Unit Economics Tracking

Unit Economics Tracking

Overview

Unit economics describes the financial dynamics of a single customer. The four key metrics — Customer Acquisition Cost (CAC), Customer Lifetime Value (LTV), the LTV:CAC ratio, and payback period — tell you whether your business model is economically viable. These metrics are among the most scrutinized by investors and boards because they reveal the underlying health of the business independent of short-term revenue trends.

This skill guides you through calculating and tracking these metrics using your platform's analytics tools and dedicated customer analytics apps.

When to Use This Skill

• When preparing investor materials and needing to present unit economics metrics
• When understanding whether it is profitable to increase marketing spend in a given channel
• When analyzing why CAC has increased over the past 6 months
• When comparing the quality of customers acquired through different channels
• When building a financial model and needing to validate LTV assumptions
• When setting budget guardrails: maximum allowable CAC by channel
• When evaluating a new acquisition channel and projecting its payback period

Core Instructions

Step 1: Choose your unit economics tracking tool by platform

Step 2: Calculate Customer Acquisition Cost (CAC)

CAC is the total marketing and sales spend required to acquire one new customer.

Types of CAC:

Getting CAC data by platform:

Shopify with Lifetimely: 1. Install Lifetimely from the Shopify App Store 2. Connect ad accounts (Meta, Google, TikTok) under Integrations 3. Go to Lifetimely → Channels — view CAC by acquisition channel with trend over time 4. Lifetimely tracks "new customers" based on first Shopify order date and attributes them to their first-touch UTM source

Shopify with Triple Whale: 1. Install Triple Whale and connect ad accounts 2. Go to Triple Whale → Summary Dashboard — "New Customer CAC" tile shows blended new customer acquisition cost 3. Go to Triple Whale → Attribution → New Customer Revenue for CAC by channel

Manual CAC calculation (any platform): 1. Export: Monthly marketing spend by channel (from your ad platforms or agency reports) 2. Export: New customer count by acquisition channel and month (from your platform's analytics: Shopify Analytics → New vs. returning customers; Metorik → Customers → First order date) 3. Calculate: CAC by channel = Channel spend / New customers attributed to that channel

Typical CAC benchmarks by channel:

• Google Shopping / Search: $15–$50 (lower for branded, higher for competitive non-branded)
• Meta (Facebook/Instagram): $20–$80 for DTC
• TikTok: $10–$40 (often lower for discovery-driven products)
• Influencer marketing: $15–$60 (highly variable)
• Email/SMS (acquisition via lead gen): $5–$20

Step 3: Calculate Customer Lifetime Value (LTV)

LTV is the total net revenue (or contribution margin) you expect from a customer over their relationship with your business.

Historical (observed) LTV: Pull this from your analytics tool directly.

Shopify + Lifetimely: 1. Go to Lifetimely → Cohorts — see a cohort table showing cumulative revenue per customer at 1, 3, 6, 12, 18, 24 months after acquisition 2. The month-12 and month-24 rows represent your observed LTV at those time horizons 3. Go to Lifetimely → Predicted CLV — Lifetimely's model predicts 12-month and 24-month CLV per customer based on their early purchase behavior

WooCommerce + Metorik: 1. Go to Metorik → Reports → Customer Cohorts — cohort retention table with cumulative revenue per customer by months since acquisition 2. Go to Metorik → Customers → Filter by: First order date range — view average revenue per customer for any acquisition cohort

Predicted LTV formula (for planning models):

When you do not have 12+ months of cohort data, use this simplified formula:

LTV = (Average Order Value × Purchase Frequency per Year × Gross Margin %) / Annual Churn Rate

Example:
  AOV: $65
  Purchase Frequency: 2.5 orders/year
  Gross Margin: 55%
  Annual Churn Rate: 40%

LTV = ($65 × 2.5 × 0.55) / 0.40 = $89.375 / 0.40 = $223.44

This formula gives steady-state LTV. It assumes stable purchase behavior, which is a simplification — cohort-based analysis is more accurate when you have the data.

Step 4: Calculate LTV:CAC ratio and payback period

LTV:CAC ratio:

LTV:CAC = LTV / CAC

Benchmarks:
  2:1 = Minimum viable (barely profitable customer acquisition)
  3:1 = Healthy for DTC ecommerce
  5:1+ = Excellent; strong case for scaling marketing spend

Payback period:

Payback period = CAC / (Monthly Contribution per Customer)

Example:
  CAC: $60
  AOV: $65
  Purchase Frequency: 2.5 orders/year → 0.21 orders/month
  Gross Margin: 55%
  Fulfillment + other variable costs: 15%
  Contribution margin rate: 40%

Monthly contribution per customer = $65 × 0.21 × 0.40 = $5.46
Payback period = $60 / $5.46 = 11 months

Payback benchmarks:

• <6 months: Excellent; very capital-efficient growth
• 6–12 months: Healthy
• 12–24 months: Acceptable if retention is strong past month 24
• 24+ months: Problematic unless you have significant venture/debt capital to bridge

Step 5: Track unit economics by acquisition channel

The most important dimension for unit economics is acquisition channel — customers from different sources often have dramatically different LTVs, repeat purchase rates, and initial AOVs.

Using Lifetimely (Shopify) to compare channels: 1. Go to Lifetimely → Channels — select "LTV comparison by channel" 2. View 12-month and 24-month LTV by first-touch acquisition source 3. Pair with CAC by channel (from ad platform spend) to calculate LTV:CAC by channel

Channel LTV comparison template:

Key insight from this type of analysis: Meta Prospecting appears to have a high ROAS in Meta's dashboard but has the worst LTV:CAC because those customers have lower repeat purchase rates. This is the power of LTV-based analysis over single-order ROAS.

Step 6: Monitor CAC trends weekly

Rising CAC is the first warning sign of channel saturation or increased competition. Monitor it weekly:

1. Set up a CAC alert in Triple Whale or Polar Analytics: alert when weekly new customer CAC exceeds your maximum allowable CAC by channel 2. Your maximum allowable CAC = LTV / Target LTV:CAC ratio

• Example: If LTV = $180 and target LTV:CAC = 3.0, max CAC = $60

3. Any channel where CAC exceeds the maximum allowable CAC should be reviewed before the next weekly budget cycle

Best Practices

• Use contribution margin LTV, not gross revenue LTV — LTV calculated on revenue overstates actual customer value; use contribution margin (after COGS, fulfillment, and variable costs) for a realistic economic picture
• Segment LTV by acquisition channel from day one — customers from organic search, paid social, and influencer partnerships often have dramatically different LTVs; pooling them into a blended LTV obscures channel economics
• Track LTV curves, not just point estimates — plot cumulative gross profit per customer over 24 months for each cohort; comparing curves across cohorts reveals whether recent cohorts are better or worse than historical averages
• Set maximum CAC guardrails for each channel — derive Max CAC = LTV / Target LTV:CAC ratio; use this as a hard budget guardrail so marketing teams cannot overpay for customers without executive approval
• Validate LTV predictions against cohort actuals — every 6 months, compare LTV predictions against the actual cumulative gross profit of cohorts that are now old enough to measure; recalibrate if predictions are consistently off
• Account for reactivation costs in long-tail LTV — customers who lapse and return via win-back campaigns have reactivation costs (discounts, extra email volume) that should reduce the apparent value of long-tail behavior

Common Pitfalls

Related Skills

• @customer-analytics
• @attribution-modeling
• @marketing-spend-analysis
• @financial-analytics-dashboard
• @ecommerce-budgeting-forecasting

Customer Cohort Profitability Analysis

Problem/Feature Description

Verdana Skincare has been running for three years and acquired customers through three channels: paid social advertising, organic/SEO traffic, and an email referral program. The VP of Marketing believes paid social is their most valuable channel based on volume, but the CFO suspects that organic customers spend more over time and may actually be worth more. They've been having the same debate for six months without resolution because nobody has built the analysis to settle it.

There's a secondary complication: the company ran two large win-back campaigns in 2024 targeting customers who hadn't purchased in over 9 months, offering 30% discounts to re-engage them. Some of these reactivated customers are now showing up in the "active customer" numbers, and leadership wants to understand whether those win-back investments are paying off or inflating the metrics.

Build a Python analysis tool using the provided transaction data that helps the team understand which customers are truly valuable and how customer quality has evolved across cohorts acquired over different time periods.

Output Specification

Write a Python script (cohort_analysis.py) that processes the provided data and writes a JSON report (cohort_report.json) containing:

• LTV analysis broken down by acquisition channel, with separate handling for reacquired customers vs new customers
• Cohort-level cumulative performance at months 1, 3, 6, and 12 after acquisition
• A comparison across cohorts showing whether customer quality is improving or declining over time
• A summary table of channel-level economics

Print a human-readable summary to stdout. Write full results to cohort_report.json.

Input Files

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/customers.json =============== { "customers": [ {"customer_id": "C001", "first_order_date": "2023-01-15", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C002", "first_order_date": "2023-01-22", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C003", "first_order_date": "2023-02-03", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C004", "first_order_date": "2023-02-14", "acquisition_channel": "email_referral", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C005", "first_order_date": "2023-03-05", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C006", "first_order_date": "2023-03-18", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C007", "first_order_date": "2023-04-09", "acquisition_channel": "email_referral", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C008", "first_order_date": "2023-04-22", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C009", "first_order_date": "2023-05-11", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C010", "first_order_date": "2023-06-01", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C011", "first_order_date": "2024-01-08", "acquisition_channel": "paid_social", "is_reacquired": true, "reactivation_cost": 28.50}, {"customer_id": "C012", "first_order_date": "2024-01-15", "acquisition_channel": "email_referral", "is_reacquired": true, "reactivation_cost": 22.00}, {"customer_id": "C013", "first_order_date": "2024-02-05", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C014", "first_order_date": "2024-02-19", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C015", "first_order_date": "2024-03-07", "acquisition_channel": "paid_social", "is_reacquired": true, "reactivation_cost": 31.00}, {"customer_id": "C016", "first_order_date": "2024-03-22", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C017", "first_order_date": "2024-04-14", "acquisition_channel": "email_referral", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C018", "first_order_date": "2024-05-02", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C019", "first_order_date": "2024-05-20", "acquisition_channel": "organic", "is_reacquired": false, "reactivation_cost": 0}, {"customer_id": "C020", "first_order_date": "2024-06-10", "acquisition_channel": "paid_social", "is_reacquired": false, "reactivation_cost": 0} ] }

=============== FILE: inputs/orders.json =============== { "orders": [ {"order_id": "O001", "customer_id": "C001", "order_date": "2023-01-15", "gross_revenue": 89.00, "contribution_margin": 38.50}, {"order_id": "O002", "customer_id": "C001", "order_date": "2023-04-20", "gross_revenue": 112.00, "contribution_margin": 47.00}, {"order_id": "O003", "customer_id": "C001", "order_date": "2023-08-10", "gross_revenue": 95.00, "contribution_margin": 41.00}, {"order_id": "O004", "customer_id": "C001", "order_date": "2024-01-05", "gross_revenue": 130.00, "contribution_margin": 55.50}, {"order_id": "O005", "customer_id": "C002", "order_date": "2023-01-22", "gross_revenue": 75.00, "contribution_margin": 32.00}, {"order_id": "O006", "customer_id": "C002", "order_date": "2023-03-15", "gross_revenue": 88.00, "contribution_margin": 37.50}, {"order_id": "O007", "customer_id": "C002", "order_date": "2023-06-02", "gross_revenue": 102.00, "contribution_margin": 44.00}, {"order_id": "O008", "customer_id": "C002", "order_date": "2023-09-18", "gross_revenue": 91.00, "contribution_margin": 39.00}, {"order_id": "O009", "customer_id": "C002", "order_date": "2024-02-14", "gross_revenue": 115.00, "contribution_margin": 49.00}, {"order_id": "O010", "customer_id": "C003", "order_date": "2023-02-03", "gross_revenue": 65.00, "contribution_margin": 27.00}, {"order_id": "O011", "customer_id": "C003", "order_date": "2023-07-22", "gross_revenue": 78.00, "contribution_margin": 33.00}, {"order_id": "O012", "customer_id": "C004", "order_date": "2023-02-14", "gross_revenue": 95.00, "contribution_margin": 41.50}, {"order_id": "O013", "customer_id": "C004", "order_date": "2023-05-08", "gross_revenue": 110.00, "contribution_margin": 47.50}, {"order_id": "O014", "customer_id": "C004", "order_date": "2023-08-30", "gross_revenue": 88.00, "contribution_margin": 38.00}, {"order_id": "O015", "customer_id": "C005", "order_date": "2023-03-05", "gross_revenue": 82.00, "contribution_margin": 35.50}, {"order_id": "O016", "customer_id": "C005", "order_date": "2023-06-14", "gross_revenue": 97.00, "contribution_margin": 42.00}, {"order_id": "O017", "customer_id": "C005", "order_date": "2023-10-05", "gross_revenue": 110.00, "contribution_margin": 47.50}, {"order_id": "O018", "customer_id": "C005", "order_date": "2024-03-20", "gross_revenue": 125.00, "contribution_margin": 53.50}, {"order_id": "O019", "customer_id": "C006", "order_date": "2023-03-18", "gross_revenue": 70.00, "contribution_margin": 29.00}, {"order_id": "O020", "customer_id": "C006", "order_date": "2023-09-25", "gross_revenue": 85.00, "contribution_margin": 36.50}, {"order_id": "O021", "customer_id": "C007", "order_date": "2023-04-09", "gross_revenue": 98.00, "contribution_margin": 42.50}, {"order_id": "O022", "customer_id": "C007", "order_date": "2023-07-18", "gross_revenue": 115.00, "contribution_margin": 49.50}, {"order_id": "O023", "customer_id": "C007", "order_date": "2023-11-22", "gross_revenue": 102.00, "contribution_margin": 44.00}, {"order_id": "O024", "customer_id": "C008", "order_date": "2023-04-22", "gross_revenue": 88.00, "contribution_margin": 38.00}, {"order_id": "O025", "customer_id": "C008", "order_date": "2023-08-14", "gross_revenue": 105.00, "contribution_margin": 45.00}, {"order_id": "O026", "customer_id": "C008", "order_date": "2024-01-30", "gross_revenue": 118.00, "contribution_margin": 50.50}, {"order_id": "O027", "customer_id": "C009", "order_date": "2023-05-11", "gross_revenue": 72.00, "contribution_margin": 30.00}, {"order_id": "O028", "customer_id": "C009", "order_date": "2023-11-08", "gross_revenue": 89.00, "contribution_margin": 38.00}, {"order_id": "O029", "customer_id": "C010", "order_date": "2023-06-01", "gross_revenue": 91.00, "contribution_margin": 39.50}, {"order_id": "O030", "customer_id": "C010", "order_date": "2023-09-22", "gross_revenue": 108.00, "contribution_margin": 46.50}, {"order_id": "O031", "customer_id": "C010", "order_date": "2024-02-08", "gross_revenue": 122.00, "contribution_margin": 52.00}, {"order_id": "O032", "customer_id": "C011", "order_date": "2024-01-08", "gross_revenue": 68.00, "contribution_margin": 24.50}, {"order_id": "O033", "customer_id": "C011", "order_date": "2024-05-14", "gross_revenue": 82.00, "contribution_margin": 31.50}, {"order_id": "O034", "customer_id": "C012", "order_date": "2024-01-15", "gross_revenue": 75.00, "contribution_margin": 30.00}, {"order_id": "O035", "customer_id": "C013", "order_date": "2024-02-05", "gross_revenue": 58.00, "contribution_margin": 22.50}, {"order_id": "O036", "customer_id": "C013", "order_date": "2024-06-18", "gross_revenue": 70.00, "contribution_margin": 28.00}, {"order_id": "O037", "customer_id": "C014", "order_date": "2024-02-19", "gross_revenue": 84.00, "contribution_margin": 36.00}, {"order_id": "O038", "customer_id": "C014", "order_date": "2024-07-05", "gross_revenue": 98.00, "contribution_margin": 42.00}, {"order_id": "O039", "customer_id": "C015", "order_date": "2024-03-07", "gross_revenue": 65.00, "contribution_margin": 22.00}, {"order_id": "O040", "customer_id": "C016", "order_date": "2024-03-22", "gross_revenue": 79.00, "contribution_margin": 34.00}, {"order_id": "O041", "customer_id": "C017", "order_date": "2024-04-14", "gross_revenue": 92.00, "contribution_margin": 40.00}, {"order_id": "O042", "customer_id": "C018", "order_date": "2024-05-02", "gross_revenue": 62.00, "contribution_margin": 24.00}, {"order_id": "O043", "customer_id": "C019", "order_date": "2024-05-20", "gross_revenue": 77.00, "contribution_margin": 33.00}, {"order_id": "O044", "customer_id": "C020", "order_date": "2024-06-10", "gross_revenue": 55.00, "contribution_margin": 21.00} ] }

Marketing Spend Efficiency Audit Tool

Problem/Feature Description

Northbrook Goods is a direct-to-consumer home goods brand that has been growing rapidly over the past two years across three acquisition channels: paid social (Meta/TikTok), Google Search, and an influencer affiliate program. The CFO is preparing for a Series B pitch and has asked the growth team to produce a rigorous analysis of whether their customer acquisition spending is actually efficient. In past fundraises, investors pushed back hard on the CAC figures because the team was only counting ad spend — they want to avoid that conversation this time.

The team also wants to operationalize their budget planning. Right now, channel managers can increase budgets freely as long as they hit volume targets, but leadership suspects they may be overpaying for some channels relative to what those customers are actually worth. They want hard limits baked into the planning process, and an alert mechanism so the finance team knows when acquisition costs are drifting.

Output Specification

Build a Python script (cac_audit.py) that accepts the sample cost data below and produces a JSON report (cac_report.json) containing:

• CAC figures for each channel and in aggregate, broken down by cost component
• Channel-level and blended metrics for each period in the input data
• A channel-level budget guardrail calculation given a target LTV:CAC ratio (use 3.0 as the target)
• A trend analysis across periods, including any warning flags for significant cost increases
• A health assessment for each channel based on standard industry benchmarks

The script should print a human-readable summary to stdout and write the full results to cac_report.json.

Input Files

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/cost_data.json =============== { "ltv_by_channel": { "paid_social": 185.00, "google_search": 210.00, "influencer": 145.00 }, "periods": [ { "period": "2025-Q3", "channels": { "paid_social": { "media_spend": 42000, "agency_fees": 5000, "marketing_tech_costs": 1200, "marketing_payroll_allocated": 8000, "first_order_discount_cost": 3100, "new_customers_paid": 310, "new_customers_organic": 95 }, "google_search": { "media_spend": 28000, "agency_fees": 2500, "marketing_tech_costs": 800, "marketing_payroll_allocated": 4500, "first_order_discount_cost": 900, "new_customers_paid": 210, "new_customers_organic": 0 }, "influencer": { "media_spend": 15000, "agency_fees": 3000, "marketing_tech_costs": 400, "marketing_payroll_allocated": 2500, "first_order_discount_cost": 2200, "new_customers_paid": 180, "new_customers_organic": 30 } } }, { "period": "2025-Q4", "channels": { "paid_social": { "media_spend": 51000, "agency_fees": 5500, "marketing_tech_costs": 1200, "marketing_payroll_allocated": 8000, "first_order_discount_cost": 4200, "new_customers_paid": 325, "new_customers_organic": 88 }, "google_search": { "media_spend": 34000, "agency_fees": 2500, "marketing_tech_costs": 800, "marketing_payroll_allocated": 4500, "first_order_discount_cost": 980, "new_customers_paid": 215, "new_customers_organic": 0 }, "influencer": { "media_spend": 18500, "agency_fees": 3200, "marketing_tech_costs": 400, "marketing_payroll_allocated": 2500, "first_order_discount_cost": 2700, "new_customers_paid": 172, "new_customers_organic": 28 } } } ] }

LTV Forecasting Model for Investor Due Diligence

Problem/Feature Description

Elevate Commerce is a hybrid ecommerce company with two customer segments: subscribers who pay $29/month for a curated product box, and one-time transactional buyers who purchase irregularly based on seasonal promotions. The company is preparing for a growth equity raise and their lead investor has asked for a defensible LTV model as part of due diligence. In a prior preliminary meeting, the investor specifically called out that most DTC companies "make up" their LTV numbers by projecting far beyond their data, and that they want to see the methodology clearly documented.

The company has 24 months of transaction data for subscribers and 18 months for transactional customers. The Head of Finance wants a Python-based LTV model that produces credible, investor-ready forecasts — one that a skeptical investor could scrutinize and trust. The model must include a validation mechanism using historical cohort predictions already in the data so the Finance team can demonstrate the model's track record and reliability.

Output Specification

Write a Python script (ltv_model.py) that reads the provided input data and produces a JSON report (ltv_report.json) containing:

• LTV predictions for both subscriber and transactional customer segments at the 12, 24, and 36 month horizons
• A validation section comparing prior LTV predictions (provided in the input) against actual cohort outcomes
• A summary of model performance and recommendations for improvement

Print a human-readable summary to stdout and write full results to ltv_report.json.

Input Files

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/model_inputs.json =============== { "subscribers": { "segment": "subscription", "monthly_revenue_per_customer": 29.00, "contribution_margin_rate": 0.52, "monthly_churn_rate": 0.045, "data_months_available": 24, "discount_rate_annual": 0.10, "cohort_size": 280 }, "transactional": { "segment": "transactional", "avg_order_value": 87.50, "purchases_per_year": 2.8, "gross_margin_rate": 0.43, "annual_churn_rate": 0.38, "data_months_available": 18, "discount_rate_annual": 0.10, "cohort_size": 640 }, "prior_predictions": [ { "cohort": "2023-Q1", "segment": "subscription", "predicted_ltv_12m": 148.00, "actual_cumulative_margin_12m": 131.50, "months_observed": 12 }, { "cohort": "2023-Q1", "segment": "transactional", "predicted_ltv_12m": 95.00, "actual_cumulative_margin_12m": 88.20, "months_observed": 12 }, { "cohort": "2023-Q2", "segment": "subscription", "predicted_ltv_12m": 152.00, "actual_cumulative_margin_12m": 127.80, "months_observed": 12 }, { "cohort": "2023-Q3", "segment": "subscription", "predicted_ltv_12m": 155.00, "actual_cumulative_margin_12m": 119.40, "months_observed": 12 }, { "cohort": "2023-Q3", "segment": "transactional", "predicted_ltv_12m": 98.00, "actual_cumulative_margin_12m": 82.60, "months_observed": 12 } ] }