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.
Minneapolis, MN
Conversion Rate Optimisation in Minneapolis, MN
A structured test programme built around a market where two months of the year carry a disproportionate share of the revenue.
Delivered remotely for brands across Minneapolis and Minnesota.
Grow · Minneapolis
Why Minneapolis brands come to us for this
- Test calendar built against your real demand curve: openers, first freeze and Q4, not an even monthly cadence
- Research sprint and instrumentation scheduled into the slow spring stretch so peak weeks are spent testing, not planning
- Fit, layering and temperature-rating content tested against retained revenue, because returns are where cold-weather margin goes
- MAP-safe levers prioritised — bundles, kits, freight thresholds and assortment depth rather than discount framing
- A declared change freeze through your peak, with only pre-scoped low-risk experiments running
Testing in a violently seasonal market is a scheduling problem before it is a statistics problem. A test running through the week of the fishing opener, the first hard freeze or the Q4 gifting run is measuring a shopper who is not representative of the rest of your year, and a test running in a flat July week may never reach a sample size at all. So the roadmap gets built against your actual demand curve: research and instrumentation in the quiet stretch, high-traffic tests queued for the weeks that can feed them, and a hard change freeze when the revenue arrives.
The second constraint here is that price is frequently off the table. If your everyday pricing is governed by a MAP policy and a retail partner can see your homepage, the standard CRO lever of discount framing is unavailable. That is not a handicap — it forces the programme onto levers that compound instead: bundle and kit construction, shipping threshold placement, assortment depth messaging, and the content that resolves a hesitation rather than buying past it.
And in cold-weather and technical categories, conversion rate on its own is a misleading number. Pushing an insulated jacket or a bibbed suit harder without resolving fit produces orders that come back in six weeks, and returns are where the margin on this category quietly disappears. We instrument the return reason alongside the conversion event and judge tests on retained revenue per session, which changes which winners we keep.
Local context
Test around the openers, the freeze, and a peak you cannot risk
The Minnesota calendar gives a testing programme both its best and its worst weeks, and treating them the same is the most common error we inherit. The May fishing opener and the November deer opener pull enormous, narrow, high-intent traffic that is excellent for testing category and fitment navigation and useless for testing a gifting page. The first hard freeze produces a demand step-change that will make any test running through it look like it won. Q4 delivers the volume a test needs and is also the quarter where a losing variant costs the most, so we run pre-declared, tightly scoped tests there and nothing structural. The quiet spring and summer stretch, which most agencies treat as dead time, is where the research sprint, the session-replay review, the customer interviews and the instrumentation work belong — so that when the volume arrives, we are executing a queue rather than deciding what to try.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Minneapolis 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 Minneapolis store
We do not work off a rate card. Every Minneapolis 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 Minneapolis — your questions
Both, but for different things. Low-traffic months are for research, instrumentation, qualitative work and long-running tests on high-frequency events like add-to-cart or filter usage. High-traffic months are for revenue-level tests that need volume, run to a pre-declared sample size and scoped so a loss is survivable. What we do not do is run a structural experiment through your peak and hope.
It removes one lever and sharpens the rest. Shipping threshold placement, bundle construction, kit pricing on direct-only assortment, financing display, and the content that resolves a spec question are all testable without touching everyday price. In practice, MAP-constrained brands end up with a more durable programme, because a discount test teaches you very little that survives the promotion ending.
By treating a return as a negative conversion and reading results on net revenue per session over a window long enough to capture them. In apparel and technical gear this routinely flips a result: a variant that lifted add-to-cart by removing a fit interstitial looks like a winner for three weeks and a loser by week eight. We agree the readout window with you up front so nobody is surprised when a win gets revised.
Where the volume supports it, yes, though the method changes. B2B traffic is too low for classic A/B testing in most cases, so we work qualitatively: session replays of real dealer reorders, timed task testing with a handful of accounts, and before-and-after measurement on hard operational metrics like reorder completion rate and support tickets per order. That produces reliable answers where a split test would produce noise.
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 Minneapolis 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.