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.
Riverside, CA
Conversion Rate Optimisation in Riverside, CA
Conversion work for Inland Empire stores where the biggest leak is usually a shipping number the customer did not expect until the last step.
Delivered remotely for brands across Riverside and California.
Grow · Riverside
Why Riverside brands come to us for this
- Research starts at freight and delivery choice, the single largest abandonment driver for Inland Empire heavy goods
- Fitment selector and garage tested as a conversion surface, including the no-results state that usually dead-ends
- Trade and retail buyers analysed as separate segments, so one audience never funds the other's win
- Total delivered cost surfaced early, in line with California mandatory-fee disclosure requirements
- Test durations sized against desert-season demand and summer heat suppression, not a flat monthly average
In this region the classic conversion failure is not a weak headline. It is a customer who added a two-hundred-pound item to the cart, reached checkout, saw a freight quote with a lift-gate surcharge attached and left. Sometimes the quote is correct and the page simply failed to prepare anyone for it. Sometimes the quote is wrong because dimensional data on the variant was estimated years ago. Either way, research starts at the cart and works backwards, because that is where Inland Empire revenue actually leaks.
The second recurring leak is the fitment selector. On a parts store the year-make-model tool is the highest-traffic interactive element on the site and it is usually the least tested one. We watch session replays of people trying to use it: dropdowns that reset on back-navigation, a garage that forgets the vehicle, a no-results state that dead-ends instead of offering the universal alternatives, submodel lists that make sense to a supplier and not to a driver. Fixing those is unglamorous and it moves revenue more than any hero redesign.
The third is the two-audience problem. Stores here serve retail buyers and trade buyers on the same URLs, and optimising for one usually degrades the other. A contractor or shop owner who knows exactly what they want is punished by a discovery-led layout; a first-time consumer is confused by case packs and trade terminology. We segment the analysis before running any test, so a result is never a win for one audience quietly funded by a loss from the other.
Local context
California pricing rules and heavy goods change what you are allowed to test
California now requires the price a customer first sees to include mandatory fees, which removes a whole category of the drip-pricing tactics some stores still lean on and makes honest total-cost presentation a compliance matter rather than a preference. For heavy-goods merchants that is a constraint worth using: if the full delivered cost has to be visible anyway, showing it early with an explanation of what lift-gate, curbside and appointment delivery actually mean tends to outperform hiding it. Prop 65 warnings are a second local wrinkle, because a poorly placed warning block can spook a consumer while a well-designed one is simply a fact on the page. And seasonality is real here: the off-road and powersports side ramps into the cooler desert months, while triple-digit summer heat suppresses browsing on categories that involve going outside. We size tests against that curve so a result is not just a season being read as a winner.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Riverside 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 Riverside store
We do not work off a rate card. Every Riverside 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 Riverside — your questions
Usually both, and they need separating before you test anything. First we check whether the quotes themselves are right, because bad dimensional or class data on the variant produces inflated rates that no amount of copy will rescue. Once the numbers are defensible, the CRO work is about when and how the cost appears, what delivery options are offered, and whether the page explains curbside versus threshold delivery in terms a buyer understands.
Yes, provided the experiment platform can read logged-in company status so results split cleanly. That segmentation is non-negotiable, because trade buyers convert at a much higher rate and will drag any blended result toward whatever they happened to do that fortnight. We usually find the two audiences want opposite things from navigation, which is itself a useful finding.
On a parts catalogue it is often the difference between a browsing session and an order. The pattern we see repeatedly is not that people cannot find the tool, it is that they abandon partway through: a dropdown resets, the vehicle is not remembered on the next page, or a valid vehicle returns nothing because the application data is incomplete. Those are fixable in build, and each fix is measurable.
Sometimes not for classic A/B testing, and we will say so rather than run underpowered tests and report noise as insight. Where volume is thin, the honest programme is qualitative research, session replay, buyer interviews and sequential before-and-after measurement on high-confidence fixes, plus testing concentrated on the consumer side where the traffic actually is. Declaring a two percent lift on four hundred sessions helps nobody.
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 Riverside 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.