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.
Charlotte, NC
Conversion Rate Optimisation in Charlotte, NC
A testing programme for Charlotte stores where the biggest conversion lever is usually confidence, not persuasion.
Delivered remotely for brands across Charlotte and North Carolina.
Grow · Charlotte
Why Charlotte brands come to us for this
- Research aimed at compatibility confidence first: fitment confirmation, spec clarity and the returns terms behind a heavy-item purchase
- Freight cost, liftgate and lead-time disclosure tested on the product page rather than sprung at checkout
- Retail and trade buyers separated in every readout, so a wholesale-friendly change is never scored on blended numbers
- Test windows held across a whole race-season or market cycle so seasonality is not mistaken for a result
- Reporting built for a finance-trained founder: revenue per session and contribution, not clicks and engagement rate
The dominant conversion problem in this metro is doubt rather than desire. A buyer already wants the header, the cushion, the yarn-dyed henley. What stops the purchase is not being certain it fits their car, their frame, their programme spec — and the cost of being wrong is high enough that they leave to go check. Every hour a customer spends on a forum verifying what your product page should have told them is a conversion you paid for and did not close. So the research here tends to point at compatibility confirmation, dimensional clarity and returns policy long before it points at button colour.
The second lever is the money conversation happening after the price. Freight surcharges discovered at checkout, an eight-week production window revealed in a confirmation email, a liftgate fee nobody mentioned. Charlotte sells a lot of heavy and made-to-order goods, and in those categories the checkout drop-off is usually a surprise, not a hesitation. Fixing it is unglamorous work: putting delivery method, cost band and lead time on the product page, and testing whether being honest earlier costs you more clicks than it saves you abandoned carts. In our experience it does not, but we test it rather than assert it.
Third, we report to a finance standard because that is what this audience expects. This is a metro full of operators who have sat in credit committees and read a variance report for a living, and they are not interested in a dashboard of sessions. Every hypothesis is scored on impact, confidence and effort with the sample size stated up front, tests run to significance or get called, and losers are published in the same detail as winners. A method that only ever produces winners is not a method — and in Charlotte someone will notice.
Local context
Charlotte traffic is seasonal, segmented and easy to misread
Two things make naive testing dangerous on Charlotte stores. The first is seasonality with a hard edge. Performance and aftermarket demand swings around the race calendar and the winter build season, when a car is off the road and the owner is spending; home furnishings swing around High Point Market and the spring delivery cycle; craft beverage swings around release weekends and taproom traffic. A test started in one regime and read in another is measuring the calendar, not the change. The second is channel mix. A store with a live wholesale channel has two audiences on the same templates — a retail buyer researching for herself and a trade buyer checking availability for an account — and blended results hide the fact that a change helped one and hurt the other. We declare the segments before the test runs, hold tests across whole cycles rather than convenient weeks, and read device, source and new-versus-returning separately as standard.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Charlotte 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 Charlotte store
We do not work off a rate card. Every Charlotte 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 Charlotte — your questions
Often yes, but the target changes. On a high-ticket catalogue the win is rarely a percentage point of sitewide conversion; it is shortening the research loop and reducing the number of buyers who leave to verify a spec. We would look at assisted paths, quote and enquiry completion, and revenue per session rather than raw conversion rate, because a two percent lift on a four-thousand-dollar sofa is a very different sum from the same lift on a T-shirt.
Yes, provided we can identify them. Trade buyers behave completely differently — they arrive knowing what they want, skip merchandising and go straight to search or a reorder list — so if they sit in the same sample they will drag the result toward no effect. We exclude logged-in company accounts from consumer tests where possible, or at minimum report them separately, and we test the trade experience on its own terms.
It usually comes down to where confirmation appears and how sticky it is. We test whether a saved vehicle or spec persists across collection and product pages, whether the page shows a clear positive confirmation rather than only filtering out mismatches, and what happens on a near-miss where a part fits with a modification. Those are structural changes, so they are built carefully and QA'd across your app stack before they see traffic.
It rules out split-testing small changes, not improvement. At that volume we run a research sprint, fix the friction the evidence is unambiguous about, and use before-and-after measurement with a stated caveat rather than pretending an underpowered A/B test proved something. Once traffic supports it we move to a proper testing cadence. We would rather tell you that than sell you a programme that cannot reach significance.
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 Charlotte 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.