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.
Milan
Conversion Rate Optimisation in Milan
A structured test programme for Milan stores where the Italian buyer and the export buyer abandon for completely different reasons.
Delivered remotely for brands across Milan and Italy.
Grow · Milan
Why Milan brands come to us for this
- Funnel analysis split by market from the start, so Italian and export abandonment are diagnosed separately
- Payment method presentation tested for Italian buyers who expect PayPal, cards and increasingly BNPL at the point of decision
- Landed-cost and duties clarity tested for buyers outside the EU, where a surprise at the door is a refund you already paid to ship
- Considered-purchase paths measured properly: swatch requests, sample orders and showroom appointments treated as conversions
- Test calendar built around the saldi windows, Salone traffic and the August lull rather than through them
The first thing worth knowing about a Milan store's funnel is that it is really two funnels wearing one dashboard. Italian traffic and export traffic convert at different rates, abandon at different steps and want different reassurance, and a blended conversion rate averages those two stories into one number that describes neither. So research starts by splitting the data by market before anyone proposes a test, because a change that helps a Munich buyer can be irrelevant or actively harmful to a buyer in Milan.
What stops the Italian buyer is usually trust and mechanics rather than desire. Which payment methods appear, whether the ones they use every day are among them, whether the delivery estimate is a date or a vague range, whether returns are explained in plain Italian, whether there is a phone number or a human anywhere on the page. What stops the export buyer is different: an unclear final price, no answer on duties or import handling, a shipping cost revealed at the last step, and copy that reads like it was translated by a machine, because it was.
Then there is the category problem. A furniture or lighting brand has a considered purchase with a lead time, and the conversion event that matters is often a sample request, a swatch order or a showroom appointment rather than a checkout. A fashion label's problem is size and fit, and its return rate is a conversion metric in disguise. We define the metric that matters before touching the page, run tests to a pre-calculated sample size, and call the ones that will not reach it rather than pretending a two-week test on modest traffic settled anything.
Local context
Testing around a promotional calendar you do not fully control
Italian retail has fixed discount seasons — the winter and summer saldi, whose start dates are set by the regions rather than by your marketing team — and that has a real effect on an experimentation programme. Behaviour inside a saldi window is not the same as behaviour outside it, so a test running across the boundary is contaminated, and results gathered in early January tell you very little about March. The same is true of April: Salone and the Fuorisalone push a burst of unusual, high-intent, largely international traffic through interiors sites for a week or two, and August does the opposite as Milan empties out. We build the roadmap around those edges — tests inside a stable window, seasonal periods used for observation and measurement rather than for experiments, and any test that must span a boundary segmented so we can read it honestly. On modest traffic that discipline matters more than clever hypotheses, because the fastest way to get a wrong answer is a test that finished for the wrong reason.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Milan brand’s realities.
Full service detailPrioritised 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.
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.
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%.
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.
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 Milan store
We do not work off a rate card. Every Milan 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 scopedHow it runs
From kickoff to results
Measure
We instrument the funnel properly first. Most stores have broken or double-counted events, and you cannot optimise against numbers you cannot trust.
Research
Quant tells us where visitors leave. Qual tells us why. We combine analytics, replays and direct customer input before writing a single hypothesis.
Prioritise
Hypotheses are scored and sequenced so the highest-value, lowest-effort tests run first. The roadmap is shared and you can reorder it.
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.
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
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
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, 2025FAQ
Conversion Optimisation in Milan — your questions
Sometimes yes and sometimes no, and the honest answer depends on your order volume rather than your sessions. Below a certain weekly conversion count a split test simply cannot resolve a realistic effect in a sensible timeframe, and running one anyway produces confident nonsense. Where that is the case we shift to research, sequential before-and-after measurement on large changes, and fixing things that are unambiguously broken — which on most Milan stores is a full quarter of work before testing becomes the right tool.
Run the same hypothesis where it makes sense, but read the results separately and expect them to disagree. Reassurance needs differ by market: a German buyer's questions about returns and invoicing are not the same as an Italian buyer's questions about delivery and payment. If one market has enough volume to resolve a test on its own, it gets its own decision; if not, we treat the smaller market as directional evidence rather than proof.
By making the wait credible instead of hiding it. The tests that move considered purchases are usually about certainty: an explicit production and delivery timeline, what happens after the order, who to talk to, evidence the piece is made where you say it is. Burying the lead time until checkout raises conversion for a week and raises cancellations for a quarter, which is why we measure to net revenue after cancellations and returns rather than to the checkout event.
Not usefully, and we will say so rather than take the work. If GA4 revenue and Shopify revenue disagree, or if European consent handling means a chunk of your traffic is missing from reporting entirely, every result you produce is contested. Fixing the measurement layer is usually a short piece of work compared with running a testing programme on numbers nobody trusts, and it makes everything downstream cheaper.
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 Milan 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.