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.
Boston, MA
Conversion Rate Optimisation in Boston, MA
A test programme that knows the difference between a real lift and the fact that it is the first week of September.
Delivered remotely for brands across Boston and Massachusetts.
Grow · Boston
Why Boston brands come to us for this
- Test windows scheduled into the clean stretches so a semester spike is never mistaken for a lift
- Student and parent buyer paths analysed separately instead of averaged into one conversion rate
- Subscription objection tested where it happens — pause, skip and cancel clarity on the supplement PDP
- Delivery-date certainty tested as a conversion lever for perishable and cold-weather catalogues
- Fit, warmth and fabric-weight content tested against returns as well as against conversion
Boston is a difficult city to run a naive test programme in, and most of the damage is invisible. Traffic composition changes violently twice a year. A test that starts in mid-August and ends in mid-September has not measured a variant, it has measured a population change: the visitor mix moved from returning local customers to first-time student and parent buyers with different price sensitivity, different device behaviour and a completely different urgency profile. The variant wins, gets shipped, and quietly underperforms for the rest of the year.
So the calendar is part of the method here. Clean read windows sit in October and November, and again from late February through April once the winter volatility settles. Tests that must run through a pulse get segmented reads by new versus returning and by traffic source, and if the segments disagree we call it and rerun rather than banking a number we do not believe. Sample size is declared before the test starts, which matters more in a market where a single week can supply a quarter of the annual traffic.
The friction itself is local too. Wellness and supplement stores here lose people at the subscription decision, not the add-to-cart — the cancellation anxiety is the objection, so the pause and skip mechanics belong on the product page rather than buried in an account portal. Perishable food stores lose people at delivery uncertainty, where the winning changes are usually about showing the arrival date earlier rather than about the button. Outerwear stores lose people to fit and warmth doubt, which is a content and specification problem dressed up as a conversion one.
Local context
The parent is a different buyer from the student
Almost every Boston brand with campus exposure is serving two people at once and reporting them as one number. The student buys on a phone, late, price-conscious, comparing, often influenced by something a friend has. The parent buys remotely on the student's behalf, at a higher AOV, from a desktop, with gifting and delivery-date requirements the student never has, and with a completely different tolerance for shipping cost. Blended into a single conversion rate they cancel each other out and the roadmap gets built on an average that describes nobody. We split them at the analysis layer using device, source, shipping-address behaviour and order composition, then test against each separately — the gift-message and delivery-date work that lifts the parent path usually does nothing for the student path, and that is fine as long as you know which one you moved.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Boston 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 Boston store
We do not work off a rate card. Every Boston 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 Boston — your questions
You can, but only tests designed for that population and read on that population. A generic sitewide test running through the spike inherits a visitor mix that will not exist again for eight months, so the result does not generalise. What is genuinely worth testing in that window is the peak-specific stuff: bundle presentation, gifting, delivery-date messaging and the parent checkout path.
Research and instrumentation first, then fewer and bolder tests. When volume is concentrated, running eight small tests a quarter is a way of learning nothing slowly. We run session replay, on-site polling and user testing to build a strong evidence base, then test larger changes with real effect sizes during the windows where volume supports it.
As a delivery-confidence problem and as a device shift. Storm weeks produce elevated cart abandonment on anything with a delivery promise, and it recovers on its own, so we annotate those weeks and exclude them from test reads rather than treating them as findings. The durable lesson is usually that the arrival date needs to appear earlier in the journey than it currently does.
On the product page, and that is where we would start the roadmap, because the objection is about commitment rather than price and it needs answering while the customer is still reading. The pause, skip and cancel mechanics have to be visible at that moment. Offering the subscription only at checkout converts the people who were going to subscribe anyway and misses the ones who needed reassuring.
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 Boston 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.