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.
Columbus, OH
Conversion Rate Optimisation in Columbus, OH
A test programme judged on revenue that stays sold, not on orders placed and returned six weeks later.
Delivered remotely for brands across Columbus and Ohio.
Grow · Columbus
Why Columbus brands come to us for this
- Every apparel test read twice — at checkout and again after the return window, with reason codes attached
- Fit content tested as a conversion lever: real measurements per size, on-body imagery at multiple sizes, review-based fit feedback near the selector
- Size-run availability handled honestly on the page, so a broken run informs the buyer instead of silently killing the session
- Test calendar planned against your drop schedule, the August campus surge and the March strength season, not an even monthly drip
- Results joined to sell-through by size, so a win on styles heading for markdown is reported as what it is
In most categories, conversion optimisation ends at the thank-you page. In apparel and footwear it does not, and a Columbus programme that ignores that is optimising the wrong number. A variant that lifts add-to-cart by nudging shoppers toward a size they have not thought about will show as a winner and cost you money once returns land. So we read every test twice: at checkout, and again after the return window closes, with reason codes attached. Net revenue per session is the metric that survives contact with a fitting problem.
That reframes the research too. The friction on an apparel product page is rarely the button. It is the absence of an answer — how does this run, what is the model wearing, does the waist sit here or there, is my size actually in stock or is the picker going to disappoint me. We go looking for those gaps in session replays, site-search logs, review text and the returns reason data itself, then test content that closes them: real measurements at every size, on-body imagery at more than one size, fit feedback surfaced near the selector, and honest size-run availability instead of a greyed-out swatch with no explanation.
The traffic pattern here also decides what is testable. Columbus brands are often spiky rather than steady — a drop, an August surge, a March event week — which means a test that would take eight weeks on baseline traffic can reach significance in days if you run it into the right window, and that a test launched the week before a drop will read as noise. We plan the calendar against your season so the roadmap matches the traffic you will actually have, and we say plainly when a hypothesis is not testable at your volume yet.
Local context
Testing against a spiky calendar and a returns tail
Two local realities shape how this programme runs. The first is that a large share of Columbus ecommerce revenue arrives in bursts — the campus return in August, football Saturdays, the Arnold in March, and whatever drop cadence you have set for yourself — so the test calendar is planned around those windows rather than as an even monthly drip. High-traffic weeks are where we run the tests that need volume; the quiet stretches are where we ship the structural work that does not need a control group. The second is the returns tail. Any apparel test read before your return window closes is a provisional result, and we mark it as such in the report rather than banking a win that has not settled. Where the merchandising team already tracks sell-through and markdown by size, we join our results to that data, because a test that lifts conversion on the sizes you were about to mark down is a genuinely different outcome from one that clears the sizes you needed to protect.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Columbus 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 Columbus store
We do not work off a rate card. Every Columbus 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 Columbus — your questions
A drop is a legitimate test population, but it is a different one from your baseline, so it has to be segmented rather than blended. Drop traffic is warmer, more mobile, more likely to be repeat, and behaves differently at the size selector. We either run a test entirely within drop weeks and read it against other drop weeks, or we exclude the window. Mixing the two in one readout is how brands convince themselves a change worked.
In our experience the order is: real garment measurements per size, imagery showing the product on more than one body, customer fit feedback placed at the selector rather than buried in reviews, and clear stock truth at size level. Those four close the questions a fitting room answers in seconds. Badge rows and countdown timers rarely touch a fit decision, whatever they do to a generic funnel.
By instrumenting the return before the test starts. Every variant carries through to the order record, so when the return arrives with a reason code we can attribute it back to the experience that produced the sale. If a variant lifted checkout conversion and lifted 'too small' returns by more, we call it a loss and say so. That takes longer to report and it is the only version worth having.
Yes, and it is usually underworked. B2B friction is different — reorder speed, size-run ordering across a grid, visibility of contracted pricing, whether a buyer can see stock cover before committing. Those are testable, the population is small but high-value, and improvements tend to be structural rather than statistical. We run that as a usability and workflow track rather than as an A/B programme, because the sample sizes do not support the latter.
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 Columbus 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.