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.
Tampa, FL
Conversion Rate Optimisation in Tampa, FL
A structured test programme for Bay-area stores where the visitor's real question is fit and freight, not price.
Delivered remotely for brands across Tampa and Florida.
Grow · Tampa
Why Tampa brands come to us for this
- Site-search log analysis for part numbers and model codes that return nothing, then tested fixes to the null-result page
- Freight and delivery-promise placement tested on oversize PDPs, measured on revenue per session rather than clicks
- Fitment selector tests: placement, persistence, and how confidently the page confirms compatibility before add-to-cart
- Test windows kept inside one demand regime so winter-resident and summer-boater traffic never get averaged together
- Storm-spike days tagged and isolated so a September surge cannot manufacture a false winner
Conversion is in our name, so here is the blunt version for this market: most Tampa stores are not losing sales on the button colour, they are losing them at the moment a buyer cannot confirm the part fits or cannot find out what shipping costs. Session replays on marine, pool and enclosure catalogs show the same loop over and over — search, filter, back, search a part number, open a PDF spec sheet in a new tab, leave. That is a research finding you can act on, not a hunch.
So the research sprint here goes somewhere specific. We read site-search logs for part numbers that return nothing, count how many sessions open a spec document before abandoning, measure the drop between add-to-cart and shipping calculation on oversize items, and pull the support inbox for the questions that should have been answered on the page. Then the backlog gets scored, and the first tests are almost always about confidence and freight transparency rather than persuasion.
The testing calendar itself needs local judgement. Bay-area traffic is not one population. Winter residents behave differently from summer boaters, and a storm week produces a demand spike with completely different intent. We keep test windows inside a single demand regime, exclude storm-spike days from readouts rather than letting them poison a result, and report by segment so a winning variant is not a February artefact you rolled out in July.
Local context
Two audiences, one store, and a September that breaks your averages
Running a Tampa test programme means accepting that your traffic changes character three times a year. From roughly November through April the store is serving winter residents and gift buyers who research carefully and buy on desktop more than you would expect. Summer is boating, fishing and pool season — hot, urgent, phone-heavy, replacement-driven. Then a storm week arrives and produces the highest-intent traffic you will ever see, buying generators, shutters and hardware with no interest in your brand story. Averaging those three together produces a conversion rate that describes none of them. We pre-declare sample sizes inside a single regime, tag storm-spike days so they can be isolated in every readout, and never call a test that straddles the boundary. Structural work — search that recognises part numbers, filters that hold, freight shown early, mobile speed — pays in all three regimes, which is where we start.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Tampa 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 Tampa store
We do not work off a rate card. Every Tampa 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 Tampa — your questions
Yes, with discipline about windows. We size tests to run inside a stable stretch and refuse to draw conclusions across a seasonal or storm boundary. Where a spike lands mid-test, the affected days are tagged and the result is read both with and without them; if the two readings disagree, the test gets extended rather than declared.
Almost always the compatibility moment. Whether the fitment selector appears before the grid or after it, whether the confirmation is explicit on the product page, and whether the choice persists as the buyer moves through collections. Getting that wrong costs more sessions than anything below the fold, and the fix tends to lift search and filter engagement at the same time.
In our experience with oversize categories it helps, and the mechanism is that it removes a late surprise rather than adding an early one. The buyer who leaves at a shipping shock rarely comes back; the one who sees a real number on the product page either accepts it or self-selects out cheaply. We test placement and framing, and we read it on revenue per session so a drop in add-to-cart does not get mistaken for a loss.
Possibly not for classic A/B testing, and we will say so rather than sell you a programme that takes four months per result. Below meaningful volume the honest work is research-led: replay analysis, search-log fixes, checkout instrumentation and structural redesign of the fitment and freight paths, implemented directly and measured before-and-after with clear caveats.
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 Tampa 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.