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In weekly use at ChicknLegs
One view of marketing performance, built for budget decisions.
At ChicknLegs, a DTC running-apparel brand, I run paid media, email and launches, and I built the reporting leadership uses every week to decide where the next marketing dollar goes. It grew from a first-weeks baseline workbook into an AI-assisted dashboard.
Interactive dashboard demo
Non-brand ROAS, indexed
+14% on the prior 28 days, measured on Shopify orders, with Performance Max focused on prospecting.
Indexed to the prior 28 days = 100. The change is real; absolute values are not shown.
Email revenue, indexed
The last 28 days ran 852% ahead of the same period in 2025.
Indexed to the same 28 days of 2025 = 100. The change is real; absolute values are not shown.
Instagram followers, 7 days
100K → 200K in one week. I traced the spike to a single post with Instagram Insights.
The start and end counts are real. The daily path in between is illustrative.
How it fits together
Seven sources, one view.
Paid media, ecommerce, site, email, social and launch data sit in one view, with Shopify orders as the source of truth, so "is paid working?" has one clear answer.
What's inside
Brand vs non-brand ROAS
Splits paid revenue into demand captured and demand created, against a break-even line.
Budget pacing
Month-to-date spend by channel against plan, with projected run-rate.
Search terms & auctions
Search themes, competitor auction insights and brand impression share in one view.
Cohorts & LTV
Value by first-order channel, repeat rates and payback against acquisition cost.
Email & list growth
Klaviyo revenue and subscriber growth alongside paid and organic.
Organic social
Followers and growth by platform from the Sprout Social API.
Change annotations
Every major change is flagged on the charts so cause and effect stay visible.
Brand protection
Tracks lookalike storefronts through abuse reports, DMCA notices and follow-up alerts.
Inventory & top products
Stock levels next to what paid media is promoting, so spend follows what is in stock.
AI weekly digest
An AI-written weekly summary plus to-dos, launches and the marketing inbox.
Impact
Decisions the reporting shaped
- 01
Focused Performance Max on new-customer acquisition
Search-term reporting showed where Performance Max spend was going. I moved brand demand into its own dedicated campaign and pointed PMax at non-brand prospecting. Non-brand ROAS is up 14% on the prior 28 days, with cost per new customer steady.
- 02
Set a paid media mix policy built for growth
Leadership adopted a target channel mix that puts more of every dollar into creating new demand, with brand search funded to protect branded sales.
- 03
Gave leadership one ROAS number with a clear method
Attribution runs on Shopify orders, brand and non-brand are reported separately, and a toggle shows platform-reported numbers side by side, so every number in the weekly email can be explained.
- 04
Put break-even, LTV and payback in front of the founder
Unit economics became a break-even ROAS line, 12-month LTV by first-order channel, LTV:CAC and target ROAS by campaign type, plus a geo-holdout test design to measure incrementality.
- 05
Brought brand protection into the leadership view
Lookalike storefronts are tracked as a pipeline with takedown status and follow-up alerts.
Try the math yourself: the break-even ROAS and LTV:CAC calculator uses the same formulas.
Timeline
- May–Jun 2026Baseline workbook
Brand vs non-brand, ROI after fees and margin, channel scale readiness and a scored list of new channels to test.
- Jul 2026Platform data intake
Merged Meta, Google Ads, Klaviyo, GA4 and affiliate exports; auto-tagged every campaign brand, non-brand or mixed.
- Aug 2026Weekly recap for the VP
Consolidated ROAS, spend, week-over-week change and cost per new customer by channel.
- Sep 2026Live dashboard for leadership
AI-assisted dashboard with live data from paid media, Shopify, GA4, Klaviyo, Sprout Social and Monday.com.
- Oct 2026Budget policy & Q4 plan
Target channel mix, LTV-based ROAS targets and a Black Friday VIP segment built from cohort data.