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.
Most Albuquerque stores cannot run a statistically valid A/B test in January — so we tell you which kind of conversion work you actually qualify for.
Delivered remotely for brands across Albuquerque and New Mexico.
Here is the uncomfortable part of selling CRO in this market: a formal testing programme needs roughly 40,000 sessions and 800 orders a month, and a large share of Albuquerque stores clear that for eight weeks a year and nothing like it for the other forty-four.
That does not mean conversion work is off the table. It means the method changes. Outside the season we do research-led conversion work — session replay, funnel analysis, on-site polls, and fixes shipped against evidence rather than a split test. Inside the season, when traffic concentrates hard, we run real experiments and get a year's worth of statistical power in a quarter.
The leaks themselves are also different here. In a coastal metro the biggest PDP problem is usually trust or fit. In Albuquerque it is shipping. Heavy, bulky, perishable, insured — a case of salsa, a stoneware set, a frozen chile order — and the cost lands on the buyer at the worst possible moment. Where and how you disclose that number is worth more than any button colour test we could run.
New Mexico's flagship export categories are physically awkward to ship: heavy glass jars, frozen and insulated food, fragile pottery, high-value insured jewelry. That produces a specific, measurable failure — a cart abandonment cliff at the shipping step that is far steeper than the DTC average, because the buyer discovers a $28 insulated-shipping charge after committing emotionally to a $34 product. The fix is almost never a discount; it is threshold architecture, disclosure earlier in the journey, and bundle sizes engineered so the shipping-to-goods ratio stops looking absurd. The second local reality is timing. Because order volume compresses into the harvest and holiday windows, we front-load the research and build phases into spring, queue a prioritised test backlog, and then run it hard in September through December when the traffic exists to reach significance in days rather than months.
The same standard of work we run for every client — applied to a Albuquerque brand’s realities.
Full service detailFunnel 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.
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.
Tests built and QA'd across devices, browsers and your app stack, with flicker-free rendering and no measurable hit to Core Web Vitals.
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%.
Cart drawer, shipping thresholds, payment options, express checkout placement and post-purchase upsell, tested against AOV and revenue per session rather than clicks.
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.
We do not work off a rate card. Every Albuquerque 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 scopedWe instrument the funnel properly first. Most stores have broken or double-counted events, and you cannot optimise against numbers you cannot trust.
Quant tells us where visitors leave. Qual tells us why. We combine analytics, replays and direct customer input before writing a single hypothesis.
Hypotheses are scored and sequenced so the highest-value, lowest-effort tests run first. The roadmap is shared and you can reorder it.
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.
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.
Anonymised under NDA. Figures pulled from the client’s own analytics.
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.
~$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.
“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%.”
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.
Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.