Marketing Operations KPIs: The Metrics That Connect MOps to Revenue

The measurement gap is a revenue problem

Marketing teams measure a lot. Impressions, clicks, open rates, social followers, webinar attendees, content downloads. The dashboards are full. The reports are colorful. And yet, only 23% of marketers are confident they are tracking the right KPIs (Ruler Analytics). That number should concern anyone who runs marketing operations.

The gap is not about having too few metrics. It is about measuring activity instead of impact. Most marketing dashboards tell you what happened (we sent 50,000 emails) without connecting it to what matters (those emails generated $1.2M in pipeline that closed at a 28% rate with a 74-day sales cycle).

Marketing operations exists to bridge this gap. MOps is the function that connects marketing activity to revenue outcomes, and the metrics framework you build determines whether marketing is seen as a cost center or a growth engine. This post lays out the metrics that actually matter, organized by what they tell you about your funnel, your efficiency, and your growth trajectory.

The problem with marketing vanity metrics

Vanity metrics are measurements that look impressive in a report but do not correlate with revenue outcomes. They are not useless; they have diagnostic value in specific contexts. The problem is when they become the primary way marketing communicates its impact.

Impressions tell you how many times content appeared on a screen. They tell you nothing about whether the right people saw it or whether it influenced a buying decision.

Click-through rates measure engagement with a specific asset. A 3% CTR on an email is fine, but if those clicks come from people outside your ICP who will never buy, the metric is misleading.

Email open rates became even less reliable after Apple's Mail Privacy Protection launched in 2021, which pre-fetches email content and inflates open rate data. Many MOps teams have stopped reporting on open rates entirely.

Social media followers measure audience size, not audience quality or buying intent.

None of these metrics are inherently bad. The problem is when a CMO walks into a board meeting and leads with email open rates instead of marketing-sourced pipeline and CAC by channel. The board does not care about opens. The board cares about how marketing investments translate into revenue.

MOps must own the metrics framework that makes this translation possible. Here is how to build it.

Marketing operations metrics framework

We organize marketing operations metrics into three tiers. Each tier answers a different strategic question. Together, they give you a complete picture of marketing's contribution to the business.

Tier 1: Customer journey metrics

These metrics track how prospects move through the funnel from first touch to closed-won revenue. They are the foundation of marketing accountability.

Marketing Qualified Lead (MQL) volume and rate

Formula: Total leads meeting MQL criteria in a given period. MQL rate = MQLs / Total new leads.
What "good" looks like: Highly variable by industry and motion. For B2B SaaS with inbound-heavy models, MQL rates of 5-15% of total leads are common. The absolute volume matters more than the rate; a 10% MQL rate on 1,000 leads is less valuable than a 5% MQL rate on 10,000 leads if the lower-rate leads convert better downstream.
Common pitfall: Optimizing for MQL volume without tracking downstream conversion. If your MQL volume doubles but MQL-to-SQL conversion drops by half, you have not improved anything. You have just moved the bottleneck downstream.

MQL-to-SQL conversion rate

Formula: SQLs accepted by sales / Total MQLs passed to sales.
What "good" looks like: 25-40% for most B2B SaaS companies. Below 20% signals a misalignment between marketing's definition of "qualified" and what sales actually considers worth pursuing.
Common pitfall: Not tracking rejection reasons. When sales rejects an MQL, capturing why (wrong ICP, too early in buying cycle, bad data, already in active opp) provides actionable feedback for scoring model refinement.

Lead-to-opportunity conversion rate

Formula: Opportunities created / Total leads generated (or MQLs generated, depending on your definition).
What "good" looks like: 10-20% for inbound leads, 2-5% for outbound. These numbers vary significantly by segment and industry, so benchmark against your own historical data first, then against industry peers.
Common pitfall: Not segmenting by source. Blended conversion rates mask the performance of individual channels. Your content syndication leads may convert at 3% while your demo requests convert at 45%. The blended number tells you nothing useful.

Lead-to-customer conversion rate

Formula: New customers / Total leads generated in the same cohort.
What "good" looks like: 1-5% for most B2B SaaS. This is the metric that connects top-of-funnel activity to actual revenue. It takes months to mature for longer sales cycles, so track it on a cohort basis.
Common pitfall: Measuring this on a calendar basis instead of a cohort basis. A lead generated in January that closes in June should be attributed to the January cohort, not the June reporting period.

Time from first touch to MQL

Formula: Average days between lead creation (or first known touch) and MQL status.
What "good" looks like: Depends entirely on your sales cycle. For velocity models (SMB, PLG-assisted), 7-14 days is strong. For enterprise, 30-90 days may be normal.
Common pitfall: Not breaking this down by channel. Paid search leads that hit MQL in 3 days behave very differently from content-nurtured leads that take 60 days. Understanding these patterns helps you allocate nurture resources effectively.

Tier 2: Efficiency metrics

These metrics tell you how efficiently marketing converts spend into pipeline and revenue. They are the metrics that finance and the board care about most.

Customer Acquisition Cost (CAC) by channel

Formula: Total marketing and sales spend attributed to a channel / Number of new customers acquired through that channel.
What "good" looks like: CAC varies enormously by segment. For SMB SaaS, $200-$1,000 is common. For mid-market, $5,000-$25,000. For enterprise, $25,000-$100,000+. The number itself matters less than the trend and the ratio to LTV.
Common pitfall: Calculating blended CAC only. Blended CAC hides the fact that your organic channel acquires customers at $500 while your paid channel acquires them at $8,000. Channel-level CAC drives budget allocation decisions.

Marketing-sourced pipeline as % of total

Formula: Pipeline value from marketing-sourced opportunities / Total pipeline value.
What "good" looks like: For companies with balanced GTM motions, marketing should source 30-50% of total pipeline. Companies with strong inbound and content marketing may see 50-70%. Below 20% suggests marketing is underperforming as a pipeline engine or that attribution is broken.
Common pitfall: Conflating "sourced" with "influenced." Marketing-sourced means marketing was the first touch that created the opportunity. Marketing-influenced means marketing touched the deal at some point. Both are valid, but they answer different questions. Report both, clearly labeled.

Marketing-influenced pipeline as % of total

Formula: Pipeline value from opportunities with at least one marketing touchpoint / Total pipeline value.
What "good" looks like: 70-90% for most B2B companies with active marketing. If marketing does not influence the majority of your pipeline, either attribution tracking is incomplete or marketing is disconnected from the buying process.
Common pitfall: Claiming influence credit too broadly. If a contact clicked one email three months before an outbound-sourced opportunity was created, counting that as "marketing influenced" stretches credibility. Define meaningful influence criteria and be honest about what counts.

Cost per MQL and Cost per SQL

Formula: Total marketing spend / MQLs generated (or SQLs generated).
What "good" looks like: Cost per MQL for B2B SaaS typically ranges from $30-$200 depending on industry and channel. Cost per SQL is typically 3-5x cost per MQL, reflecting the conversion rate between stages.
Common pitfall: Optimizing cost per MQL without considering quality. The cheapest MQLs (e.g., from broad content syndication) often have the lowest downstream conversion. Optimize for cost per SQL or cost per opportunity instead.

Marketing ROI by program and channel

Formula: (Revenue attributed to marketing - Marketing cost) / Marketing cost. Expressed as a ratio or percentage.
What "good" looks like: 5:1 is a common benchmark (for every $1 spent, $5 in revenue). Best-in-class programs achieve 10:1+. Below 3:1, the program is marginally profitable at best after accounting for fully loaded costs.
Common pitfall: Using pipeline value instead of revenue. A program that generates $500K in pipeline that closes at 10% produced $50K in revenue. Reporting the $500K as ROI massively overstates impact.

Tier 3: Growth and revenue metrics

These metrics connect marketing performance to long-term business health and strategic decision-making.

Pipeline velocity (marketing-sourced)

Formula: (Number of marketing-sourced opportunities x Average deal value x Win rate) / Average sales cycle length.
What "good" looks like: Pipeline velocity is a composite metric, so the absolute number is less important than the trend. Increasing pipeline velocity means your revenue engine is getting more efficient. Decreasing velocity means something in the funnel is degrading.
Common pitfall: Not isolating marketing-sourced pipeline velocity from outbound-sourced. Marketing leads and outbound leads often have very different deal sizes, win rates, and cycle lengths. Blending them obscures where improvements or problems exist.

Revenue attribution by channel

Formula: Closed-won revenue attributed to each channel, calculated under your chosen attribution model (first touch, last touch, multi-touch).
What "good" looks like: The distribution should roughly correlate with your investment allocation, adjusted for channel economics. If you invest 40% of your budget in paid channels but they produce 15% of attributed revenue, something is misaligned.
Common pitfall: Reporting attribution under only one model. First-touch attribution overcredits awareness channels. Last-touch overcredits bottom-of-funnel converters. Multi-touch provides a more balanced picture but requires more sophisticated tracking.

Customer Lifetime Value (CLV) by acquisition channel

Formula: Average revenue per customer x Gross margin x Average customer lifespan (or: Average revenue per customer / Churn rate for SaaS companies).
What "good" looks like: LTV:CAC ratio of 3:1 or higher per channel. A channel with a $50K LTV and $20K CAC (2.5:1) is less efficient than a channel with $30K LTV and $5K CAC (6:1), even though the first channel produces higher absolute LTV.
Common pitfall: Treating CLV as a static number. CLV varies significantly by acquisition channel, customer segment, and product. Segment your CLV analysis to understand which channels produce the most valuable (not just the most) customers.

Marketing spend as % of revenue

Formula: Total marketing budget / Total company revenue.
What "good" looks like: For B2B SaaS, typical ranges are 10-20% of revenue for growth-stage companies and 5-15% for mature companies. Companies prioritizing growth spend at the higher end; companies prioritizing profitability operate at the lower end.
Common pitfall: Using this metric in isolation. A company spending 25% of revenue on marketing that is growing 100% YoY is in a very different position than a company spending 25% that is growing 15%. Context matters.

Multi-touch attribution: the MOps challenge

Attribution is one of the most technically complex and politically charged areas in marketing operations. It determines how credit for revenue is distributed across marketing channels, programs, and touchpoints. The model you choose shapes budget allocation decisions, team incentives, and marketing strategy.

Why single-touch attribution misleads

First-touch attribution gives 100% credit to the first interaction. Last-touch gives 100% credit to the final interaction before conversion. Both are wrong in the same way: they pretend that one touchpoint deserves all the credit when the average B2B deal involves 6-10 touchpoints across multiple channels over weeks or months.

A prospect might discover your company through a blog post (organic search), attend a webinar (email nurture), visit the pricing page (direct), and then fill out a demo form after clicking a retargeting ad (paid social). First-touch credits organic search. Last-touch credits paid social. Neither tells the full story.

Common multi-touch attribution models

Linear attribution distributes credit equally across all touchpoints. A deal with four marketing touches gives 25% credit to each. Simple and fair, but it treats a passing email open the same as a high-intent demo request.

Time decay attribution gives more credit to touchpoints closer to the conversion event. Recent interactions receive more weight than earlier ones. This model reflects the fact that later-stage touches tend to be higher intent, but it systematically undervalues the awareness-stage activities that started the journey.

Position-based (U-shaped) attribution gives 40% credit to the first touch, 40% to the last touch, and distributes the remaining 20% across middle touchpoints. This model recognizes the importance of both discovery and conversion while acknowledging the assists in between.

W-shaped attribution gives 30% each to first touch, lead creation, and opportunity creation, with the remaining 10% distributed across other touchpoints. This model works well for B2B companies with distinct stages.

The practical approach

Perfection in attribution is unattainable. The data is incomplete (dark social, word-of-mouth, and untracked touchpoints always exist), models are approximations, and cookie deprecation continues to reduce tracking fidelity.

The practical approach: use multi-touch attribution (position-based or W-shaped) for strategic decisions like budget allocation and channel investment. Use last-touch attribution for daily operational decisions like campaign optimization and lead source routing. Report both, explain the differences to stakeholders, and resist the temptation to pick whichever model makes your numbers look best.

Building a MOps dashboard that speaks to the C-suite

Different stakeholders need different levels of detail. Building one massive dashboard that tries to serve everyone serves nobody. Instead, build three views.

CEO and board view: 3 metrics

The board does not need 40 marketing metrics. They need three:

Marketing-sourced pipeline and revenue. How much pipeline did marketing create, and how much of it converted to revenue? Shown as both absolute numbers and percentage of total.
Customer Acquisition Cost. Blended CAC and the trend over the last four quarters. Is marketing getting more or less efficient?
Marketing ROI. For every dollar invested in marketing, how many dollars of revenue were generated?

These three metrics answer the only question the board is really asking: is marketing a productive use of capital?

CMO view: 8 metrics

The CMO needs the board view plus operational context:

CAC by channel. Where is spend most and least efficient?
MQL-to-SQL conversion rate. Is the handoff to sales working?
Marketing-sourced pipeline velocity. Is the marketing funnel getting faster or slower?
Campaign performance (top 10). Which programs are producing results and which are underperforming?
Marketing spend as % of revenue. Are we within our operating model?

This view lets the CMO make resource allocation decisions and identify areas that need attention before they become problems.

MOps operational view: full stack

The MOps team needs the complete picture:

All of the above, plus:
Data quality metrics. Lead enrichment rates, email deliverability, bounce rates, duplicate creation rate.
Automation performance. Nurture program engagement, scoring model accuracy (what % of high-scored leads actually convert?), workflow error rates.
Delivery metrics. Email deliverability, landing page conversion rates, form completion rates.
Funnel stage duration. How long do leads spend at each lifecycle stage? Where are the bottlenecks?
SLA compliance. Are leads being followed up within the defined response time? (See speed to lead for why this matters.)

This operational view is what MOps uses daily to identify issues, optimize campaigns, and maintain the health of the marketing engine.

Connecting MOps metrics to sales and CS

Marketing metrics do not exist in a vacuum. The most important MOps metrics are the ones that connect to sales and customer success outcomes.

The handoff metrics

MQL response time. How quickly does a sales rep make first contact after a lead reaches MQL status? This is the single highest-leverage metric at the marketing-to-sales boundary. Research consistently shows that leads contacted within 5 minutes convert at dramatically higher rates than those contacted hours or days later. For the full data, see speed to lead.

SQL acceptance rate. What percentage of MQLs that marketing passes to sales are accepted as SQLs? A low acceptance rate (below 25%) signals that marketing's qualification criteria and sales' expectations are misaligned. Track rejection reasons to diagnose the specific disconnect.

Lead-to-opportunity time. How many days elapse between MQL and opportunity creation? Long gaps indicate either slow sales follow-up or a qualification gap where leads need more nurturing before handoff.

For more on how scoring and routing work together at this handoff point, see lead routing best practices.

The feedback loop metrics

Pipeline-to-close rate by marketing source. Not all marketing-sourced pipeline is equally likely to close. Tracking close rates by source channel tells you which marketing programs generate pipeline that actually converts versus pipeline that inflates the forecast but never closes.

Customer retention by acquisition channel. Some channels produce customers who stick; others produce customers who churn. If your paid search customers have a 60% annual retention rate while your organic content customers retain at 90%, that information should reshape your channel investment strategy.

Expansion revenue by original acquisition channel. The best customers are not just the ones who stay. They are the ones who grow. If customers acquired through a specific program or channel expand at higher rates, that channel's true value is significantly higher than its initial CAC suggests.

Building the metrics infrastructure

Having the right metrics framework is step one. Building the infrastructure to actually measure them reliably is step two.

Tag everything at the source. UTM parameters, lead source fields, and campaign member tracking must be in place before you can attribute anything. Retroactive attribution is possible but painful and imprecise.

Define your data model. How do leads, contacts, accounts, opportunities, and campaigns relate to each other in your CRM? This data model determines what attribution is possible. If campaign members are not linked to opportunities, multi-touch attribution is impossible without a third-party tool.

Automate where possible. Manual reporting breaks down at scale. Invest in BI tools (Looker, Tableau, or CRM-native analytics) that pull from your data warehouse or CRM in real time. Every manual step in reporting is a potential point of error and delay.

Audit regularly. Attribution data degrades over time as tracking breaks, UTM conventions drift, and integrations fail silently. Run a quarterly audit of your attribution infrastructure. Check that tracking is firing correctly, that data is flowing between systems, and that your reports match reality.

The bottom line

Marketing operations metrics are not about proving marketing's value. They are about understanding it, optimizing it, and communicating it in terms the business cares about.

Start with the customer journey metrics that track funnel progression. Layer on efficiency metrics that connect spend to outcomes. Add growth metrics that link marketing to long-term business health. Build dashboards tailored to each stakeholder's decision-making needs. And invest in the attribution infrastructure that makes reliable measurement possible.

The 77% of marketers who lack confidence in their KPIs are not failing because they measure too little. They are failing because they measure the wrong things. MOps is the function that fixes this.

If you are building the operational infrastructure to connect marketing activity to revenue outcomes, RevenueTools can help. We are building the tools that turn clean data into execution across the full revenue lifecycle.