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.
Austin, TX
Conversion Rate Optimisation in Austin, TX
A structured experimentation programme for Austin brands whose founders will read the sample-size calculation before they read the summary.
Delivered remotely for brands across Austin and Texas.
Grow · Austin
Why Austin brands come to us for this
- Test roadmap built around units-per-order and subscription commitment, not button colours.
- Readouts segmented around SXSW and ACL traffic composition shifts instead of averaged through them.
- Shipping-threshold tests modelled against your real AOV distribution and Texas summer cold-pack costs.
- Sample sizes declared before launch, published for anyone on your team to audit.
- Losers reported in the same detail as winners — the number your investors will actually trust.
Austin is the easiest city in the country to sell CRO into and the hardest to fake it in. A large share of consumer founders here came out of software or venture, which means they have shipped an experiment before, they know what p-hacking is, and they will ask why a test was called on day four. Good. That is how it should be run everywhere.
The tests themselves look different for a consumable business than for a fashion one. Nobody here is optimising a size chart. The high-value experiments are about units and commitment: default pack size, sampler-versus-single framing, where subscribe-and-save sits on the PDP and how the discount is expressed, free-shipping thresholds set against your actual AOV distribution, and post-purchase offers that raise the first order without poisoning the second.
We publish the losers alongside the winners, at the same level of detail. A programme that reports only winners is not a programme, it is a reporting choice, and it will not survive ten minutes with an Austin board.
Local context
Testing against a seasonality curve most cities do not have
Austin's demand calendar has hard edges that will corrupt a test read if you ignore them. Two March weeks around SXSW and an October weekend around ACL create traffic composition shifts, not just traffic volume shifts — a large slug of out-of-town and first-touch visitors who convert nothing like your normal cohort. August brings the UT term restart and a different basket. And from roughly May through September, heat changes buying behaviour for anything meltable: shipping questions rise, cart abandonment moves, and a shipping-threshold test run in July will read completely differently in November. We segment readouts by these windows rather than averaging through them, and we sequence the roadmap so the tests that matter most run in clean periods.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Austin 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 Austin store
We do not work off a rate card. Every Austin 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 Austin — your questions
You can run tests, but do not make decisions on that data in isolation. The visitor mix during those March weeks skews heavily toward first-touch and out-of-town traffic that behaves nothing like your normal cohort. We either exclude the window or read it as its own segment, and we say which before the test starts.
Default pack size, subscription framing and placement on the PDP, sampler entry offers, and shipping thresholds. Those four move units-per-order and repeat rate, which is where a consumable's margin actually lives. Hero image tests are cheap to run and rarely move a number anyone reports on.
For formal A/B testing, yes — you would be waiting months per test. Below roughly 40,000 sessions we do research-led conversion work instead: session replay, funnel analysis, analytics repair and heuristic redesign of the highest-traffic templates. We would rather scope you into that than sell a programme that cannot reach significance.
By treating the May-to-September window as a distinct condition. Heat changes shipping questions, cold-pack attach rates and abandonment for anything meltable, so a threshold or shipping test run in July is not automatically valid in November. Where a result looks season-dependent, we re-run it in the opposite season before hard-coding it.
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 Austin 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.