Unit Economics

CAC is the total cost of convincing a potential customer to buy your product. This includes all marketing spend, sales team salaries, tools, and overhead directly tied to acquiring new customers. The formula: CAC = Total Sales & Marketing Spend ÷ New Customers Acquired. A company spending $50K/month on marketing and sales and acquiring 100 customers has a $500 CAC. CAC varies dramatically by channel — paid ads might be $300 CAC while organic content is $30. VCs obsess over CAC because it determines unit economics: if CAC exceeds LTV, every customer you acquire destroys value.

CAC = Total Sales & Marketing Spend ÷ New Customers Acquired

The LTV:CAC ratio compares how much a customer is worth over their lifetime to how much it costs to acquire them. It is the single most important ratio for determining whether your business model is fundamentally viable. The golden benchmark is 3:1 — each customer generates 3x what you spent to acquire them. Below 1:1, you're paying more to acquire customers than they'll ever generate. Between 1:1 and 3:1, you're viable but thin. Above 5:1, you may be under-investing in growth — competitors who spend more can outpace you.

LTV:CAC Ratio = LTV ÷ CAC (target: 3:1)

Lifetime Value is the total revenue you can expect from a single customer over the entire duration of your relationship. It is the most critical number for understanding how much you can afford to spend on acquiring customers. The simplest formula: LTV = ARPU ÷ Monthly Churn Rate. A customer paying $100/month with 5% monthly churn has an LTV of $2,000. Netflix's LTV exceeds $1,200 per subscriber because churn is below 2.5% — this justifies their $17B+ annual content spend. LTV is the roof of your building: it determines the maximum CAC you can afford, the features you can build, and the team you can hire.

LTV = ARPU ÷ Monthly Churn Rate

The CAC Payback Period is how many months it takes for a customer to generate enough gross profit to cover the cost of acquiring them. It measures how quickly your business recoups its marketing investment. Formula: CAC ÷ (ARPU × Gross Margin). If your CAC is $600, ARPU is $100/month, and gross margin is 80%, payback = $600 ÷ ($100 × 0.80) = 7.5 months. VCs care about this as much as LTV:CAC because it determines your cash efficiency — a business with 3-month payback can reinvest acquisition dollars 4x per year, while a 12-month payback business can only reinvest once.

Payback Period = CAC ÷ (ARPU × Gross Margin %)

Unit economics is the direct revenue and costs associated with a single 'unit' of your business model (usually one customer). If your unit economics are positive, every new customer generates profit. If negative, every new customer accelerates your death. The core calculation: Unit Profit = (LTV × Gross Margin) − CAC. If LTV is $2,000, gross margin is 80%, and CAC is $1,200, unit profit is ($2,000 × 0.80) − $1,200 = $400 per customer. This means each customer eventually contributes $400 toward covering fixed costs and generating profit.

Unit Profit = (LTV × Gross Margin) − CAC

Cohort analysis groups customers by their signup date (or another shared attribute) and tracks their behavior over time. Instead of looking at blended metrics that mask trends, you see how each 'class' of customers performs independently. A SaaS company with 5% monthly churn might discover that January cohort churns at 3% while March cohort churns at 9% — the blended 5% hides a deteriorating acquisition quality problem. Amplitude found that companies using cohort analysis identify retention problems 6-8 weeks earlier than those using aggregate metrics.

Cohort Retention Rate = (Active Users in Cohort at Month N ÷ Total Users in Cohort at Month 0) × 100

Expansion revenue is additional revenue generated from existing customers through upsells, cross-sells, add-ons, or usage growth — without acquiring a single new customer. It's the engine behind Net Revenue Retention above 100%. If your existing customer base generated $100K last month and generates $108K this month with no new sales, you have $8K in expansion revenue (8% expansion rate). Snowflake's 158% NRR is almost entirely driven by usage-based expansion — their customers spend more every quarter as their data volumes grow.

Expansion Rate = (Expansion MRR ÷ Beginning MRR) × 100

Contribution margin measures how much revenue from each unit sold contributes to covering fixed costs and generating profit after variable costs are subtracted. If you sell a subscription for $100/month and the variable costs (hosting, support, payment processing) are $20/month, your contribution margin is $80 (80%). This is the TRUE profit engine — every additional dollar of revenue at 80% CM adds $0.80 directly toward covering fixed costs. Once fixed costs are covered, contribution margin becomes pure profit. DoorDash operated at negative contribution margin for years (-$1.50 per order in 2019), meaning they lost money on every single delivery before even counting corporate overhead.

Contribution Margin = Revenue per Unit − Variable Costs per Unit | CM% = (CM ÷ Revenue) × 100

CAC by Channel breaks your blended customer acquisition cost into its constituent channels — paid search, paid social, content/SEO, referrals, outbound sales, partnerships — so you can see which channels are actually profitable and which are silently bleeding cash. Blended CAC is a marketing vanity number; channel-level CAC is the operational truth. Formula per channel: CAC_channel = (Channel Spend + Allocated Headcount + Tools) ÷ Customers Acquired via that Channel. The KnowMBA POV: blended CAC almost always hides addiction to one expensive channel that subsidizes (or destroys) the average. The day Google or Meta auction prices spike 30%, undiagnosed channel mix becomes an existential problem.

CAC by Channel = (Channel Marketing Spend + Allocated Sales Cost + Tools) ÷ New Customers Acquired via Channel

Blended CAC includes ALL customers (organic, referral, paid, word-of-mouth) divided by ALL acquisition costs. Paid CAC includes ONLY customers acquired via paid channels divided by paid spend. The gap between them is your free-acquisition leverage. If blended CAC is $80 and paid CAC is $400, your organic engine is doing the heavy lifting. The KnowMBA POV: blended CAC is what you tell the board; paid CAC is what tells you the truth. Most growth-stage companies obsess over the prettier blended number — and then panic when paid auction prices rise and they realize they have no organic moat.

Blended CAC = Total S&M Spend ÷ Total New Customers | Paid CAC = Paid Spend ÷ Paid Customers Only

LTV by Segment splits your average customer lifetime value into the meaningful subgroups: industry, plan tier, company size, geography, acquisition channel, or behavioral cohort. The KnowMBA POV: average LTV is a lie told by spreadsheets. Real businesses have one segment doing 5x the LTV of another, and you should be acquiring the first relentlessly while questioning whether to acquire the second at all. Formula: LTV_segment = ARPU_segment × Gross Margin_segment ÷ Churn Rate_segment. Segmented LTV reveals which customers actually pay for themselves — and which ones are subsidized by everyone else.

LTV by Segment = ARPU(segment) × Gross Margin(segment) ÷ Churn Rate(segment)

Gross Margin by Product breaks blended company gross margin into per-SKU, per-product-line, or per-cloud margins so you can see which products are subsidizing which. Formula per product: (Revenue_p − COGS_p) ÷ Revenue_p. The KnowMBA POV: blended gross margin is a financial press release; per-product gross margin is the operating decision. A 70% blended gross margin can hide a 90%-margin SaaS subscription propping up a 30%-margin services line that's burning operations time. Founders who don't decompose gross margin scale the wrong revenue.

Gross Margin by Product = (Product Revenue − Product COGS) ÷ Product Revenue × 100%

Variable costs scale with each unit sold (hosting per user, payment processing, fulfillment, support per ticket). Fixed costs stay constant regardless of unit volume (rent, salaries, software subscriptions). The ratio between them defines your operating leverage and break-even point. Formula: Total Cost = Fixed Costs + (Variable Cost per Unit × Units). KnowMBA POV: high-fixed-cost businesses are a coiled spring — losses below break-even are brutal, but profits above it explode. High-variable-cost businesses are smooth and forgiving but never have a magical breakout quarter. Choose your cost structure deliberately, not accidentally.

Total Cost = Fixed Costs + (Variable Cost per Unit × Units Sold)

Marginal cost is what it costs to produce ONE MORE unit — not the average cost across all units. Formula: Marginal Cost = ΔTotal Cost ÷ ΔUnits. The KnowMBA POV: average cost is what you report; marginal cost is what determines whether your next decision creates or destroys value. The marginal cost of a 1,001st AWS API request is fractions of a cent. The marginal cost of a 1,001st enterprise customer might require a new account executive ($200K/year). Average cost is a backward-looking accounting metric; marginal cost is the forward-looking decision metric.

Marginal Cost = Change in Total Cost ÷ Change in Units (typically Δ over the next 1,000 units)

Contribution per Customer is the dollars each customer leaves on the table after their direct variable costs — the cash that flows to covering fixed costs and (eventually) profit. Formula: Contribution per Customer = ARPU − Variable Cost per Customer (hosting, support, payment fees, fulfillment per that customer). Distinct from gross margin because it's per-customer, not per-product — which lets you see which CUSTOMERS (not products) are paying for themselves. The KnowMBA POV: averaging contribution across customers hides the fact that 30% of your customer base often consumes more support cost than they pay in revenue. Find the parasites and price them out — or fire them.

Contribution per Customer = ARPU − (Hosting + Support + Payment Fees + Other Variable Costs per Customer)

Sales Efficiency Ratio (also called the 'Magic Number') measures how many dollars of new ARR you get for each dollar spent on sales and marketing. Formula: Magic Number = (Net New ARR in Quarter × 4) ÷ S&M Spend in Prior Quarter. A Magic Number of 1.0 means $1 of S&M generates $1 of net-new ARR (12-month payback). Above 1.0 = invest more. Below 0.75 = pull back. KnowMBA POV: this single ratio decides whether to step on the gas or hit the brakes. Founders who pour money into S&M with a 0.4 Magic Number are subsidizing growth — VCs in 2024-2026 will not bail them out as readily as 2021 VCs did.

Magic Number = (Net New ARR in Quarter × 4) ÷ S&M Spend in Prior Quarter

Burn Multiple, popularized by David Sacks in 2020, measures how much cash you burn for every $1 of new ARR you add. Formula: Burn Multiple = Net Burn ÷ Net New ARR. A Burn Multiple of 1.0 means you burned $1 to add $1 of ARR. The KnowMBA POV: in 2026, Burn Multiple has displaced 'revenue growth' as the dominant capital-efficiency lens. The 2021 era of 'growth at any cost' is dead — investors now ask 'how many dollars did you light on fire to get that growth?' before celebrating it. A company growing 100% at 5x Burn Multiple is worse than a company growing 40% at 1x.

Burn Multiple = Net Cash Burn ÷ Net New ARR (both for the same period, typically annualized)

SaaS Quick Ratio, framed by Tomasz Tunguz (Theory Ventures, formerly Redpoint), measures the quality of your ARR growth by comparing what you GAINED (new + expansion) against what you LOST (contraction + churn). Formula: Quick Ratio = (New ARR + Expansion ARR) ÷ (Churned ARR + Contraction ARR). A Quick Ratio of 4 means you added $4 for every $1 you lost. The KnowMBA POV: net new ARR and revenue growth tell you the score; Quick Ratio tells you HOW you got there. A company with great net growth but a Quick Ratio of 1.2 is treading water — they're acquiring as fast as they're leaking, which breaks the moment growth slows.

Quick Ratio = (New ARR + Expansion ARR) ÷ (Churned ARR + Contraction ARR)

Hardware margin engineering is the discipline of designing physical products so that their bill of materials (BOM), assembly, packaging, freight, warranty, and reverse logistics together leave room for a defensible gross margin. Unlike software, hardware margin is locked in at the design stage — once you commit to a chassis, a chipset, a hinge, a cell chemistry, the cost structure is mostly frozen. Apple targets 35-40%+ hardware gross margin and engineers the supply chain to deliver it: vertical integration of silicon, multi-year tooling investments, and volume commitments that crush per-unit cost. Tesla took years to drag Model 3 gross margin from negative to ~25% through battery cost reduction, vertical integration, and manufacturing yield improvements.

Landed Gross Margin = (Net ASP − BOM − Assembly − Freight − Tariffs − Warranty Reserve − Returns Reserve − Channel Margin − Tooling Amortization) ÷ Net ASP

Take rate is the percentage of gross merchandise value (GMV) that a marketplace keeps as revenue. It is the central economic dial of every two-sided platform. Uber takes ~25-30% of every ride. Airbnb takes ~14-16% of every booking (split between host and guest fees). Etsy takes ~10% via transaction fees, payment fees, and ad fees combined. eBay takes ~13% blended. Take rate determines whether a marketplace can fund acquisition, support, fraud prevention, and platform R&D — but raise it too high and supply or demand defects to a competitor. The take rate ceiling is set by what the marketplace uniquely delivers: trust, demand, distribution, payments, fulfillment, or insurance.

Take Rate = Platform Revenue ÷ GMV | Effective Take Rate = (Total Net Revenue − Supply Incentives) ÷ GMV

SaaS implementation margin is the gross margin earned on the professional services revenue that customers pay for onboarding, configuration, integration, training, and customization. For enterprise SaaS like Workday or Salesforce, implementation revenue can be 15-30% of total revenue, but the margin profile is dramatically different from subscription revenue. Subscription revenue carries 75-85% gross margin. Implementation revenue typically carries 10-30% gross margin — and at many SaaS companies, it operates at break-even or a small loss. Healthy implementation margin signals two things: pricing discipline (PS is not given away to close subscription deals) and product maturity (low-touch onboarding requires less services labor).

Services Gross Margin = (Services Revenue − Services COGS) ÷ Services Revenue | Services Mix = Services Revenue ÷ Total Revenue

Services revenue mix is the percentage of total company revenue that comes from professional services, implementation, training, or custom development — versus the percentage that comes from product/subscription. The mix is one of the most informative metrics for valuing a company. Pure SaaS companies (Snowflake, Datadog, Atlassian) report under 5% services mix. Enterprise SaaS with heavy implementations (Workday, Veeva) run 15-20%. Hybrid companies (Palantir until recently, ServiceNow's early years) ran 30-50%. Pure consulting firms (Accenture) are 95%+ services. Wall Street values $1 of subscription revenue at 8-15× revenue and $1 of services revenue at 1-3× revenue — so a company with 30% services mix is worth dramatically less than the same revenue at 5% services mix. Investors price the mix, not just the absolute number.

Services Mix = Services Revenue ÷ Total Revenue × 100 | Mix Trajectory = (Current Quarter Mix − Year-Ago Mix)

Per-seat pricing charges a fixed amount per user per month (Slack at $7.25/user, HubSpot Sales Hub at $90/seat, Zoom Pro at $14.99/host). Usage-based pricing charges based on consumption — API calls, queries, GB stored, transactions processed (Snowflake per credit, Datadog per host, AWS per instance hour, OpenAI per token). The two models produce dramatically different unit economics: per-seat delivers high revenue predictability, low expansion variance, and easier sales motion — but caps growth at workforce size. Usage-based unlocks unbounded expansion (a customer's bill can grow 10× in a year if they use more) but introduces revenue volatility that destroys forecasting accuracy. The KnowMBA POV: usage pricing introduces revenue volatility that kills predictability — every quarter, the CFO is guessing instead of forecasting. Companies that go usage-only without strong consumption signals end up missing guidance and getting punished by Wall Street.

Per-Seat ARR = Seats × Price/Seat × 12 | Usage ARR = Annualized Consumption × Price/Unit | Predictability Score = 1 − Coefficient of Variation in Quarterly Revenue

Channel margin analysis measures profitability separately for each go-to-market channel: direct sales, self-serve, partner/reseller, retail, and marketplace. The same product earns dramatically different margins depending on how it's sold. A direct-to-consumer sale on your own website might deliver 70% gross margin. The same product sold through Amazon yields ~55% (after referral fees, FBA, returns). Through Best Buy retail it's ~35% (after channel margin, slotting, co-op marketing). Through an enterprise reseller it might be 50% (after partner discount and MDF). Atlassian famously runs both channel-led (~30% of revenue, partner-delivered) and direct/self-serve (~70%) — and the margin profile differs by 15-20 percentage points between them. Procter & Gamble carefully manages channel margin across direct-to-retailer, club store, and e-commerce, knowing each channel has different cost-to-serve and competitive dynamics.

Channel Contribution Margin = (Channel Revenue − Channel COGS − Channel-Specific OpEx) ÷ Channel Revenue | Blended GM Drift = Σ(Channel Mix Δ × Channel Margin)

Customer acquisition velocity is the rate at which new paying customers are added per unit of time, typically measured weekly or monthly. Velocity is what separates linear businesses from exponential ones. A SaaS adding 50 customers per month indefinitely produces linear ARR growth. A SaaS where velocity itself accelerates — 50, then 65, then 85, then 110 customers per month — produces exponential growth. Velocity is the leading indicator that pipeline conversion, demand generation, and product-market fit are tightening. When velocity is flat or decelerating despite increased marketing spend, you have a structural conversion problem, not a marketing problem. CAC payback and LTV/CAC measure unit-level health; velocity measures system-level momentum.

Customer Acquisition Velocity = New Customers Added ÷ Time Period | Velocity Δ = (Current Period Velocity − Prior Period Velocity) ÷ Prior Period Velocity

Time to profitability is the duration from a company's launch (or a specific cohort's start) to the point at which contribution margin or operating margin turns positive. At the company level, TTP is when monthly revenue exceeds monthly cash operating expenses. At the cohort level, TTP is the months until the cumulative gross profit from a customer cohort exceeds the cost to acquire and serve them. Strong consumer SaaS targets cohort TTP of 12-18 months. Strong B2B SaaS targets 18-30 months. Strong marketplaces target 24-36 months. At the company level, modern venture-backed startups now target operating profitability within 36-48 months — a sharp shift from the 2018-2021 era when companies could stay unprofitable indefinitely with cheap capital. The 2022 capital markets correction made TTP a board-level metric again.

Cohort TTP = Months until Cumulative Cohort Gross Profit ≥ Cohort Acquisition Cost | Company TTP = Months until Monthly Revenue ≥ Monthly OpEx

Sales capacity planning is the discipline of calculating exactly how many sales reps (Account Executives, SDRs, Customer Success) you need to hit a revenue target — and when each must be hired so they ramp in time to contribute. The model uses three inputs: target new ARR, productivity per ramped rep (quota attainment × ramped quota), and ramp time (typically 3-6 months for AEs). At a target of $20M new ARR per year with $800K ramped quota per AE at 70% attainment, each ramped AE produces $560K. You need 36 ramped AEs. With 4-month ramp, an AE hired in January contributes a full year; one hired in October contributes 25% of a year. Capacity planning forces honesty about whether the revenue plan is hire-able — many startups fail because the plan required hiring 30 senior AEs in a quarter, which is operationally impossible at high quality.

Productive Capacity per AE = Ramped Quota × Attainment Rate | Required AE Headcount = New ARR Target ÷ Productive Capacity | Required Hires = Required Headcount × (1 + Attrition Rate) × Ramp Adjustment

ROAS (Return on Ad Spend) is revenue generated per dollar of paid advertising spend. ROAS by channel decomposes this into channel-specific performance: Meta ads at 4.2× ROAS, Google Search at 6.8×, TikTok at 2.1×, podcast at 5.5×, etc. The decomposition is essential because blended ROAS hides everything that matters. A company reporting blended ROAS of 4× might be running Google Search at 8× and TikTok at 0.8×, with the profitable channel subsidizing the unprofitable one. Cutting the unprofitable channel and reallocating spend would dramatically improve the blended number. Channel-level ROAS, paired with marginal ROAS (the return on the next $10K of spend in each channel), is the foundation of every disciplined paid acquisition program.

Channel ROAS = Channel Revenue ÷ Channel Spend | Contribution-Margin ROAS = (Channel Revenue × Gross Margin %) ÷ Channel Spend | Marginal ROAS = ΔRevenue ÷ ΔSpend (last incremental spend tier)

Cohort LTV by Acquisition Channel measures the lifetime value of customers grouped by the channel that acquired them, tracked over time as a cohort. Customers acquired via paid social rarely behave like customers acquired via referral or organic search — their retention curves, expansion rates, and gross margins differ by 2-5×. A blended LTV of $3,200 might hide that referral cohorts are worth $9,800 and Facebook Ads cohorts are worth $1,400. The math: for each channel cohort, track Monthly Revenue × Gross Margin × Average Customer Lifetime — measured from actual retention curves, not assumed churn rates. KnowMBA POV: blended LTV is malpractice if you're spending more than $50K/month on paid acquisition. You're either underfunding your best channel or overfunding your worst.

Channel LTV = Σ (Monthly Revenue × Gross Margin × Survival Rate at Month N) for the channel cohort | Channel LTV/CAC = Channel LTV ÷ Channel CAC

CAC by Customer Segment measures the fully-loaded acquisition cost separately for each customer segment — typically broken down by company size (SMB, Mid-Market, Enterprise), industry vertical, geography, or product tier. Enterprise customers might cost $80,000 to acquire (long sales cycles, multiple stakeholders, custom procurement) while SMB customers cost $400. Both numbers can be 'right,' but a blended CAC of $4,000 hides which segment is actually profitable. Calculation: (Total Sales + Marketing Spend Allocated to Segment) ÷ (New Customers Acquired in Segment). KnowMBA POV: if your enterprise CAC is less than 5% of first-year ACV, your sales team is underinvesting in deal quality. If SMB CAC exceeds 25% of first-year ACV, you have a unit economics problem disguised as a growth problem.

Segment CAC = (Sales Costs Allocated to Segment + Marketing Spend Allocated to Segment + Allocated Overhead) ÷ New Customers Acquired in Segment

Sales Velocity is the master equation that aggregates pipeline volume, conversion rate, deal size, and cycle length into a single revenue-per-day output. The formula: Sales Velocity = (Number of Opportunities × Average Win Rate × Average Deal Size) ÷ Average Sales Cycle Length (in days). If you have 200 opportunities, 25% win rate, $40K average deal, and 90-day cycle, you generate $40K × 200 × 0.25 ÷ 90 = $22,222 per day. KnowMBA POV: sales velocity is the master equation because every other sales metric is just a lever on one of the four inputs. Doubling any input doubles velocity; halving any input halves it. Sales leaders who don't know their velocity number can't diagnose where to focus.

Sales Velocity = (Number of Opportunities × Win Rate × Average Deal Size) ÷ Sales Cycle Length (days)

Pipeline Conversion Rate is the percentage of opportunities that progress from one sales stage to the next, calculated stage-by-stage and end-to-end. A typical B2B SaaS funnel has 5-7 stages (Lead → MQL → SQL → Discovery → Demo → Proposal → Closed-Won), each with its own conversion rate. End-to-end conversion (Lead to Closed-Won) is the product of stage conversions. If each stage converts at 50%, end-to-end conversion is 0.5^5 = 3.1%. Stage-level analysis tells you where deals die. The biggest stage drop is your biggest leverage point. KnowMBA POV: most companies obsess over the 'close' stage when the leakage is actually upstream — typically Discovery-to-Demo or Demo-to-Proposal. Fixing the right stage compounds through the entire funnel.

Stage Conversion = Opportunities Advanced ÷ Opportunities Entered Stage | End-to-End Conversion = Product of all Stage Conversion Rates

Lead Velocity Rate (LVR) is the month-over-month growth rate of qualified leads (MQLs or SQLs). The formula: LVR = ((Qualified Leads This Month − Qualified Leads Last Month) ÷ Qualified Leads Last Month) × 100. Coined by Jason Lemkin, LVR is the most reliable predictor of revenue 6-12 months out because qualified leads convert through a predictable funnel into bookings. If LVR is consistently 8-10% MoM, revenue will grow at a similar rate one to two quarters later. LVR is the leading indicator that bookings are the lagging indicator of. KnowMBA POV: if your LVR is positive but bookings are flat, your funnel is breaking somewhere downstream — diagnose the stage. If LVR is negative, your bookings are about to drop — and no sales effort can fully save them.

Lead Velocity Rate = ((Qualified Leads This Month − Qualified Leads Last Month) ÷ Qualified Leads Last Month) × 100

SDR Productivity is the qualified meetings or qualified opportunities a single Sales Development Representative (SDR) generates per month. The Bridge Group SDR Productivity Report (the industry benchmark) tracks this metric across hundreds of B2B SaaS companies. Median SDR generates 8-15 qualified meetings per month, depending on motion (outbound vs inbound) and target segment (SMB vs Enterprise). Productivity = (Qualified Meetings Booked × Meeting-to-Opp Conversion Rate) ÷ SDR Headcount. Fully-loaded SDR cost is typically $90K-$150K/year, so productivity must generate at least 4-8× that in pipeline value to justify the function. KnowMBA POV: SDR underperformance is almost never a 'lazy SDR' problem — it's a list quality, target market, or product-market fit problem manifesting at the SDR layer.

SDR Productivity = (Qualified Meetings Booked × Meeting-to-Opp Conversion) ÷ SDR Headcount | SDR ROI = Pipeline $ Generated ÷ Fully-Loaded SDR Cost

AE Productivity is the new ARR (or new bookings) generated per ramped Account Executive per year. The Bridge Group AE Compensation Report (the industry benchmark) tracks this metric across hundreds of B2B SaaS companies. Median ramped AE produces $700K-$900K in new ARR per year, with top quartile above $1.2M. Productivity = Ramped Quota × Quota Attainment. A $1M quota at 65% attainment yields $650K productive output. Fully-loaded AE cost (base + variable + benefits + tools) typically runs $250K-$400K, so productivity must generate at least 3-5× cost to justify. KnowMBA POV: AE productivity is the most-cited and least-understood sales metric. Top reps look like talent; in reality they get the best territories, best leads, and best deals — productivity variance is more about deal flow than skill flow.

AE Productivity = Ramped Quota × Quota Attainment Rate | AE ROI = Annual Closed-Won ARR ÷ Fully-Loaded AE Cost

Quota Attainment is the percentage of assigned quota a sales rep (or sales team) actually achieves in a measurement period. Formula: Attainment % = (Actual Bookings ÷ Assigned Quota) × 100. Healthy B2B SaaS attainment rates: 60-75% of reps hit quota, with team-average attainment of 70-85% (Bridge Group, OpenView). Snowflake disclosed sales team attainment that historically tracked above industry medians during their hyper-growth phase, while Workday's mature enterprise sales org has consistently disclosed attainment in the high-60s to mid-70s percentile. KnowMBA POV: quota attainment below 60% is usually a quota-setting problem, not a rep performance problem. If most of your team can't hit quota, the quota is wrong — and treating it as a performance issue destroys morale, accelerates attrition, and produces no productivity gain.

Quota Attainment = (Actual Bookings ÷ Assigned Quota) × 100

Quota Capacity Modeling is the discipline of building a bottoms-up sales capacity number from headcount × quota × attainment, then validating it against the company's revenue plan. The model: Total Quota Capacity = Σ (Ramped AEs × Quota × Expected Attainment). If you have 20 ramped AEs at $1M quota with 70% expected attainment, capacity is $14M. Your revenue plan should be ≤ capacity OR you have a capacity gap. DataDog's mature capacity model is widely referenced as best-in-class — they explicitly disclose hiring plans tied to revenue targets in earnings calls, demonstrating discipline in capacity-revenue alignment. KnowMBA POV: most revenue plans miss because the underlying capacity model wasn't built — leadership picked a target and back-fit the headcount, instead of starting with realistic per-rep productivity.

Total Productive Capacity = Σ (Ramped AEs × Quota × Attainment) + Σ (Ramping AEs × Quota × Ramp Fraction × Attainment) − Attrition Adjustment

Sales Cycle Length is the average number of days from opportunity creation (or first qualified meeting) to closed-won. Atlassian's product-led motion famously achieves cycles measured in minutes (self-serve credit card upgrades), while enterprise SaaS deals routinely run 6-12 months. Industry benchmarks (Salesforce State of Sales): SMB SaaS = 30-45 days, Mid-Market = 60-120 days, Enterprise = 150-365+ days. Sales cycle is the denominator of the sales velocity equation — cutting cycle by 30% increases velocity by 43%, with no other lever changes. KnowMBA POV: cycle compression is the highest-leverage and most-overlooked sales improvement. Most companies focus on adding pipeline volume; the better play is usually shrinking the cycle.

Sales Cycle Length = Average (Date Closed-Won − Date Opportunity Created), measured in days, segmented by won deals only and by deal segment

Average Order Value (AOV) is the average dollar amount a customer spends in a single transaction. AOV = Total Revenue ÷ Number of Orders. If you do $500K in revenue across 10,000 orders, your AOV is $50. AOV is one of three levers that determine total revenue: Traffic × Conversion Rate × AOV. It's often the most underleveraged lever — most teams obsess over traffic and conversion while AOV improvements compound dollar-for-dollar to the bottom line. Costco's AOV is roughly $136 — nearly 4x Walmart's $35 — and is the structural reason Costco can run on a 2% net margin and still print money.

AOV = Total Revenue ÷ Number of Orders

Repeat Purchase Rate (RPR) is the percentage of customers who buy from you more than once in a defined window. RPR = (Customers with 2+ Orders ÷ Total Customers) × 100, measured over 30/60/90/365 days depending on category. It's the canary for product-market fit in transactional businesses. A first purchase tells you marketing worked; the second purchase tells you the product worked. Healthy DTC consumables run 30-50% RPR within 90 days. Healthy fashion DTC runs 25-40% within 12 months. Below those, you're running an acquisition treadmill.

Repeat Purchase Rate = (Customers with 2+ Orders ÷ Total Customers in Period) × 100

Acquisition Channel ROAS measures revenue generated per dollar of paid spend, broken out by individual channel: Google Ads, Meta Ads, TikTok, LinkedIn, etc. ROAS = Revenue Attributed to Channel ÷ Channel Spend. A 4:1 ROAS on Google Ads and a 2:1 ROAS on Meta tells you exactly where the next dollar should go. But the only channel ROAS that matters is COHORT ROAS — measured over the customer's full lifetime, not the first transaction. Google Ads might show 1.2x first-purchase ROAS but 6x lifetime ROAS once you include repeat purchases over 12 months.

Channel ROAS = Channel-Attributed Revenue ÷ Channel Ad Spend

Cost per Acquisition Optimization is the systematic process of reducing CAC without sacrificing volume — through bidding strategy, audience refinement, creative testing, landing-page conversion-rate optimization, and funnel-stage improvements. The goal is to lower CAC by 20-40% over 6-12 months while maintaining or growing customer acquisition volume. CPA optimization is NOT a one-time project; it's a continuous discipline. Companies that systemize it (weekly creative rotation, monthly audience pruning, quarterly landing-page testing) typically achieve 30%+ CAC reduction year-over-year. Companies that optimize ad-hoc see CAC inflate 15-25% per year as competition increases.

Target CPA = (LTV × Hurdle Margin) ÷ Hurdle Multiple. Optimization = (Old CPA - New CPA) ÷ Old CPA

Cost per Lead by Channel (CPL) measures what you pay to generate a single lead from each acquisition source: paid-search, paid-social, content/SEO, webinars, partnerships, etc. CPL = Channel Spend ÷ Leads Generated by that Channel. The trap most marketers fall into is comparing channels on CPL alone — but a $20 CPL channel that converts at 3% to customer is worse than a $50 CPL channel that converts at 25%. CPL only matters when paired with downstream Lead-to-Customer conversion. The right metric is 'Cost per Customer' = CPL ÷ (Lead-to-Customer rate).

CPL = Channel Spend ÷ Channel Leads. True Cost per Customer = CPL ÷ Lead-to-Customer Rate

Customer Cohort Health is the practice of evaluating each acquisition cohort (e.g., 'customers acquired in March 2026') as its own mini-business and tracking its vital signs over time: retention curve shape, expansion rate, gross margin, and net dollar retention. A healthy cohort retains 80%+ of revenue at month 12; an unhealthy one retains 30%. Aggregating cohorts hides the truth — your March 2025 cohort might be churning while your March 2026 cohort is expanding. Cohort health is how you detect product-market fit drift, channel quality decay, and pricing changes BEFORE they show up in aggregate metrics like MRR or churn.

Cohort Health = blend of (Retention Curve Shape, NDR, Gross Margin, CAC Payback) per cohort

Customer Lifetime Profit (CLP) is what's actually left after every cost: CLP = LTV − CAC − Cost-to-Serve − Variable Costs over Lifetime. It's the only customer-level metric that maps directly to enterprise value. LTV alone is a vanity metric because it ignores acquisition cost and ongoing servicing. A customer with $10,000 LTV but $3,000 CAC and $4,000 in cost-to-serve is only worth $3,000 in profit. CLP forces you to confront the full economics of each customer relationship — and reveals why some 'high-LTV' customer segments are actually unprofitable when you include support, infrastructure, and servicing costs.

CLP = LTV − CAC − Cumulative Cost-to-Serve over Customer Lifetime

Free Trial Conversion Economics is the math of how many trial signups convert to paid customers, what each trial costs to deliver, and what the resulting CAC looks like. The unit economics are governed by four levers: (1) Signup-to-Activation rate (do they actually use the product?), (2) Activation-to-Conversion rate (do they hit the value moment?), (3) Cost-per-Trial (infrastructure + support), and (4) Self-serve vs Sales-Assisted conversion. A 'good' free trial converts 15-25% of signups to paid; a great one converts 30%+. But the volume-vs-conversion tradeoff matters: a 5% conversion rate on 10,000 signups produces more revenue than a 30% conversion rate on 1,000 signups.

Trial CAC = (Trial Acquisition Cost + Trial Delivery Cost) ÷ Trial-to-Paid Conversion Rate

Marketing funnel conversion math is the discipline of decomposing your funnel into discrete stages — Visitor → Lead → MQL → SQL → Opportunity → Customer — and tracking the conversion rate between each pair. The blended top-to-bottom rate (Visitor → Customer) is a vanity metric. The stage-by-stage rates are where actual decisions get made. If you need 100 customers/month and your stage rates are 2% / 30% / 40% / 25% / 30%, you need ~555,000 visitors. Change any one stage by 5pp and the visitor requirement swings by 30%+. Funnel math is how you back-solve from a revenue target to a marketing spend.

Customers = Visitors × Visit→Lead × Lead→MQL × MQL→SQL × SQL→Opp × Opp→Won

Marketplace Liquidity is the probability that a buyer's request gets fulfilled by a seller within an acceptable time. Liquid marketplaces (Uber, Airbnb, eBay) have match rates above 80%; illiquid ones fail. Liquidity is geographic and category-specific: Uber in San Francisco is liquid; Uber in rural Wyoming is not. The key metrics are: (1) Match Rate = % of buyer requests filled, (2) Time-to-Match, (3) Density (suppliers per square mile or per category), (4) Fulfillment Rate. KnowMBA POV: marketplace liquidity is the only metric that matters at the bootstrap stage. Take rate, GMV, and CAC are vanity if liquidity is broken — buyers churn after one bad search and never return.

Match Rate = Filled Requests ÷ Total Requests (per liquidity unit). Liquidity = f(Density, Time-to-Match)

Revenue per Visitor (RPV) is the single most useful unit-economics metric for any web business: RPV = Total Revenue ÷ Total Unique Visitors. It collapses three metrics — conversion rate, AOV, and traffic mix — into one number that tells you what each visitor is worth. Median DTC RPV runs $2-5; best-in-class can hit $15+. The reason RPV beats conversion-rate-and-AOV separately: it's directly comparable to your CPC. If your RPV is $4 and CPC is $1.50, you have a 2.7x ROAS before accounting for repeat purchases. RPV is also stable across traffic-volume swings, while conversion rate fluctuates wildly with traffic mix.

RPV = Total Revenue ÷ Total Unique Visitors (over the same period)

Self-Serve vs Sales-Assisted Conversion is the strategic decision of which customer segments convert through pure product (no human contact) and which require sales involvement. The economics differ wildly: self-serve customers cost $50-$500 to acquire and produce $500-$5,000 ARR; sales-assisted customers cost $5,000-$50,000 to acquire and produce $50,000-$500,000 ARR. The right answer for most modern B2B SaaS is BOTH: self-serve captures volume below $10K ACV, sales-assisted captures the long-tail enterprise above $50K ACV. The hybrid model — 'Product-Led Sales' or PLS — has become the dominant playbook because it lets companies serve the full ACV range without forcing every customer through a sales cycle.

Blended CAC = (Self-Serve CAC × Self-Serve Customers + Sales CAC × Sales Customers) ÷ Total Customers