Turning GTM Metrics into Actionable Execution

How do we make go-to-market metrics truly actionable? Let's explore the importance of context. Leadership teams often have a wealth of data at their fingertips, yet decision-making still leans too heavily on gut feelings and assumptions. It’s the context that transforms raw data into insightful, actionable intelligence.

When evaluating your metrics, here are six key contextual elements to consider:

1. Relation to plan

2. Time horizon

3. Causality

4. Benchmarks

5. Speed

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Additionally, it's critical to ensure that your revenue tech stack is properly configured and instrumented, enabling smooth data flows to inform these decisions effectively. I’d argue that the last few years we’ve had a number of interesting tools enter the fray which has made the math much easier to bring to the table for operators. I distinctly remember having to build out my own databases and layering Tableau on top. Creating fragmented notebooks and sharing it to separate teams was easy enough but became tough once change requests started to come in. Updating each dashboard for each team started to become tedious.

Before diving in I want to thank my partners over at Revlitix for unlocking this article for my readership. Revlitix’s founder Madhu Puranik and I have had the opportunity to do three webinars together (two for this newsletter and one for the COO community within Pavilion). I find his instincts spot on in terms of creating seamless experiences to derive insights and to connect both the top of funnel and acquisition funnel together. And I see that his team is putting in the effort to build a world class experience for both marketing and sales teams. This article extends upon many of the conversations he and I have had together.

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Metrics should tie in with your Plan

When you dig into your results, the first thing to do is measure how your actual outcomes stack up against what you originally planned for. Your plan isn't just a collection of goals—it's built on specific assumptions about how things should unfold.

By comparing reality against those expectations, you can uncover friction points and inefficiencies that are often hidden in the data. Conversion rates are a great example.

A plan I put together for a client back in 2019/2020

Simply looking at charts might not reveal much. But when you overlay your planned performance against actual results, the gaps become clear. These discrepancies show you exactly where your team might be underperforming, and more importantly, where you need to dig in. From there, you can create targeted action plans to close those performance gaps and get back on track. It’s this kind of proactive analysis that turns data into a tool for continuous improvement, not just a report card.

In the example above I had a client that asked me to model what pipeline contribution could look like for their upcoming 2020 fiscal year. As any analyst would do I took a look at their past performance to arrive at a split between inbound and outbound. The eventual modeled number was 70% outbound and 30% inbound. The projected blended deal size was $51K suggesting a mid-market type motion. A typical sales-marketing expense split in this GTM motion would be the same 70/30.

Lecture 2 of Finance for RevOps Course

Now is this how things will turn out? The Plan is a guide for how we think the year will play out. The numbers are grounded in historical data but the past isn’t always prologue. Hence why I think it’s so important to stay close to the metrics each week, month, and quarter to ensure that we are on track. Ideally we’re overperforming.

Timing is everything

Time is one of those key factors you can’t ignore, and it breaks down into three important pieces:

1. Period-over-Period comparisons

2. Trends

3. Triggers or Events

Understanding the time element helps you gauge the speed and direction of change. Take a monthly trend graph, for instance—layer in key events, and you’ll see exactly when and why things shifted. This added context makes it easier to spot the moments that really moved the needle, whether for better or worse. Over time, you’ll get a clearer picture of which strategies are driving results and which ones are holding you back. It’s all about understanding not just what changed, but when and why it did.

The chart above was recently given to a RevOps candidate for an interview. We workshopped a few ideas on what he could say during the interview below.

Here are a few insights from the chart:

1. Outbound Calls vs. Conversion Rate: The number of outbound calls fluctuates throughout the year, with significant dips in June and September, while the conversion rate (MQL to SQL) appears more consistent but with some spikes.

2. High Call Volume vs. Conversion Rate: October shows the highest number of outbound calls, and interestingly, the conversion rate also spikes during that time. This suggests a correlation between high activity and improved conversion rates.

3. Event Impact: December shows a drop in both outbound calls and conversion rate. The dotted lines extending from December suggest an event or a projection moving into January (P1), indicating this might be a key point to watch for shifts in performance.

4. Consistency vs. Performance: Despite fluctuations in the number of outbound calls, the conversion rate stays relatively stable, hovering around the same level (between 21% and 35%). This suggests that the efficiency of converting MQLs to SQLs remains somewhat constant even when activity levels drop.

5. May and June Trend: In May, despite an increase in outbound calls, the conversion rate starts to decline slightly, possibly indicating diminishing returns at higher call volumes, or a need to adjust strategy during this period.

We have no idea if we’re right or wrong. Just that we tried to put context to the timing of these metrics in the context of their actual business.

Causality

Knowing the relationships between metrics allows for GTM teams to create actionable plans to improve performance. For example, let’s say that an organization has a metric called “Stick Rate”. It measures how many demos scheduled are actually held. Here’s how this metric could be defined:

Demos Held / (Demos Scheduled For Today or Earlier - Active Reschedules)

The business has a target of 70% Stick Rate but the team’s performance is hovering above 60%.

What to do?

One method is to break down the stick rate by each individual rep:

• SDR 1: 80%

• SDR 2: 75%

• SDR 3: 70%

• SDR 4: 65%

• SDR 5: 50%

The first thing we should do is to understand the specific behaviors/actions each of these reps are doing. In the course of our investigation we find the following:

• SDR 1, 2, 3: schedules demos no more than 4 days later and sends confirmations on both calendar + SMS

• SDR 4: schedules demos no more than 4 days later but sometimes forgets to send confirmations

• SDR 5: schedules demos for anytime including 2 weeks later and does not send confirmations

Well the solution to address the StickRate metric seems pretty clear now doesn’t it.

1. Enforce a policy of how late one can schedule a demo

2. Automate confirmation processes to eliminate manual processes (uh, forgetfulness? carelessness?)

Benchmarks

Two benchmarks to be mindful of:

1. Industry benchmark

2. Relative to plan

With an industry benchmark you’ll find there are asymptotes to which metrics reach with certain behaviors. Adhering to process and executing well may have limits which metrics will converge to. For example, metrics such as 1/ win rates, 2/ sales cycle, 3/ deal sizes, 4/ meeting held rates. Knowing these benchmarks gives you a sense of whether you are optimally performing or if there is significant room to improve.

Here’s a benchmark graphic from Insight Partners I found to be super slick:

Insight Partners SaaS Sales Periodic Table

Don’t beat yourself up if you’re off the mark. Do something about it!

Here’s their marketing version if you were wondering.

One more for you

Speed to insights

Metrics are like a hot pizza. It’s only good if it’s delivered to you hot. The insights derived from them can render decisions meaningful. The typical RevOps analysts knows their business’ numbers inside and out. But sometimes putting it together takes too long.

Merkle and MarketingCharts.com

The newest tools out there are not only going to be to pull fragmented data together, process the math, but also leverage LLMs as an reasoning engine to develop speedier insights. I’m personally confident (and a little scared?) that machines are going to be able to do the heavy lifting previously done by analysts. But if insights can be churned out 10x faster or 10x deeper than I am all for it.

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