We’ve all been in that board meeting. The one where the GTM dashboard is a sea of green—MQLs, pipeline created, and sales activity are all up and to the right. Then we get to revenue. We missed our target. Again.

This is the painful disconnect that keeps CROs up at night. The problem isn’t just a marketing issue; it’s a revenue-wide problem. We’re measuring our entire Go-To-Market engine through a blended lens, and it’s lying to us. When we lump all our leads, all our deals, and all our customers into single, aggregated buckets, we treat them as if they were created equal. And we know, deep down, that’s just not true.

The High Cost of Blended Data

Let’s start with a classic example from the top of the funnel. An MQL from a “Request a Demo” form is worlds apart from an MQL from a broad webinar. One signals high intent; the other signals early-stage research. Yet, in a blended funnel, they both count as “1 MQL”. This flawed view leads to disastrous marketing investments.

This is just one example of a problem that infects the entire GTM motion. The same dangerous logic applies to our sales pipeline. A blended view might show a healthy 25% close rate. But what if that average is masking that deals we’re selling into one industry are closing at 30%, while deals in another are closing at 10%?

A blended view of our sales cycle might look like a reasonable 90 days, hiding the fact that one product normally closes in 70 days while deals for another product get stuck for 6 months, killing our forecast accuracy.

This bad data becomes a political shield across the entire revenue team. Marketing hits its MQL target. Sales hits its pipe gen or pipeline coverage target. But we are still staring at a revenue gap. The finger-pointing begins, fueled by dashboards that reward activity, leads, and pipeline, not profitable outcomes. Unblending the data is the first step toward optimizing the full GTM engine.

The First Cut: A Practical Guide to Segmenting Our GTM Engine

We must stop looking at our GTM motion as a single funnel. Instead, we must see it as a collection of distinct micro-funnels, from initial lead to final close and beyond. This requires segmenting your data across a few critical dimensions at every stage.

At the Top: Leads & Pipeline Generation

• By Intent Level: Distinguish high-intent hand-raisers (“Request a Demo”) from low-intent researchers (eBook downloads).
• By Ideal Customer Profile (ICP) Fit: Score every lead based on how well it fits your target profile.
• By GTM Motion, Source, and Channel: Separate inbound from outbound. Then, within inbound, break down every lead by its origin: Paid Search, Organic, Webinars, Partners, etc.

*I know we JUST wrote a Framework advising folks to combine inbound and outbound. Bear with me. We also said that inbound leads should convert to revenue at least as much as outbound. We need to see if that’s the case. If not, why are we wasting money calling THOSE types of inbound leads when we could just make more outbound calls and generate more revenue?

In the Middle: Sales Execution & Closing

• By Customer Type: Do different types of customers have different close rates, sales cycles, or ASPs?
• By Product/Geo: Do different products or geographies have drastically different conversion rates?
• By Sales Team / Rep: Are certain reps or teams outperforming others with specific types of deals?

At the Bottom: Net Revenue Retention

• By Customer Segment: Are enterprise customers expanding at a higher rate than SMB customers? Is one industry churning more than another?
• By Product Line: Which products are driving the most expansion revenue versus those with high contraction or churn?
• By Original Acquisition Channel: Do customers who were acquired via high-intent channels have better retention and growth profiles than those from low-intent sources? This closes the loop on the full GTM engine.

These are just a few examples. We can slice and dice our GTM data in unlimited ways but even basic unblending reveals uncomfortable truths. We might discover that we’re wasting money and resources chasing certain types of customers, selling specific products, or working certain geos. We might discover some combination of those is a winning or losing formula.

When I was an AE at Salesforce, they literally gave me a report, broken down by industry codes (NAICS), showing which industries bought the most from us, in the US, by product. They gave us other reports showing which personas influenced decisions on each product most. My territory had already been carefully designed AND I was able to stack rank my accounts and contacts with this intel.

Connecting the Dots: From Segmented Activity to Attributable Revenue

The goal is to connect every GTM activity to the only metrics that truly matter: revenue and profitability. We must track these metrics not as blended averages, but for each individual segment.

1. Stage-by-Stage Conversion Rates: What percentage of leads become SQLs? What percentage of SQLs turn into closed-won deals? Analyze this for every segment, if possible. If not, looking at the conversion from the top of the funnel (Lead/MQL/First Meeting) to the bottom (Closed Won) will tell us what’s generating revenue and what’s not.
2. Sales Cycle: How many days does it take to close a deal for each segment? Identify the bottlenecks.
3. Average Contract Value (ACV) & Lifetime Value (LTV): Which segments produce your most valuable customers? (We’ve already shared we hate LTV, but estimating 3-5 years of customer value can provide important insights.)
4. Customer Acquisition Cost (CAC) & CAC Payback: This is the ultimate measure of ROI. A blended 2:1 CAC Payback can hide a profitable 1:1 channel/segment and an unprofitable 5:1 channel/segment.
5. Net Revenue Retention (NRR): This same logic must be applied post-sale. A healthy blended NRR of 110% might feel good, but it could be masking that our customers in one industry are expanding at 140% while customers in another are churning at 85%. Unblending this data shows us where to focus our Customer Success resources for maximum impact, as well as who to target for new logos.

The Unblended Engine in Action

As an example, let’s look at high-intent vs. low-intent MQLs and how they convert to revenue for a company with a $50,000 ACV.

This table tells a story no blended dashboard ever could. On the surface, the low-intent leads look productive; they generated more SQLs (72 vs. 60). A leader looking only at that metric might push for more investment in gated content. But the unblended truth is devastatingly clear: the high-intent “hand-raisers,” which made up only 10% of the initial MQL volume, generated 60% of the total revenue. They converted to customers at over 12x the rate of low-intent leads and closed 45 days faster. This isn’t just data; it’s a clear mandate for where to focus sales energy and marketing spend.

The Revenue-First Mandate: From Insight to Action

The unblended data gives us the blueprint. Now, we must act.

1. Double Down on What Works

Systematically reallocate resources to your winning plays. This means funding the marketing channels that produce your most profitable customers, but it also means hiring more AEs who fit the profile of your top performers and focusing them on the segments where they win.

2. Optimize or Eliminate What Doesn’t

For underperforming segments, try to optimize. Can you improve the win rate for a certain customer segment with better training or a revised playbook? Is it worth the effort? Be ruthless. If a channel, a team, or a strategy consistently proves unprofitable, cut it and reallocate the capital.

3. Use Data as the Ultimate Aligner

The unblended revenue report becomes the single source of truth. The conversation is no longer “sales vs. marketing”; it’s “us vs. the number.” Use this data to drive incentive structures that reward profitable revenue, not just activity. Tie marketing bonuses to sourced revenue and sales compensation to deals that fit the profitable ICP profile.

From Unblended Data to Unstoppable Growth

Unblending your GTM engine is a fundamental shift to being a truly data-driven GTM team and CRO.

It’s a commitment to move from measuring activity to measuring outcomes. It’s also a commitment of time and resources. It requires investment in GTM Strategy & Operations with dedicated time to do this analysis, often the CRO themselves allocating time as well, and holding a regular Pipeline Council to discuss these insights and take action on them.

This is the path to turning your revenue engine from a source of frustration into a transparent, controllable system that delivers predictable, profitable, and scalable growth.

If you’d like help, whether it’s advice on how to execute this and/or the resources to do it, reach out to us and we’d be happy to discuss how we can support your team.