CACformula in one line: total customer acquisition costs divided by new customers acquired in the same period. The complexity is in what counts as "acquisition cost."
The short version: most teams overcomplicate this. Below is the actual sequence we run for clients, what works, what's a waste of time, and the order to do things in for compounding results.
The basic formula
CAC= (Ad Spend + Marketing Tools + Salaries of Marketing Team + Agency Fees) / New Customers Acquired.
For an ecommerce brand spending $50,000/month across paid media, $5,000 on tools, and $10,000 on marketing salaries, acquiring 1,200 new customers: CAC= $65,000 / 1,200 = $54.17.
What to include (and exclude)
- →Include: Paid ad spend across all channels, agency/freelancer costs, marketing tools (Klaviyo↗, Triple Whale, attribution), marketing team salaries, creative production.
- →Exclude: Product costs, fulfillment, customer service, returns, one-time setup costs.
- →Judgment call: Influencer gifting, PR, organic social content production, include if these drove new customers.
Paid vs blended CAC
Paid CAC= paid spend / new customers from paid. Blended CAC= all marketing spend / all new customers including organic. Report both. Paid CACtells you your channel efficiency. Blended CACtells you your true business economics.
CAC benchmarks by model
DTCecommerce: $30-80 is healthy for AOV $50-100 orders. Subscription: $60-150 acceptable if LTV exceeds $500. Luxury DTC: $150-400 given higher AOVs. B2B SaaS: $300-3,000 depending on ACV.
Calculate your CACmonthly using our LTV:CACcalculator linked above. More importantly, calculate payback period, how fast does CACcome back as contribution margin? Under 12 months is healthy; under 6 months means you should scale aggressively.
Frequently asked questions
Is this approach right for early-stage companies?
Most frameworks in this space assume a certain level of operational maturity, dedicated team members, established measurement infrastructure, some history of experimentation to build on. Pre-seed and seed-stage companies often lack these prerequisites and need a lighter-weight adaptation. For brands doing under $3M in annual revenue, focus on three or four of the principles that matter most for your specific business model rather than trying to implement the full framework at once. Rigor matters more than coverage at this stage.
How does this work for B2B versus B2C businesses?
The underlying principles around how to calculate cacapply across both contexts, but execution differs meaningfully. B2B unit economics typically has longer sales cycles, multiple stakeholders per deal, and consideration periods measured in months rather than minutes. Measurement frameworks need longer windows. Attributionbecomes more complex. The same core strategic logic applies, but the tactical implementation looks different. We've worked extensively in both contexts and can flex the approach accordingly.
What changes when we integrate this with existing systems?
Every implementation requires integration work, systems don't exist in isolation. Analytics platforms, CRM, email systems, ad accounts, BI tooling all need to talk to each other for this to work at scale. Plan for 2-4 weeks of integration work at the start of any implementation. Shortcutting this phase creates data quality issues that compound and undermine the entire program over 6-12 months. We've seen teams skip integration work to move faster, only to spend 6 months later reconciling measurement discrepancies that could have been prevented upfront.
When should we reconsider the approach?
Every 6 months, run a structured review against the principles outlined here. Ask whether the market has shifted meaningfully, whether your business model has evolved, whether competitive dynamics have changed. Frameworks should evolve with context. A rigid commitment to any specific approach, including ours, eventually becomes the problem rather than the solution. The teams that outperform long-term are the ones that update their operating model based on evidence, not the ones that defend past decisions.
.Statista, Global retail e-commerce sales 2014–2027Apply this: free unit economics tools.
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