is Event Forwarding?

Event Forwarding is Adobe’s server-side data collection, formerly called Launch Server Side. It forwards event data from the Edge Network to Adobe and non-Adobe destinations without client-side code, improving page performance and data governance.

What is the difference between Tags and the Web SDK?

Tags manages what fires and when on the client side. The Web SDK (alloy.js) is the single library that sends data to a datastream and through the Edge Network. They work together in a modern Data Collection setup.

Still running on an older Adobe Launch setup, or unsure whether your tags and consent are configured correctly? scandiweb audits and migrates Adobe tag management and data collection so your analytics stay accurate and fast. Tell us about your current setup and we will review it with you – see our Adobe Launch service.

The post What Is Adobe Launch? Tag Management in Adobe Data Collection (2026) appeared first on scandiweb.

Most PPC advice in 2026 still assumes the 2023 playbook works. It does not. Google’s AI Overviews now sit above paid ads on a meaningful share of high-intent queries and have compressed paid click-through rates by roughly 68% on AIO-triggering searches. Performance Max is no longer optional. First-party data has replaced cookies as the bidding signal that decides who wins the auction. The accounts running 2023 strategies are losing money on the same budgets. The accounts running 2026 strategies are scaling on half the budget.

The scandiweb paid-search team manages PPC for eCommerce brands including BUFF, Puma, Macron, and Läderach. This guide is the framework we walk those clients through – where the auction has shifted, which platforms still convert, what 2026 spend allocations look like for a healthy mid-market store, and which structural decisions stop budgets from bleeding.

Overview

• Pay-per-click (PPC) advertising is a paid-media model where the advertiser pays a publisher (Google, Meta, Microsoft, Amazon, TikTok, LinkedIn) only when a user clicks their ad. You buy targeted visits, not impressions.
• In 2026, Google holds 48.5% of US search ad spending – the first time below 50% in more than 20 years (eMarketer, 2026). Meta is projected to surpass Google in total digital ad revenues for the first time at $243.46 billion vs $239.54 billion (eMarketer, 2026). The category is fragmenting fast.
• AI Overviews reshape the math. Paid CTR for queries with an AIO dropped from 19.70% to 6.34% between June 2024 and September 2025 (Search Engine Land, 2025). Brands cited inside the AI Overview earn 91% higher paid CTR than non-cited brands (Search Engine Land, Q3 2025) – brand authority and AEO eligibility now feed paid performance.

Understanding PPC advertising

PPC advertising, also known as PPC marketing, is a model where businesses display ads on search engines like Google and social networks like Meta, and only incur a cost when someone clicks the ad. As simple as the mechanic sounds, PPC has evolved into a precise, automated discipline in 2026 with sophisticated ad formats, audience-targeting options, and platform-specific bidding models. The platforms help businesses reach their audience, increase brand visibility, drive targeted traffic, generate leads, and measure campaign performance in near-real-time.

From keyword research, ad creation, and ongoing optimization to landing-page experience, tracking, and analytics, a successful PPC campaign involves many moving parts. The ads run on Google Ads, Meta Ads (Facebook and Instagram), Microsoft Advertising, Amazon Ads, TikTok Ads, and LinkedIn Ads. The right platform mix depends on intent, audience, and budget.

What is pay-per-click advertising?

Pay-per-click advertising is a paid-media model where the advertiser pays a publisher each time a user clicks an ad. The publisher (Google, Meta, Microsoft, Amazon, LinkedIn, TikTok) runs a continuous auction for ad placements, and the advertiser wins by combining a bid amount with ad quality. You are charged on click, not on impression.

The single mechanic sounds simple. In practice it sits under five layers of automation in 2026: smart bidding, audience signals, Performance Max asset mixing, broad-match query expansion, and ad-relevance scoring. The advertiser sets the goal (revenue, leads, demos, store visits), the platform decides who sees the ad, when, where, and at what bid.

Fact: Google US search ad revenue is forecast at $67.3 billion in 2026, with mobile search alone accounting for over $41 billion (eMarketer Q4 2025 forecast).

The evolution of PPC

PPC has changed substantially since its inception. The 2023-era playbook centered on manual bid management, exact-match keywords, and granular campaign segmentation. The 2026 playbook is fundamentally automated: Performance Max on Google, Advantage+ on Meta, broad-match plus smart bidding, and platform AI assembling ad creative from advertiser-supplied assets.

The three changes that matter most for eCommerce in 2026:

• AI Overviews compete with ads. On informational queries the AIO answer block now appears above the first text ad. Click-through on first-position search ads on AIO-active queries has dropped 14 to 22% YoY across the accounts we manage.
• First-party data is the new bidding signal. Google and Meta’s automated bidding leans on advertiser-supplied audience and conversion data. Accounts without enhanced conversions, GA4-linked customer-match lists, and server-side tagging are getting outbid systematically.
• Creative is the variable. With targeting flattened by privacy changes and broad-targeting pushes, creative iteration drives roughly 70% of performance variance on social. Brands without weekly creative refresh stagnate.

Key components of a successful PPC campaign

A successful PPC campaign is not set-and-forget. It involves planning, strategic execution, and constant monitoring. When establishing parameters for a campaign, align it with the overall business objective, define clear outcomes, and allocate a budget that can produce statistically meaningful learning within 30 to 60 days.

The primary components: a well-researched keyword set (or audience set on social), bid management aligned to ROAS goals, an ad group structure that matches the buyer journey, and a negative-keyword list that grows weekly. Each component compounds, weakness in any single one degrades the others.

Keyword research

Keyword research is the foundation of any search PPC campaign. It is how you decide which auctions you enter and which you exclude. The output is a keyword set with high commercial intent (queries that include buy, best, compare, brand + product), strong long-tail variants, and an aggressive negative-keyword list (free, salary, jobs, lyrics, wikipedia, competitor brand names if you are not running competitor campaigns).

Match types still matter in 2026. Exact match catches volume that gets lost in broad, broad match plus smart bidding catches conversions exact match misses. Most accounts work best with a mix. Pull search-query reports weekly, promote good queries to exact match, and add new negatives every cycle.

Ad creation and optimization

Creating and optimizing PPC ads in 2026 involves giving the platform the best raw material to assemble from. For Google Responsive Search Ads: 15 unique headlines per ad group, each with a distinct angle (feature, benefit, price, social proof, urgency, brand), four description lines with a clear CTA verb, and every applicable ad extension (sitelinks, callouts, structured snippets, lead-form, image, location, price).

For Meta and TikTok: pair static and Reel / video for every campaign, test three to five creative variants per week per audience, lead with the hook in the first 1.5 seconds (video) or first five words (static). Read our Meta Collection Ads case study for the creative pattern that drives +62% ROAS and +35% AOV in retail accounts.

Landing page optimization

The landing page is where most accounts leak money. A landing page must do one thing.

• Match the ad exactly – headline, offer, image. Mismatch kills both conversion and Quality Score.
• Mobile-first design – 70%+ of ad clicks in 2026 are mobile.
• One CTA, repeated three times – above the fold, mid-page, end.
• Load in under 2.5 seconds on a median 4G connection (Core Web Vitals LCP).
• Remove the navigation if you can – every escape route is a lost conversion.

We tripled ROAS while reducing CPC on a mid-market client by rebuilding the landing page and switching the bidding strategy.

Tracking and analytics

If you cannot measure it, you cannot scale it. The 2026 baseline for any PPC account:

• GA4 with enhanced eCommerce events
• GA4 linked to Google Ads, conversions marked in GA4, not just in Ads
• Server-side tagging where possible – third-party cookies are no longer reliable
• Enhanced conversions sent to Google and Meta with hashed first-party data
• A closed-loop revenue view: ad spend → click → on-site action → order → returned / refunded → net revenue. Optimize on net revenue, not gross.

For what happens when the tracking layer is wired properly, read our margin tracking case showing +30% conversions via Google and Bing Ads.

Popular PPC platforms and their features

The four PPC platforms that still convert reliably for eCommerce in 2026 are Google Ads (high-intent demand capture), Meta Ads (demand generation and retargeting), Microsoft Advertising (B2B and low-competition CPC), and Amazon Ads (retail SKU search). Most others have specific use cases but should not anchor a budget.

CPCs reflect a blend of scandiweb-managed account medians and WordStream 2026 benchmarks.

Google Ads

Google Ads, the largest and most familiar PPC platform, offers the widest range of ad formats and inventory in 2026. You can run Search ads on Google, Shopping ads on Google Shopping, video ads on YouTube, Gmail ads, and Display ads on the Google Display Network. The audience-targeting options (uploaded customer lists, In-Market Audiences, Layered Audiences, Predictive Audiences) pair with Performance Max to give the bidding model rich first-party signal.

Three Google-specific changes that matter in 2026: Performance Max is no longer optional for retail (standard Shopping campaigns have been retired for most verticals), AI Overviews compete with ads on informational queries (shift budget toward bottom-funnel queries where AIO is less likely to trigger), and the bidding model is hungry for first-party conversion data (without enhanced conversions you are systematically outbid).

Microsoft Advertising (Bing Ads)

Microsoft Advertising – formerly Bing Ads – is an effective PPC platform that often delivers higher ROI than Google Ads on B2B and higher-income demographics, simply because the competition is lower. Microsoft Advertising reaches 137 million unique desktop searchers on the Bing Network in 2026 and now layers LinkedIn-profile targeting onto search keywords, which is the unique advantage no other ad platform offers.

For B2B retailers and high-AOV brands targeting older and higher-income audiences, allocating 5 to 10% of paid budget to Microsoft Advertising routinely produces a lower CAC than the equivalent Google spend.

Social media PPC platforms

The rise of social media has opened a new path for PPC. Meta (Facebook and Instagram), TikTok, and LinkedIn offer audience-driven targeting based on interests, behaviors, and demographics. These platforms support image, video, and text creative – and the algorithms favor mixed-asset campaigns over single-format ones.

Meta in 2026 has shifted to Advantage+ Shopping as the default for eCommerce – a single campaign type that combines acquisition, retargeting, and product feeds with minimal manual segmentation. With targeting flattened by privacy changes, creative iteration drives roughly 70% of performance variance. Read our Meta Ads restructure case showing +111% revenue growth for the structural pattern.

Strategies for effective PPC management

PPC management in 2026 is about strategic planning, continuous optimization, and disciplined budget allocation. Whether budget allocation and bid management, audience targeting, or creative iteration – each strategy compounds the others.

Budget allocation and bid management

Budget allocation is where most accounts lose efficiency before any creative work begins. The rule we apply across managed accounts: allocate to the channel and campaign that is closest to net revenue, not gross. Most healthy eCommerce brands above $5M GMV run at least three platforms in parallel and let each platform’s strength compensate for the others’ weaknesses. A starting allocation for a mid-market eCommerce brand spending $25,000/month across paid media:

For the bid-management discipline that pairs with this allocation, read our bid management guide.

Targeting and audience segmentation

Audience segmentation is what gives a campaign the precision to convert. Identify and segment specific groups most likely to respond to a product or offer, concentrate budget on those segments, and let the platform’s bidding model optimize within each. Privacy changes have flattened raw demographic targeting, first-party customer-match lists, behavioral segments built from your own GA4 data, and lookalikes from your highest-LTV customers are now the levers that move the needle.

A worked example: scandiweb’s +224% revenue peak season restructure for a mid-market retailer was built around audience segmentation, not bid tweaks.

A/B testing and continuous optimization

A/B testing on PPC in 2026 is mostly creative testing. Targeting tests have lower ceilings than they used to because the platform algorithms move the audience for you, the variable you control is the creative.

The continuous-optimization discipline that pays in 2026: review search-query reports weekly (add negatives, promote good queries to exact match), run one structural test per month (bidding strategy, audience build, creative concept), pause anything below 0.5× target ROAS at 30 days, reallocate budget to the top decile of campaigns and cut the bottom decile. AI-powered strategies compound this – read the AI-powered PPC case showing 56 ROAS and 2x higher revenue.

Measuring PPC success: key metrics and KPIs

Running a PPC campaign is only half the work. Measuring its outcomes is the other half. The five metrics that predict commercial outcomes are click-through rate, cost-per-click, conversion rate, return on ad spend, and customer acquisition cost. Vanity metrics (impressions, reach, raw click counts) explain almost nothing about whether the spend is working.

Click-through rate (CTR)

Click-through rate is the number of clicks an ad receives relative to the number of impressions it serves. A 5% CTR means 5 out of every 100 viewers clicked. Higher CTR indicates the ad is relevant to the searcher and effectively capturing attention – and Google rewards it with a higher Quality Score, which lowers CPC over time. The way to lift CTR is to refine ad copy, target a tighter audience, and A/B test ad variations.

2026 eCommerce benchmark: Google Search 4 to 8%, Google Shopping 0.8 to 1.6%, Meta 0.9 to 1.8%.

Cost per click (CPC)

Cost per click is what you pay for each click on the ad. Total cost divided by total clicks gives you the average CPC. The metric pairs with conversion rate and AOV to show whether the auction is cost-effective for your business – a $5 CPC supports a $50 AOV at modest conversion, the same CPC kills a $20 AOV product.

The 2026 average Google Ads CPC across 23 industries is $5.42 (WordStream 2026 benchmarks). It varies wildly by vertical: Legal $8.58, Dentists $7.85, Home Improvement $7.85, Education $6.23, eCommerce median $1.50 to $4.00 search and $0.40 to $1.80 Shopping, Travel $2.12, Restaurants $2.05, Arts and Entertainment $1.60. The cheapest verticals are not always the most profitable – match aggressiveness to margin.

Conversion rate

Conversion rate is the percentage of users who complete a desired action (purchase, sign-up, demo booking) after clicking the ad. Lifting it means optimizing every element of the campaign – ad copy, targeting, landing page, call-to-action. Continual refinement is how mid-market accounts move from 2% to 4%+ on eCommerce, which doubles ROAS without spending an extra dollar on media.

2026 benchmark: eCommerce 2.4 to 4.0%, B2B lead-gen 5 to 8%.

Return on ad spend (ROAS)

Return on ad spend measures revenue generated relative to ad spend – revenue divided by spend. ROAS is the metric most worth optimizing toward, because it directly answers whether the campaign is profitable. A campaign at 4× ROAS at scale beats a 6× ROAS campaign at half the volume in absolute dollars.

2026 benchmark: 2.5 to 4× at scale for eCommerce, >4× for retargeting, varies wildly by margin. Optimize to ROAS and CAC, not to CTR or CPC – a campaign with 1% CTR and 4× ROAS beats a campaign with 8% CTR and 1× ROAS every time.

Common PPC challenges and how to overcome them

Like any marketing strategy, PPC presents its own set of challenges in 2026: high competition and rising CPCs, ad fatigue and banner blindness, and limited budgets and resources. With the right strategies and tools each is solvable.

High competition and rising CPCs

Rising CPCs indicate heightened competition for ad space and a higher cost-per-click. It can produce increased costs, difficulty achieving a positive ROI, and the need to compete with brands deeper-pocketed than yours. The fix:

• Audit Quality Score on your top 20 spend keywords. Anything under 6 needs a new ad group.
• Tighten match types – broad match without smart bidding loses money fast.
• Test dayparting – pull spend out of low-converting hours.
• Shift some budget toward bottom-funnel queries where AI Overviews are less likely to compress paid CTR.

Read our Kanuk winter sales case for a structural reset that cut PPC costs in half without sacrificing volume.

Ad fatigue and banner blindness

Ad fatigue happens when viewers become desensitized to an ad through frequent repetition. Banner blindness is the broader phenomenon of viewers tuning out display ads entirely. Counter both with creative variation – short-form video, Reels, Stories, dynamic product feed ads – and audience-frequency caps. Test new creative every two to three weeks on social and every six to eight weeks on search.

Limited budgets and resources

Limited budgets force discipline. Set realistic ROAS targets, define a single channel and campaign you will optimize first, and only expand after that channel hits target. The accounts we see succeed on tight budgets focus on one platform deeply rather than spreading thin across five.

A worked example: scandiweb’s 632% CTR climb restructure for a translation services provider produced the result on a constrained budget – discipline beats spend.

How AI Overviews affect PPC in 2026

AI Overviews compress paid CTR on the queries where they trigger – roughly a 68% drop between June 2024 and September 2025 (Search Engine Land, 2025) – but they also reward brand authority. When a brand is cited in the AIO, paid CTR is 91% higher than when the brand is not cited (Search Engine Land, Q3 2025). Paid and organic strategy can no longer be run as separate disciplines.

In practical terms across the accounts we run:

• Shift budget toward bottom-funnel queries (buy, price, brand + product, near me) where AIO triggers less often.
• Invest in AEO (answer engine optimization) on the organic side so the brand earns AIO citations – the citation lift compounds the paid side.
• See our AEO services for the structural changes that earn AIO citations.

What scandiweb’s paid-search team has learned running PPC for eCommerce since 2009

A few patterns are worth flagging across the accounts we manage:

• Account structure beats tactics. A clean, intent-segmented account routinely outperforms a clever-bidding-strategy account by 25 to 40% on ROAS.
• Feed quality is the highest-ROI lever for retail. Title formatting, custom labels, GTIN coverage, image cleanliness – these move Shopping performance more than any bidding change.
• The 30-day rule. Most “this is not working” calls happen too early. Hold structural changes until day 30 unless a campaign is bleeding budget below 0.5× target.
• AI assistance is not optional. Performance Max and Advantage+ Shopping outperform manual campaigns at scale in 2026 – provided you feed them good assets and clean conversion data.
• Margin tracking changes the picture. Optimizing on net margin instead of gross revenue reveals which products are profitable to scale and which are quietly losing money even at high ROAS.

scandiweb runs paid search for eCommerce brands across fashion, lifestyle, retail, and home goods. Across the case studies linked throughout this guide we have moved client accounts to +224% revenue during peak season, +731% revenue through targeted ads, 320% more leads at lower cost, 90K new users via multichannel, and 50%+ cost reductions while maintaining volume.

Frequently asked questions

What is pay-per-click advertising?

Pay-per-click (PPC) advertising is a paid-media model where the advertiser pays a publisher only when a user clicks one of their ads. Instead of buying impressions, you buy targeted visits to a website or landing page.

How does PPC work?

A user runs a query or matches an audience signal. The platform runs a real-time auction among advertisers bidding on related keywords or audiences. Ads are ranked by bid × Quality Score, the winner is displayed, and the advertiser is charged only when the ad is clicked, at the minimum price required to outrank the next bidder.

What does PPC cost in 2026?

For eCommerce, expect $1.50 to $6 CPC on Google Search, $0.40 to $1.80 on Google Shopping, $0.60 to $2.20 on Meta, and $0.90 to $2.50 on Amazon Sponsored Products. The 2026 cross-industry average is $5.42 per WordStream’s 2026 benchmarks. Realistic monthly minimums to learn at scale are $1,500 on Google Ads, $1,000 on Meta, $500 on Microsoft, $1,000 on Amazon.

Is PPC harder than SEO?

PPC produces faster results and is easier to start. SEO is slower and more competitive in 2026 because of AI Overviews, but compounds over years. PPC stops when the budget stops. SEO is harder to learn well but cheaper at scale once you rank.

Which is better, CPM or CPC?

CPC (cost-per-click) is better for direct-response goals – you pay only for clicks, which keeps the model closer to revenue. CPM (cost-per-thousand-impressions) is better for awareness – you reach a large audience at a lower unit cost but pay for views, not actions. Most eCommerce campaigns run on CPC, brand campaigns and Reels-style creative often run on CPM.

What is the 3-3-3 rule in marketing?

The 3-3-3 rule is a creative-testing heuristic: test three hooks, on three audiences, for three days each. It is a quick framework for evaluating creative without spending big – useful on Meta and TikTok where creative variance drives most performance.

How long does PPC take to work?

A new Google Ads account typically needs four to six weeks of learning before bidding stabilizes. Meta needs two to four weeks per campaign. The first 30 days are for reading data, not for big structural changes.

Can you do PPC without an agency?

Yes – Google Ads, Meta Ads, and Microsoft Advertising have setup wizards that get a small business to a first campaign in under an hour. Whether the campaign will be efficient is the harder question. Below ~$3,000/month most brands self-manage, above $10,000/month an agency or in-house specialist usually pays for itself in saved waste.

Should I bid on my own brand keywords?

Almost always yes. Brand keywords have very low CPC, very high conversion rates, and serve as defense – if you do not bid, competitors will. The only exception is when you already own 95%+ of the SERP for your brand and no competitor is bidding on it.

What is an example of a PPC ad?

A search ad on Google for running shoes for flat feet that links to a retailer’s flat-foot category page is the most familiar example. A Sponsored Product on Amazon for the same query is another. A Reel on Instagram with a “Shop now” sticker linking to a product page is a third – same model, different surface.

About this guide

This guide is maintained by scandiweb’s paid-search team, drawing on the eCommerce accounts we manage today, eMarketer’s US Digital Ad Spending and US Search Advertising Forecast 2026, WordStream’s 2026 Google Ads Benchmarks, and Search Engine Land’s AI Overview impact data.

Reviewed by scandiweb’s Paid Media lead.

Related reading from the scandiweb blog:

• Bid management in PPC campaigns
• +224% revenue peak season case
• Tripled ROAS with lower CPC
• AI-powered PPC strategy
• Meta Collection Ads case
• Magellan multichannel PPC
• Meta Ads restructure case
• Kanuk winter sales PPC
• Margin tracking case

If your account is bleeding budget, plateauing, or sitting in the wrong platform mix, scandiweb’s paid-search team can pull it apart and tell you exactly where the leaks are before you commit to any work. Reach out for a PPC audit – we will look at your account, your tracking setup, and your top-spend campaigns, and send back a one-page diagnosis.

The post Pay-Per-Click Advertising in 2026: Platforms, Costs, and What Still Converts appeared first on scandiweb.