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Conversion Rate Optimisation in Amsterdam

A structured experimentation programme for Amsterdam stores where the Dutch numbers and the cross-border numbers are two different businesses.

Delivered remotely for brands across Amsterdam and Netherlands.

Grow · Amsterdam

Why Amsterdam brands come to us for this

  • Market treated as a primary segment from the first test, so Germany and Belgium stop being averaged into the Dutch result
  • Payment presentation tested directly: iDEAL ordering, Bancontact for Belgian traffic, and what pay-later placement does to AOV
  • Delivery day, evening slot and pickup-point choice tested as conversion elements rather than fulfilment settings
  • Apparel and denim tests judged on revenue per session net of returns, because the withdrawal right is a margin line
  • Testing paused through the Sinterklaas window and used for research, since late November and early December are different shoppers

The first thing we do on a Dutch account is stop looking at the blended conversion rate. A store selling into the Netherlands, Belgium, Germany and France has four conversion rates, four average order values and four returns rates, and averaging them produces a number that describes none of them. The domestic rate is usually strong and driven by familiarity, iDEAL and a delivery promise the buyer trusts. The German rate is often half of it, and the reason is almost never the product. It is a missing payment method, a delivery estimate the buyer does not believe, or a returns path that reads as expensive.

The second thing is treating returns as part of the conversion equation instead of an operations problem downstream. The EU withdrawal right gives every consumer fourteen days to change their mind, and in denim and apparel that makes the returns rate the largest single drain on contribution margin in this market. A test that lifts add-to-cart by six percent and lifts returns by nine is a loss, and you will not see it in a standard CRO report. So our primary metric on apparel accounts is revenue per session net of returns, read by market and by size, which changes which tests are worth running.

The third is the checkout experience itself, where the Dutch specifics are unusually concrete. Payment order, because iDEAL presented below card fields costs domestic conversion measurably. Delivery choice, because a named day, an evening slot and a pickup point are what buyers expect to see rather than a single generic shipping line. And the point at which shipping cost appears, which in a market with high free-shipping expectations is a moment of truth that belongs earlier on the page than most stores put it.

Net of returnsprimary metric on apparel accounts, not raw conversion rate
Per marketresults read by country before they are read blended
Pre-declaredsample size fixed before a test starts, with no early calls
Local context

Test by market, or you will keep optimising for the country that was already working

Amsterdam brands run into a specific statistical trap. Domestic traffic is the biggest and the best-converting segment, so any test read on the blended number gets decided by Dutch buyers, and the German or French experience never gets fixed because it never gets seen. We set market as a first-class segment before the first experiment goes live, and where a secondary market lacks the volume to reach significance on its own, we say so and treat it with research rather than pretending a test read it. The other local input is seasonality with an early peak. Sinterklaas on 5 December compresses the Dutch gifting window and pulls it forward of the rest of Europe, which means a test running through late November is measuring a different shopper on either side of the date. We freeze the roadmap through the peak, use it to gather research rather than to run experiments, and resume in January with a better set of hypotheses than we went in with.

Scope

What Conversion Optimisation includes

The same standard of work we run for every client — applied to a Amsterdam brand’s realities.

Full service detail
01

Conversion Research Sprint

Funnel analysis, session replays, heatmaps, on-site polls and user tests across your top revenue templates. Output is a ranked list of friction points with revenue attached.

02

Prioritised Test Roadmap

Every hypothesis scored on impact, confidence and effort, with the projected revenue and required sample size stated up front. You always know what runs next and why.

03

Experiment Build & QA

Tests built and QA'd across devices, browsers and your app stack, with flicker-free rendering and no measurable hit to Core Web Vitals.

04

Statistical Analysis

Pre-declared sample sizes, sequential-testing guardrails and segment-level readouts by device, traffic source and new versus returning. No peeking, no calling a test at 80%.

05

Checkout & Cart Optimisation

Cart drawer, shipping thresholds, payment options, express checkout placement and post-purchase upsell, tested against AOV and revenue per session rather than clicks.

06

Monthly Programme Report

Tests run, results in full, cumulative revenue impact and what the results taught us about your customers. Losers are reported in the same detail as winners.

Scoped and quoted for your Amsterdam store

We do not work off a rate card. Every Amsterdam engagement starts with a fixed statement of work — named deliverables, named dates, one number — written after we have looked at your store, not before. If a smaller first step would serve you better, we will say so.

Get this scoped
How it runs

From kickoff to results

01

Measure

We instrument the funnel properly first. Most stores have broken or double-counted events, and you cannot optimise against numbers you cannot trust.

02

Research

Quant tells us where visitors leave. Qual tells us why. We combine analytics, replays and direct customer input before writing a single hypothesis.

03

Prioritise

Hypotheses are scored and sequenced so the highest-value, lowest-effort tests run first. The roadmap is shared and you can reorder it.

04

Test

Two to four concurrent experiments depending on traffic, each run to a pre-declared sample size. No test gets stopped early because it looks good on day three.

05

Implement & Compound

Winners get hard-coded into the theme, losers get documented, and every result feeds the next round of hypotheses. The programme gets smarter each month.

Proof

Conversion Optimisation results

Anonymised under NDA. Figures pulled from the client’s own analytics.

Premium Skincare (DTC)

8-figure DTC brand, ~140 SKUs, US + CA · Shopify Plus (migrated from Magento 2)

Magento 2 cost $9k a month in hosting, extensions and emergency dev before a single feature shipped. Mobile LCP sat at 5.4 seconds and mobile converted at less than half the desktop rate across 68% of sessions. The named constraint: 11,412 indexed URLs and a top-3 organic position on their hero category, so a migration that lost rankings would cost more than the platform ever saved.

+41%mobile conversion rate1.62% to 2.28% over the first 90 days post-launch
5.4s → 1.8smobile LCP75th-percentile CrUX field data, not lab
+27%organic sessionssix months post-migration vs. pre-migration baseline, branded queries excluded
$108k/yrplatform cost removedhosting, extension licences and the standing emergency dev retainer
Engagement Replatform + CRO retainerTimeframe 7 months (4-month migration, 3-month optimization)

Performance Apparel (DTC)

~$6M/yr DTC, 900+ SKUs across size and colour variants, US · Shopify Plus

Returns ran at 31% and refund cost consumed the entire paid media margin. One size chart image served 40 different fits, and 62% of add-to-carts started on a collection page that never showed variant availability. The named constraint: no new product photography budget, so every fix had to come out of the existing asset library and the review corpus.

+29%sitewide conversion rate1.71% to 2.21%, five-month average
+14%average order value$84 to $96 once the threshold bar and cross-sell shipped
31% → 22%return ratenine-point drop, roughly $310k/yr in recovered margin
1.9x → 2.4xblended MERwith paid spend held flat throughout
Engagement Conversion-led rebuild + paid mediaTimeframe 5 months
In their words

Clients on this work

CVR 1.9% → 2.7%

“We'd been running our own tests for a year and calling every 3% swing a win. The first thing they did was tell us our sample sizes were garbage, which was not a fun call to sit through. Six months later we're getting wins that actually hold up when you re-measure them. Sitewide CVR went from 1.9% to 2.7%.”

Founder & CEOApparel DTC brand, ~$12M/yr · Los Angeles, CA
Verified client, 2025
FAQ

Conversion Optimisation in Amsterdam — your questions

Often not honestly, and we would rather say that than run a test that cannot reach significance and then interpret the noise. Below the volume needed for a clean read, the right work is qualitative: session replays of German sessions specifically, a check of the payment set and delivery estimate a German buyer actually sees, and translated copy reviewed by someone who writes German rather than by a translation app. Those are usually fixes rather than hypotheses anyway, and they can ship without a test.

It affects your data volume and it can affect the experience if the tool is implemented carelessly. Under EU consent rules a share of your visitors will not be measurable by client-side analytics, so a testing setup that only counts consented sessions is reading a filtered sample. We check whether the split is balanced across variants, run the experiment allocation in a way that does not create a flicker on top of the banner, and use server-side order data as the revenue source of truth rather than the tag.

Yes, and it is one of the more valuable tests in this market because Dutch and Belgian buyers have high free-shipping expectations. The experiment is not simply threshold up or down; it is threshold against basket composition and returns. A higher threshold that pushes a second apparel item into the basket can raise AOV and raise returns at the same time, so we read it on contribution after returns and after outbound and inbound freight, per market, and we run it long enough to see a full return window close.

We write and structure the hypothesis, and the Dutch copy is written or reviewed by a native writer before it goes live. Test variants that turn on wording are only as good as the wording, and machine-translated Dutch reads wrong in a way that is obvious to your customers and invisible to us. Where a test is structural rather than verbal, layout, payment order, delivery choice, page sequence, language is not the variable and we run it directly.

Around 40,000 sessions and 800 orders a month is where a testing programme becomes statistically viable. Below that, tests take months to reach significance and you are better served by research-led redesign work and analytics fixes. We will tell you honestly which bucket you are in.

The first test goes live in week three, after research and instrumentation. Meaningful cumulative impact typically shows around month four, once six to ten tests have run. CRO is a compounding programme, not a one-month fix, and anyone promising otherwise is selling best practices.

Some do, and that is normal. A loser is still information: it rules out a hypothesis and sharpens the next one. We report losses in the same detail as wins because a programme that only ever produces winners is one that is not being measured properly.
Next step

Conversion Optimisation for your Amsterdam brand.

Thirty minutes with the strategist who would actually run your account. We screen-share your store, read your data live, and tell you the three highest-value things we can see from the outside.

Shopify or Shopify Plus stores doing $150k/mo or moreFounder, CEO or eCommerce lead on the callNo deck and no pitch — we open your store instead

Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.