For years, B2B marketers have been judged by numbers that looked good on a dashboard but didn’t say much about revenue. Pageviews. Social likes. Website traffic spikes. Vanity metrics are fine if they’re all you have, but we can (and must!) do better. 

We now have the ability to capture richer signals and use AI-powered analytics to connect activity to actual business outcomes. It’s time to move beyond surface-level stats and start measuring what truly matters. It’s time to make sure we can prove how marketing contributes to revenue and growth. 

Ten modern metrics every B2B marketer should pay attention to 

You don’t need to throw out your entire measuring playbook to be a successful marketer (we’ll talk more about old-school metrics later). But it’s worth leading with these AI-enhanced metrics. 

 1. Pipeline contribution (marketing-sourced and influenced revenue)

Instead of stopping at “marketing generated X leads,” measure how much pipeline your programs are actually creating and influencing. AI attribution models can map the impact of multiple touchpoints, giving you a clear view of where marketing is moving the revenue needle. 

 2. Customer acquisition cost (CAC) by segment

CAC has always been important, but now AI can break it down by channel, campaign, or customer segment. This reveals which acquisition strategies deliver the best ROI — and where you may be overspending. 

 3. Customer lifetime value (CLV) prediction

Not all customers are created equal. AI can model lifetime value based on early engagement signals, allowing marketers to prioritize high-potential accounts. Investing more in leads likely to drive long-term revenue makes your funnel more efficient. 

4. Sales velocity

Traffic is nice, but how fast are deals actually closing? Sales velocity tracks the speed at which opportunities move through the pipeline. AI helps surface bottlenecks, flagging where marketing can step in to accelerate the journey. 

5. Content influence on pipeline and revenue

Views and downloads don’t tell the whole story. AI can connect content engagement to deal progression, showing which assets actually move accounts forward. This helps you double down on content that converts. 

 6. Account engagement score

For ABM programs, an engagement score that blends intent data, website visits, content interactions, and sales activity is far more valuable than pageviews alone. AI can weigh these signals to provide a real-time snapshot of account readiness. 

 7. Marketing efficiency ratio (MER)

Also called the revenue-to-spend ratio, MER reveals how much revenue each marketing dollar generates. With AI-driven analysis, you can segment this ratio across campaigns, channels, or audiences to identify your most efficient growth levers. 

 8. Expansion revenue influence

Net-new business is only half the equation. AI makes it easier to track how marketing contributes to renewals, upsells, and cross-sells. This metric ensures marketing gets credit for influencing the customer lifecycle beyond acquisition. 

 9. Buyer journey progression rate

It’s not about how many leads you have, but how many advance to the next stage. AI can pinpoint what actions — a webinar, a case study, a demo request — help prospects move forward, helping you optimize the journey. 

 10. Predictive churn risk signals

Retention is growth. AI models can detect early warning signs of churn — like declining engagement, reduced product usage, or negative sentiment. Measuring how marketing programs reduce churn risk turns retention into a marketing KPI. 

Older metrics with new relevance in the age of AI 

Some of the numbers marketers have tracked for years are becoming more valuable thanks to AI’s ability to analyze them with greater depth. Here are a few classics with a new spin. 

Website traffic: segmented and intent-driven 

Raw traffic counts used to be a vanity metric. Now, with AI-powered tools, traffic can be enriched with firmographic data and intent signals. The result: you know who is visiting and why, not just how many. 

Email open and click rates: behaviorally enriched 

Once dismissed as shallow engagement signals, email metrics gain new meaning when combined with AI. New platforms can analyze open/click patterns to predict optimal send times, identify buying intent, and flag high-value accounts showing activity spikes. 

Time on page: contextualized with journey progression 

“Dwell time” once told you little beyond whether someone skimmed your content or went all in. With AI, you can tie time on page to deal outcomes. Machine learning can detect patterns that reveal whether extended engagement on a particular page correlates with pipeline acceleration. 

Net promoter score (NPS): predictive of churn and expansion 

NPS has long been criticized for being simplistic. But when paired with AI sentiment analysis and customer behavior data, it becomes a leading indicator of churn risk or expansion opportunities.  

Social engagement: tied to account activity 

Likes and shares don’t equal revenue on their own. But with AI-powered social listening platforms, marketers can connect spikes in social engagement with account-level activity, measuring influence on pipeline and brand lift. 

From activity to outcomes 

The days of celebrating vanity metrics are over. With AI-powered insights, B2B marketers can finally measure the actions and touchpoints that create, accelerate, and protect revenue. 

By focusing on metrics like pipeline contribution, journey progression, and churn risk reduction, marketing teams position themselves as true growth drivers.