This is Part 1 of a four-part series breaking down the Growth Intelligence framework for DTC brands. The full framework covers 10 metrics across four clusters. This article goes deep on the first two: the acquisition economics that determine whether growth is profitable before anything else matters.
Most DTC brands track blended ROAS — pull a monthly revenue number, divide by ad spend, move on. It’s a reasonable starting point. But the more valuable questions tend to go unanswered: which customer cohorts are actually contribution-margin positive by Month 3? Which acquisition channels produce buyers who repurchase versus buyers who came for the discount and didn’t come back? These aren’t failures of execution — they’re gaps in the measurement layer that most teams haven’t had the time or tooling to close.
That’s the gap these two metrics close. Revenue cohorts show you where margin actually comes from. CAC payback tells you how long you’re financing each customer before they pay you back.
Quick glossary: Contribution margin (CM) = revenue minus variable costs (COGS, shipping, discounts, payment processing). Cohort = a group of customers acquired in the same time period, tracked over time. Micro-segment = a cohort further sliced by channel, first product, or AOV band.
Metric 1: Revenue Cohorts by Micro-Segment
What it measures: How contribution margin from acquired customer cohorts evolves over time, segmented by acquisition channel, product category, and customer type. Not just revenue, but actual profit after variable costs.
Why revenue alone misleads: A cohort acquired through a 40%-off Meta campaign might show strong Month 1 revenue. By Month 6, when the discount buyers haven’t returned, that cohort is underwater. A smaller organic cohort with no discount shows lower Month 1 but compounds from there. You can’t see this without contribution margin.
Segmentation dimensions: Acquisition month, acquisition channel (Meta, TikTok, Google, organic, influencer, affiliate), first product purchased, first order AOV band (under $30, $30-60, $60-100, $100+).
What good looks like for a $5-50M DTC brand:
- Month 3 retention (repeat purchase rate): 25-35%. The average online retailer sees about 28% repeat purchase rate (Shopify, 2024).
- Month 12 cumulative revenue per customer: 1.8-2.5x first order value for non-subscription; 4-8x for subscription (Recharge State of Subscriptions, 2024).
- Contribution margin positive by cohort: Top cohorts should be CM-positive within 60-90 days.
- Segment concentration: 2-3 micro-segments typically generate 60-80% of profitable repeat revenue. Find those segments, double down.
The subscription vs. one-time split: Subscription brands like AG1 and Huel show cohort structures where roughly two-thirds of revenue comes from subscribers acquired more than 3 months ago. Their curves ramp and hold. One-time-purchase brands like Liquid Death ($333M revenue in 2024, 133K+ retail locations) and Olipop have flatter cohort curves because each purchase is a discrete decision, so the curve depends on replenishment and reactivation. Neither is wrong. But you build retention strategy differently for each.
The COGS gap everyone ignores: Shopify doesn’t natively store product-level COGS. Without COGS data, you’re running cohort analysis on revenue, which tells you less than half the story. A $60 AOV skincare order at 75% gross margin looks the same as a $60 food order at 40% margin in a revenue-only cohort. The skincare cohort can tolerate 2x the CAC.
Where the data lives
The raw inputs sit in platforms you already pay for. Shopify has your order history, customer records, and discount usage. Spend data lives in Meta Ads Manager, Google Ads, and TikTok Ads. The missing piece for most brands is COGS: that comes from your ERP (NetSuite, Cin7, ShipBob) or a manually maintained SKU-level cost table.
The problem is none of these platforms talk to each other natively, and none of them can run a contribution-margin cohort analysis on their own. The SaaS analytics tools that promise to do this for you cost $500-2,000/month, lock you into their data model, and bundle dozens of features you’ll never open. You’re paying for their roadmap, not yours.
A warehouse approach (BigQuery + dbt) pulls orders, spend, and COGS into one place and builds exactly the cohort views your business needs. Nothing more, nothing less. With modern tooling and AI-assisted SQL, the build is faster and cheaper than it was even two years ago. This is Phase 3-4 work in our implementation roadmap.
Metric 2: CAC Payback by Micro-Segment
What it measures: The number of days required for a customer’s cumulative contribution margin to exceed the cost of acquiring them. Not when they “pay for themselves” in revenue, but when they pay for themselves in profit.
Formula:
CAC Payback (Days) = Blended CAC per New Customer / Average Monthly Contribution Margin per Customer
What good looks like by category:
- Subscription brands (supplements, coffee, pet food): Payback within the first order or within 60 days. AG1 spends an estimated $2.2M/month on podcast ads alone, and analysts note they don’t break even on a new subscriber for over a year. But $79+ AOV and strong retention make the math work long-term.
- Consumable one-time purchase (snacks, beverages): 90-180 days. Brands like Olipop or Magic Spoon need 2-3 repeat purchases over 4-6 months.
- Beauty/personal care: 60-120 days. 70-80% gross margins make faster payback achievable even with moderate repeat rates.
The 3:1 threshold: A 3:1 or better LTV:CAC ratio is the standard benchmark. Below 3:1, you’re either overspending on acquisition or underperforming on retention. Most brands tracking only blended ROAS never see the segment-level breakdown. A 4x blended ROAS can hide a 1.5x segment that’s burning cash.
2024 benchmarks:
- Median blended ROAS: 2.04x (Triple Whale, 30K ecommerce brands). This is the industry-wide number, not a target.
- Healthy MER for growth-stage brands: 3-5x. Below 3x typically means burning cash on acquisition.
- Category-specific MER targets: F&B 3.5-5x (lower gross margins require higher efficiency), Beauty 2.5-4x (higher margins allow lower MER), Supplements 3-4.5x.
The margin translation that matters: A MER of 2.0 for a food brand at 50% gross margin translates to a GER of roughly 1.0, which is breakeven. The same MER for a beauty brand at 75% margins produces GER of ~1.5. MER without margin context is a vanity metric.
What changes when you segment: The blended number hides everything. When you break CAC payback by channel, you’ll typically find organic and referral customers pay back in under 30 days while paid social takes 90-180. That’s not an argument against paid. It’s an argument for knowing the ratio so you can set channel budgets that the business can actually sustain.
Where the data lives
Channel-level spend comes from your ad platform dashboards. Customer-level revenue comes from Shopify. Subscription data comes from Recharge or Skio. The challenge is joining those into a single view where you can calculate cumulative CM per customer against their acquisition cost, broken out by segment.
You can get a rough blended CAC from a spreadsheet. But the segment-level view, CAC payback by first product, AOV band, or acquisition channel, requires a warehouse. The query itself is straightforward once orders are joined to attribution data: cumulative contribution margin per customer plotted against acquisition cost, grouped by whatever dimension matters. That specificity is what you’re paying for with a warehouse build, and it’s exactly the specificity that off-the-shelf tools flatten into a blended average.
What These Two Metrics Give You
Revenue cohorts tell you which customers are worth acquiring. CAC payback tells you how long the business needs to finance that acquisition. Together they answer the only question that matters in DTC acquisition: are we making money on the customers we’re buying?
Most brands can answer this at a blended level. The segment-level answer is what separates brands that scale profitably from brands that scale into a cash crisis.
Next in this series: Activation Economics — the metrics that predict which first-time buyers become repeat customers, and the touchpoints that make it happen.
