This calculations glossary outlines essential formulas for key performance metrics across marketing, marketing operations, and web operations: acquisition costs, conversion rates, funnel performance, attribution impact, pipeline velocity, and other core KPIs. Using these formulas consistently ensures accuracy across dashboards, reports, and analytics tools. Standardized, reliable metrics allow your teams to benchmark, forecast, and optimize with confidence.
Acquisition + revenue metrics
Customer Acquisition Cost (CAC)
CAC = total sales & marketing costs ÷ number of new customers
Customer Lifetime Value (LTV or CLV)
There are multiple models, but the most common is:
LTV = average revenue per customer × gross margin × average customer lifespan
For subscription businesses:
LTV = (average revenue per user [or ARPU] × gross margin) ÷ churn rate
LTV : CAC ratio
LTV : CAC = LTV ÷ CAC
Note: ideal benchmark is 3:1; under 1:1 is unsustainable
Conversion + funnel metrics
Conversion rate
Conversion rate = (number of conversions ÷ total visitors or leads) × 100
Click-through rate (CTR)
CTR = (clicks ÷ impressions) × 100
Lead-to-MQL rate
Lead → marketing qualified leads [MQLs] rate = (number of MQLs ÷ number of new leads) × 100
MQL-to-SAL rate
Markting qualified leads [MQLs] → sales accepted leads [SALs] rate = (SALs ÷ MQLs) × 100
SAL-to-SQL rate
Sales accepted leads [SALs] → Sales qualified leads [SQLs] rate = (SQLs ÷ SALs) × 100
SQL-to-opportunity rate
Sales qualified leads [SQLs] → opportunity rate = (Opportunities ÷ SQLs) × 100
Opportunity-to-close rate (win rate)
Win rate = (closed-won deals ÷ total opportunities) × 100
Funnel velocity (full funnel)
For velocity across lead → revenue:
Velocity = (number of deals × average deal size × win rate) ÷ average sales cycle length (display win rate as a decimal, not percent)
Attribution + performance metrics
ROAS
Return on ad spend [ROAS] = revenue attributed to ads ÷ advertising spend (usually expressed as a multiple, for example: 4.2×)
ROMI
Return on marketing investment [ROMI] = (incremental revenue – marketing cost) ÷ marketing cost
CPL
Cost per lead [CPL] = total marketing spend ÷ number of leads generated
CPQL
Cost per qualified lead [CPQL] = total spend ÷ number of qualified leads (MQLs, SQLs, etc.)
CPA
In cost per acquisition [CPA], the acquisition is any defined action (not necessarily a paying customer), thus CPA = total cost ÷ number of acquisitions
Website + WebOps metrics
Bounce rate
Bounce rate = (single-page sessions ÷ total sessions) × 100
Exit rate
Exit rate = (exits from page ÷ total pageviews of that page) × 100
Average session duration
Average session duration = total session time ÷ total sessions
Pages per session
Pages per session = total pageviews ÷ total sessions
Core web vitals (calculated at page or site level)
LCP
Largest contentful paint [LCP] is measured in seconds (fourth contentful element’s render time).
CLS
Cumulative layout shift [CLS] = Σ (layout shift score × impact fraction). Measured, not manually calculated.
FID
First input delay [FID] = first input timestamp – event processing start.
Email + campaign metrics
Open rate
Open rate = (unique opens ÷ delivered emails) × 100
Email click rate
Email click rate = (unique clicks ÷ delivered emails) × 100
CTOR
Click to open rate [CTOR] = (unique clicks ÷ unique opens) × 100
Unsubscribe rate
Unsubscribe rate = (unsubscribes ÷ delivered emails) × 100
Data + segmentation metrics
Audience reach
Audience reach = number of unique users exposed to campaign Note: usually measured, not calculated.
Audience growth rate
Audience growth rate = ((new audience – lost audience) ÷ starting audience) × 100
Account-based metrics
Account engagement score
Varies by model, but common weighted formula:
Account engagement score = Σ (touchpoint weight × touchpoint occurrence)
Account penetration rate
Account penetration rate = (number of engaged contacts ÷ total contacts in buying committee) × 100
Testing + optimization metrics
A/B test uplift
A/B test uplift (%) = ((variant conversion rate – control conversion rate) ÷ control conversion rate) × 100
Statistical significance (simplified Z-score)
Z = (p₁ – p₂) ÷ √(p(1–p)(1/n₁ + 1/n₂))
Where:
- p₁ = variant conversion rate
- p₂ = control conversion rate
- p = pooled conversion rate
- n₁, n₂ = sample sizes
MKTGWEBOPS uses AI tools across our framework. We depend on them to streamline operations, optimize content, and provide a consistent experience. We believe AI is critical in today’s workflow and will continue to become more so. AI enables us to automate rote or complex tasks (e.g. generating a glossary of terms such as this one) so our team can focus on delivering content and services that only come with decades of experience.
On this page, AI helped us collect common marketing operations, web operations, and marketing terms, find authoritative sources for definitions, and distill those into a single widely accepted definition.
Even when we use AI, our team meticulously reviews and approves every AI-assisted element before publishing to make sure it’s accurate and true to our brand. And, sometimes, AI gets it wrong and it’s a total rewrite.