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.
A structured experimentation programme for Stockholm stores where the Swedish numbers and the export numbers are two different businesses wearing one dashboard.
Delivered remotely for brands across Stockholm and Sweden.
The first thing we do on a Swedish account is stop reading the blended conversion rate. A store selling into Sweden, Norway, Denmark, Finland and Germany has five conversion rates, five average order values and five returns rates, and the average describes none of them. Domestic usually looks strong because the buyer recognises the brand, sees the payment method they use and understands the delivery promise. Germany is often half of it, and the reason is almost never the product — it is a price that ends in odd decimals, a delivery estimate nobody believes, or a returns path that reads as expensive from abroad.
The second is treating returns as part of the conversion equation rather than an operations problem downstream. The EU withdrawal right gives consumers fourteen days to change their mind, and in Swedish apparel and footwear that makes returns the largest single drain on contribution margin. A test that lifts add-to-cart by six percent and lifts returns by nine is a loss you will not find in a standard CRO report. So on apparel accounts the primary metric is revenue per session net of returns, read by market and by size, and that single change rewrites which hypotheses are worth building.
The third is the checkout itself, where the Swedish specifics are concrete enough to test directly. Where Klarna and Swish sit in the wallet order and what happens to completion when they move. Whether pay-later messaging on the product page matching checkout changes anything (it does). What service-point and locker selection does when it is a visible choice rather than a line of small text. And where shipping cost first appears on the page, which in a market with strong free-delivery expectations is a moment of truth most stores hide too late.
A Stockholm testing calendar does not run evenly, and pretending otherwise wastes a quarter. July is close to unusable as an experiment window: traffic composition changes completely while the country is on semester, and a test that reads significant across those weeks is describing a shopper you will not see again until August. Then the opposite problem arrives. Black Week through Christmas and into mellandagsrea is the highest-traffic, highest-intent stretch of the year, and it is also the worst time to change things underneath a promotion — a winning variant from a discount week rarely holds in February. So we run the build-and-test programme hard from February to June, treat July as a research month using session data and support tickets rather than experiments, resume with the highest-volume tests in September and October, then freeze the roadmap from Black Week and spend the peak instrumenting instead of intervening. Testing also has to respect the payment presentation rules Swedish law places on credit-based options at checkout, so pay-later variants get scoped with that in mind rather than tested blind.
The same standard of work we run for every client — applied to a Stockholm brand’s realities.
Full service detailFunnel 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.
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.
Tests built and QA'd across devices, browsers and your app stack, with flicker-free rendering and no measurable hit to Core Web Vitals.
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%.
Cart drawer, shipping thresholds, payment options, express checkout placement and post-purchase upsell, tested against AOV and revenue per session rather than clicks.
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.
We do not work off a rate card. Every Stockholm 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 scopedWe instrument the funnel properly first. Most stores have broken or double-counted events, and you cannot optimise against numbers you cannot trust.
Quant tells us where visitors leave. Qual tells us why. We combine analytics, replays and direct customer input before writing a single hypothesis.
Hypotheses are scored and sequenced so the highest-value, lowest-effort tests run first. The roadmap is shared and you can reorder it.
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.
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.
Anonymised under NDA. Figures pulled from the client’s own analytics.
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.
~$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.
“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%.”
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.
Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.