This is the compass layer of how I run Ky Lan Athletics, my DTC activewear brand. A small team's scarcest resource isn't hours — it's knowing where the hours should go. So this layer runs AI as the analyst: session recordings, customer feedback, and ad data as the evidence, and two outputs — a prioritized conversion fix list, and a marketing playbook for what to highlight and which ads to run. The starting problem that proved the point: a mobile product-page-to-cart rate of 2.9%, against a 7–10% benchmark for DTC apparel.

Microsoft Clarity session recordings and heatmaps — product-page scroll depth of roughly 20–25%, meaning most visitors never reached fit and detail information placed lower on the page; size-guide abandons and dead-tap clusters on the photo gallery located exactly where shoppers gave up

94% of traffic is mobile, so every finding was evaluated mobile-first

Returns data — 13 of 25 returns said "too small." The store didn't have a conversion problem and a returns problem; it had one sizing problem showing up in two places

Ad-level data — strong click-through on creative but a conversion gap for women; session recordings located the drop-off at the sizing and color-selection step

Competitor teardown (Alo Yoga, Skims, and others) with AI as note-taker, producing a pattern list of what established brands place above the fold

The same mining that fixes the store also steers the marketing:

What to highlight — recurring praise from reviews, in customers' own words, becomes product-page copy and ad copy. Customers tell you which benefits actually sell; you just have to quote them.

Which creative angles to prioritize — praise and complaint themes rank the ad angles worth testing next, so the creative calendar follows evidence instead of hunches

What customers actually want — purchase and browsing behavior reveals which products and bundles pull the most interest, broken down by gender, feeding merchandising, bundle design, and the next collection's buying decisions

Weekly creative monitoring — every new ad upload gets tracked across its first week: which creatives carry the account, which are fatiguing, and when to rotate

Every fix traces to a named piece of evidence — nothing shipped on taste:

Fit block at the size selector, in the top 25% of the page: "Model is 5'8" wearing S · Slim fit — size up for a relaxed fit," with per-product fit values across the catalog

Size guide rebuilt — full-screen on mobile, measurement ranges per size, closing reassurance line: "Unsure? Size exchanges are 100% free."

Reviews surfaced — star rating and review count under the product title, click-scrolls to the reviews section

Gallery pass — color swatches replacing the photo ribbon, smaller thumbnails, more photos visible at a glance

Section reorder — upsell block moved below the description and reviews it was burying

Two outright bugs the recordings exposed — a broken mobile quick-add menu and a broken free-shipping progress bar, both fixed the same week

The success metric is defined before the results: mobile product-page-to-cart from 2.9% to 5%+, with sizing-related returns held at or below 2% on post-fix cohorts. The measurement window is running now — this page gets the numbers when the data does.

This is the compass layer — it doesn't add hours to a small team, it multiplies the ones they have by pointing them at what pays. This is the model for my business-insight package: your session data, your reviews, and your numbers turned into direction. The full stack:

The assistant you didn't hire — Running My Apparel Brand on AI Ops: the daily grind handled before anyone logs in

The analyst you didn't hire — this case study: evidence in, fix list and marketing direction out

The developer you didn't hire — AI-Ready Shopify Development with Claude Code: those fixes shipped in hours, not invoices