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.
Portland, OR
Conversion Rate Optimisation in Portland, OR
A test programme judged on kept revenue, because in this market a conversion that comes back is not a conversion.
Delivered remotely for brands across Portland and Oregon.
Grow · Portland
Why Portland brands come to us for this
- Net revenue per session after returns as the primary metric, with return reasons tagged by variant
- Fit, width and layering content treated as the main experiment surface on footwear and outerwear pages
- Subscribe-versus-one-off, cadence defaults and pre-purchase skip visibility tested as their own programme
- Template-level tests for small-run catalogues where a single silhouette will never reach significance
- Test calendar planned around wet-season research demand and fourth-quarter gifting rather than a flat month
Conversion rate is the wrong headline metric for a Portland footwear or technical apparel brand. Push the rate up by two-tenths with a bolder size selector and a free-returns badge, watch the return rate rise faster, and the programme has made the business poorer while every dashboard says it worked. So we set the primary metric as net revenue per session after returns, and we tag return reasons at the SKU and variant level so a test can be read against the outcome that actually pays.
That reframing changes what gets tested. Fit and spec content becomes the main experiment surface: how width and volume are expressed, whether a comparison to a model the customer already owns beats a generic chart, whether aggregated fit feedback near the size selector outperforms the same content in the reviews tab, whether layering guidance reduces the wrong-weight return on outerwear. These are the tests with real money attached in this category, and almost nobody runs them because they are harder to build than a sticky bar.
The consumables side needs its own programme entirely. For a roaster or a club the decision being optimised is not add-to-cart, it is subscribe-versus-one-off and then survive month four. So the experiments sit on the subscription selector, the cadence default, the visibility of skip and delay before purchase rather than after, roast-date and freshness messaging, and the gifting path in the fourth quarter. Same method, completely different backlog, and we run them as separate programmes rather than averaging two businesses into one report.
Local context
Small-run catalogues mean traffic, not ideas, is the constraint
Most Portland brands we work with are in the $150k to $5M band with a deliberately narrow, small-run catalogue. That means the honest constraint on a test programme is sample size, not hypotheses — a beautifully designed test on a single boot silhouette will never reach significance before the season ends. So we run tests at template level rather than product level wherever the change is category-wide, we accept longer run times and declare them up front rather than peeking at week two, and where traffic genuinely will not support an A/B we say so and use pre-post with a holdout or a straight best-practice implementation instead of dressing a guess up as an experiment. We also plan the calendar around this metro's demand shape: the wet season drives outerwear and footwear research, gifting drives coffee and hardgoods in the fourth quarter, and neither is a sensible window to have half your traffic in a variant nobody has validated.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Portland 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 Portland store
We do not work off a rate card. Every Portland 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 Portland — your questions
By building the lag into the design rather than ignoring it. We declare a return window up front — typically matching your policy — and hold the readout until the exposed cohorts have both passed it, reporting an interim conversion result in the meantime clearly labelled as provisional. Return reasons get captured at variant level so we can see whether a fit change moved sizing returns specifically rather than total returns, which is a noisier number affected by things the test never touched.
Do not start a test whose runtime straddles the start of your peak, because the population changes underneath you and the result becomes uninterpretable. For an outerwear or footwear brand here that usually means avoiding a start in late autumn; for a roaster it means the gifting run-up. The good windows are the shoulder periods, and a test started early enough to finish before the season is worth more than a bigger idea launched at the wrong moment.
The mechanics people actually leave over. Whether the default cadence matches real consumption instead of a platform default, whether skip and delay are visible before purchase rather than hidden in an account portal, whether grind and roast selection at signup reduces early churn, and whether roast-date transparency beats generic freshness language. Discount depth is the last thing we would test, because winning that one usually means training the base to wait.
Usually yes, if the underlying data exists. Where width, volume and comparison data are already in metafields, a test is a matter of surfacing and arranging them. Where the data is trapped in an image size chart or in prose, we build a small structured set for the top revenue styles first and test on those rather than waiting for a whole-catalogue data project. That also gives you the evidence to decide whether the catalogue-wide effort is worth funding.
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 Portland 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.