eCommerce analytics & growth enablement

Most important funnel metrics are: product page view → add‑to‑cart rate, cart → checkout start, checkout completion rate, average order value, and revenue per visitor. Track them by channel and device so you can prioritize fixes where drop‑offs hurt revenue most, not just traffic or clicks.​

For D2C, a pragmatic CLTV is: CLTV ≈ average order value × purchase frequency × average customer lifespan − acquisition/servicing costs. Use it to set CAC caps, choose which cohorts to scale (high CLTV channels, products, or geos), and design loyalty/subscription offers that improve payback periods.​

Set up A/B tests via a feature‑flag/experimentation tool that randomizes users into variants at the edge, logs events consistently, and only changes one major element at a time (headline, image, price display, checkout layout). Define success metrics upfront (conversion rate, AOV, abandonment), run tests to statistical significance, and avoid mid‑test tweaks that break UX or data quality.​

Use behavioral events (product views, adds, time on page, exits) to trigger flows in your marketing automation/CDP: cart abandonment journeys (email/SMS/push with cart contents), browse‑abandon reminders, and post‑purchase upsells based on items bought. Segment by value and intent so high‑value or high‑margin shoppers get more personalized sequences and recommendations.​

Common mistakes include obsessing over vanity metrics (raw traffic, followers) instead of conversion and profit, misconfigured tracking (broken dataLayer, wrong event scopes), and comparing apples to oranges across channels or industries. Others are running tests on tiny samples, ignoring cohort and margin context, and making big strategy calls from noisy or incomplete data.​