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Not 👏 all 👏 leads 👏 are 👏 created 👏 equal 👏
And, yes, the same goes for opportunities.
Most companies are still looking at all pipeline blended together as if everything is the exact same.
But here’s the problem: When you blend all pipeline or leads together, it’s easy to miss important differences in your most important metrics like win rates, conversion rates, sales velocity, average recurring revenue, and sales cycle length—just to name a few.
Understanding these dynamics allows you to make spicier strategy decisions that can significantly impact your business.
Spoiler alert: Hitting your revenue goals isn’t always about increasing volume. But you knew that already, right?
You’ve probably heard of the Split the Funnel exercise on LinkedIn before. If you haven’t, here’s a quick video from Sarah Perry to catch you up 👇
It’s more common to help companies switch from a lead gen approach to a demand gen approach. This concept is important to understand before you get into pipeline standardization. So, you know, pay attention.
The lead gen to demand generation movement is centered on understanding—using your own data—that different leads convert differently. It’s so simple. It makes sense that leads requesting to speak to sales convert to revenue better than leads who don’t raise their hands.
But, for some reason, in many B2B organizations, goals are blended for the total number of MQLs. To hit goals, they optimize for volume and not quality. This drives misalignment for GTM teams.
Declared intent = Asked to speak with sales
Low intent = Did not ask to speak with sales
It’s a pretty simple concept: Would you rather have more demos or more ebook leads? Yeah, I thought so. It’s pretty clear that demos have higher win rates and sales velocity, plus drive more revenue with fewer resources.
So it’s a no-brainer to separate declared-intent leads from low-intent ones—and set separate goals for each. Here’s a split-the-funnel template you can use at your company to see your own results.
This illustrates the differences in your blended lead funnel to drive alignment with your GTM team and get leadership on board to change the metrics.
For example, routing decisions and optimizing speed to meeting booked. Would you treat both of these leads the same? Heck no. You want to prioritize declared-intent leads and immediately book with your sales team.
You don’t want a prospect waiting to hear from you. On average, B2B SaaS buyers wait two days to get a response to their demo request. Don’t be average!
Using Chili Piper Form Concierge, you can easily execute this and increase your qualified meetings.
You have to balance your follow-up to match the buyer's intent. Showing a sales booking calendar to a webinar registrant would not fit the offer and would be an odd experience for the prospect.
Okay, now that you understand that not all leads are created equal, let’s move on to everyone’s favorite topic: pipeline.
Not all pipelines are created equal. So why on earth are we celebrating pipeline achievements and creating goals as if they are? Adopting a standardized definition that’s not subjective and has quality control metrics will drive cohesion, teamwork, and joint accountability to improve revenue outcomes.
Top three reasons to standardize your pipeline:
Another benefit of standardizing the pipeline is external comparison and benchmarking. We’ll get into that later. First, we’ll focus on how this benefits you internally.
Let’s walk through some examples to illustrate the most common problems.
“If we create this much pipeline, we’ll hit our revenue goals.” Great, in theory, but does all qualified pipeline convert at the same rate? Usually no.
What’s your definition of “qualified” pipeline?
I ask this question to every company I work with. Why? Each company has a different definition, and oftentimes, you hear different answers from different people internally.
“At stage 2 when the demo is complete.” “When the sales team adds products to the opportunity.” “At stage 3, after X is completed.”
Each company's sales process is different, which means that opportunity stages and sales activities at each stage are different to fit that company’s selling motion. That’s why you see such different answers.
The problem with these definitions is that they’re subjective, and there’s no quality control metric that keeps this definition tight.
In most cases, I see “qualified” as being a specific opportunity stage that has specific criteria the sales team is required to validate before putting the opportunity in that stage.
But let’s be honest. What do you think the compliance rate is for ensuring all the requirements are met before a rep puts it into that specific stage? Likely not 90%+.
Depending on the compensation structure, KPIs, and team culture, many different variables come into play here.
In this example, stage 3 is “qualified.” A universal win rate for stage 3 opportunities is calculated. Let’s say it's 20%. Now, they apply that blended win rate to their model and forecast.
Another scenario I see is that companies will have different definitions based on who sourced the deal, and they are all subjective definitions based on opinions or tasks completed.
The solution: Ensure you have the exact definition for all qualified pipelines with a quality control metric. (More on this later…)
Most commonly, companies will separate the pipeline by which department “sourced” it. This is an internal measure usually mapped back to goals, but again, it’s blended and not separated by how the customer came into the pipeline.
Companies do this because it’s how their teams are goaled, how they budget, and how they forecast. How many leads, opportunities, pipeline, and revenue will each team drive?
There are a few issues here:
Breaking your funnel and opportunities out by pipeline sources is a better predictor of sales performance metrics. Why? Because how the prospect converts is a huge indicator of conversion in your funnel.
A pipeline source is how the demand was captured, and you usually have 3-7 core motions. You then have different programs and signals within each that give you more detailed information about the specifics of that source.
Let’s look at the example below. When the pipeline is broken out by source, you can see differences in win rates.
Pipeline source buckets:
Removing the focus on departments and shifting to pipeline sources—what the buyer did to come into the pipeline—removes the emotion and conflict that arise when comparing departments side-by-side.
I’m not talking about channels like organic, direct, or paid search. That is the “where” the deal or lead came from, and we want to focus on the “what” that occurred to get that person into the funnel.
Within the large inbound bucket, there are many different GTM motions. Here are just a few: website hand-raisers, contact scoring, webinar live participants, conferences, localized field events, etc. Speaking of events, check out how Chili Piper booked 97 meetings from one event.
Measure how the buying group enters the pipeline, AKA pipeline sources.
Breaking your pipeline up into these sources will show you which one has the best sales velocity. You’ll be able to see the differences in conversion rate, win rate between stages, and trends you’ve previously missed.
You can look at your historical data to see the differences. We’ve created an automated way to do this at Passetto, but you can run a manual analysis to see for yourself. It won’t be exact, but it will be directional.
The exact how depends on the data and fields you have, but here’s the gist:
Identify the differences in each source compared to department-level analysis. Again, depending on your data, this might take a lot of effort, or you might only be able to do a few pipeline sources but not all.
Pro Tip: When mapping pipeline sources, I’m not talking about channels like organic, direct, or paid search. That is where the deal or lead came from, and we want to focus on what occurred to get that person into the funnel.
Whether you use these exact definitions for pipeline sources or not, you can see that not all pipelines are created equally. This is why I recommend using a standardized definition for pipeline.
When you forecast on an objective standardized data value, you increase the accuracy of your forecast or model.
When you look at things blended, it’s easier to get lower-quality deals, which incentivizes teams to focus on volume. Volume is not the goal; efficiency should be the goal.
You should apply a standardized definition across all sources so that when anyone in the business says pipeline, it means the same thing. If the quality of the pipeline changes over time, your definition and model should account for this.
Let’s say your stage 3 win rate steadily decreases over time. Are you frequently updating your model and accounting for that? Are you changing the definition to stage 4?
Usually, people don’t catch this early enough so the model ends up being wrong or less accurate.
Finally, let’s get to the definition! HIRO is a standardized definition of “pipeline” with a dynamic quality control metric to guarantee all pipelines are measured and valued equally.
Definition: An opportunity that has a win rate of greater than 25% in a rolling 2x sales cycle length
If your sales cycle is 90 days, then you would calculate for a rolling 180 days.
The best part about HIRO is that it uses your own historical data for win rate to ensure your definition is standardized. If you get better and increase win rates, your HIRO stage will adjust.
The definition uses 25% because it means that you can confidently expect one in four deals to result in revenue.
Let’s revisit the previous example and apply a HIRO pipeline definition. You can see that the website HIRO stage is 2, outbound is 4, product is 2, events is 3, low intent is 4, and partner is 2.
Using the HIRO dates and definition, you’re now comparing apples to apples when looking at the website pipeline created and the event pipeline created.
The HIRO definition is based on stage win rates so you’ll want to calculate win rate by opportunity state. I recommend that you have opportunity stage date and time stamps to do this. The HIRO stage will be unique to each company. I see patterns after doing this analysis with 50+ customers but let the data speak for itself.
You would apply this definition to each pipeline source at the global level.
First, you’ll want to get all the opportunity stage data and know which opps are mapped to which pipeline sources. Or at least which filters to use in your CRM reports to segment opps from each pipeline source.
The methodology: Look at a set of closed opportunities in a specific time period. Then, understand if those opportunities reached each stage during the lifecycle.
You’ll pull all closed opps (closed won and lost), so the date filter will be closed date, for the time period (180 days in the example above) and apply any filters that segment the data to the specific pipeline source.
Sometimes, this is a combination of 2-3 fields.
You’ll end up with a closed opp data set for each pipeline source. You’ll run these steps below for each pipeline source data set.
Quick reminder: Win rate = # of won opps in time period / # of total closed opps in time period
To calculate this by stage, you'll need to know if each opportunity has reached that stage at any time for that closed-won opp. If you have stage dates or stage checkbox fields, you’ll be able to see if that opportunity was in stage 2 (at any point, not in the closed period).
At Passetto, we’ve automated this calculation to examine your historical data and monitor it to adjust any changes in HIRO data in real time. If you are doing this manually, I recommend re-running your HIRO win rates to adjust any needed workflows every 180 days or one sales cycle length.
Once you reach the stage that is greater than 25%, that stage is your HIRO date for that pipeline source. Stage 3 = HIRO Date in this example.
You then stamp a HIRO date field with the Stage 3 date for that pipeline source.
[ If pipeline source = X, Copy Stage 3 Date to HIRO Stage date field. ]
I recommend using a separate field to stamp this value because your HIRO dates could fluctuate over time and change. It could go up if you’re driving more educated, qualified buyers or if your sales teams get better. It could also decrease and push your HIRO stage date back. I recommend re-calculating this every quarter or sales cycle.
The best part about this standardized pipeline definition (HIRO) is it uses your own historical data for win rate, which is a dynamic quality control measure to ensure your definition is standardized.
I encourage you to run this and compare it to your definition of “qualified” pipeline.
Things to consider:
Shifting away from a blended funnel approach and adopting a standardized definition of “pipeline” allows you to see the dynamics in your funnel to more reliably forecast, resource, and plan your revenue programs. An added bonus is that it drives sales and marketing alignment. Who doesn't need help with that?
Not 👏 all 👏 leads 👏 are 👏 created 👏 equal 👏
And, yes, the same goes for opportunities.
Most companies are still looking at all pipeline blended together as if everything is the exact same.
But here’s the problem: When you blend all pipeline or leads together, it’s easy to miss important differences in your most important metrics like win rates, conversion rates, sales velocity, average recurring revenue, and sales cycle length—just to name a few.
Understanding these dynamics allows you to make spicier strategy decisions that can significantly impact your business.
Spoiler alert: Hitting your revenue goals isn’t always about increasing volume. But you knew that already, right?
You’ve probably heard of the Split the Funnel exercise on LinkedIn before. If you haven’t, here’s a quick video from Sarah Perry to catch you up 👇
It’s more common to help companies switch from a lead gen approach to a demand gen approach. This concept is important to understand before you get into pipeline standardization. So, you know, pay attention.
The lead gen to demand generation movement is centered on understanding—using your own data—that different leads convert differently. It’s so simple. It makes sense that leads requesting to speak to sales convert to revenue better than leads who don’t raise their hands.
But, for some reason, in many B2B organizations, goals are blended for the total number of MQLs. To hit goals, they optimize for volume and not quality. This drives misalignment for GTM teams.
Declared intent = Asked to speak with sales
Low intent = Did not ask to speak with sales
It’s a pretty simple concept: Would you rather have more demos or more ebook leads? Yeah, I thought so. It’s pretty clear that demos have higher win rates and sales velocity, plus drive more revenue with fewer resources.
So it’s a no-brainer to separate declared-intent leads from low-intent ones—and set separate goals for each. Here’s a split-the-funnel template you can use at your company to see your own results.
This illustrates the differences in your blended lead funnel to drive alignment with your GTM team and get leadership on board to change the metrics.
For example, routing decisions and optimizing speed to meeting booked. Would you treat both of these leads the same? Heck no. You want to prioritize declared-intent leads and immediately book with your sales team.
You don’t want a prospect waiting to hear from you. On average, B2B SaaS buyers wait two days to get a response to their demo request. Don’t be average!
Using Chili Piper Form Concierge, you can easily execute this and increase your qualified meetings.
You have to balance your follow-up to match the buyer's intent. Showing a sales booking calendar to a webinar registrant would not fit the offer and would be an odd experience for the prospect.
Okay, now that you understand that not all leads are created equal, let’s move on to everyone’s favorite topic: pipeline.
Not all pipelines are created equal. So why on earth are we celebrating pipeline achievements and creating goals as if they are? Adopting a standardized definition that’s not subjective and has quality control metrics will drive cohesion, teamwork, and joint accountability to improve revenue outcomes.
Top three reasons to standardize your pipeline:
Another benefit of standardizing the pipeline is external comparison and benchmarking. We’ll get into that later. First, we’ll focus on how this benefits you internally.
Let’s walk through some examples to illustrate the most common problems.
“If we create this much pipeline, we’ll hit our revenue goals.” Great, in theory, but does all qualified pipeline convert at the same rate? Usually no.
What’s your definition of “qualified” pipeline?
I ask this question to every company I work with. Why? Each company has a different definition, and oftentimes, you hear different answers from different people internally.
“At stage 2 when the demo is complete.” “When the sales team adds products to the opportunity.” “At stage 3, after X is completed.”
Each company's sales process is different, which means that opportunity stages and sales activities at each stage are different to fit that company’s selling motion. That’s why you see such different answers.
The problem with these definitions is that they’re subjective, and there’s no quality control metric that keeps this definition tight.
In most cases, I see “qualified” as being a specific opportunity stage that has specific criteria the sales team is required to validate before putting the opportunity in that stage.
But let’s be honest. What do you think the compliance rate is for ensuring all the requirements are met before a rep puts it into that specific stage? Likely not 90%+.
Depending on the compensation structure, KPIs, and team culture, many different variables come into play here.
In this example, stage 3 is “qualified.” A universal win rate for stage 3 opportunities is calculated. Let’s say it's 20%. Now, they apply that blended win rate to their model and forecast.
Another scenario I see is that companies will have different definitions based on who sourced the deal, and they are all subjective definitions based on opinions or tasks completed.
The solution: Ensure you have the exact definition for all qualified pipelines with a quality control metric. (More on this later…)
Most commonly, companies will separate the pipeline by which department “sourced” it. This is an internal measure usually mapped back to goals, but again, it’s blended and not separated by how the customer came into the pipeline.
Companies do this because it’s how their teams are goaled, how they budget, and how they forecast. How many leads, opportunities, pipeline, and revenue will each team drive?
There are a few issues here:
Breaking your funnel and opportunities out by pipeline sources is a better predictor of sales performance metrics. Why? Because how the prospect converts is a huge indicator of conversion in your funnel.
A pipeline source is how the demand was captured, and you usually have 3-7 core motions. You then have different programs and signals within each that give you more detailed information about the specifics of that source.
Let’s look at the example below. When the pipeline is broken out by source, you can see differences in win rates.
Pipeline source buckets:
Removing the focus on departments and shifting to pipeline sources—what the buyer did to come into the pipeline—removes the emotion and conflict that arise when comparing departments side-by-side.
I’m not talking about channels like organic, direct, or paid search. That is the “where” the deal or lead came from, and we want to focus on the “what” that occurred to get that person into the funnel.
Within the large inbound bucket, there are many different GTM motions. Here are just a few: website hand-raisers, contact scoring, webinar live participants, conferences, localized field events, etc. Speaking of events, check out how Chili Piper booked 97 meetings from one event.
Measure how the buying group enters the pipeline, AKA pipeline sources.
Breaking your pipeline up into these sources will show you which one has the best sales velocity. You’ll be able to see the differences in conversion rate, win rate between stages, and trends you’ve previously missed.
You can look at your historical data to see the differences. We’ve created an automated way to do this at Passetto, but you can run a manual analysis to see for yourself. It won’t be exact, but it will be directional.
The exact how depends on the data and fields you have, but here’s the gist:
Identify the differences in each source compared to department-level analysis. Again, depending on your data, this might take a lot of effort, or you might only be able to do a few pipeline sources but not all.
Pro Tip: When mapping pipeline sources, I’m not talking about channels like organic, direct, or paid search. That is where the deal or lead came from, and we want to focus on what occurred to get that person into the funnel.
Whether you use these exact definitions for pipeline sources or not, you can see that not all pipelines are created equally. This is why I recommend using a standardized definition for pipeline.
When you forecast on an objective standardized data value, you increase the accuracy of your forecast or model.
When you look at things blended, it’s easier to get lower-quality deals, which incentivizes teams to focus on volume. Volume is not the goal; efficiency should be the goal.
You should apply a standardized definition across all sources so that when anyone in the business says pipeline, it means the same thing. If the quality of the pipeline changes over time, your definition and model should account for this.
Let’s say your stage 3 win rate steadily decreases over time. Are you frequently updating your model and accounting for that? Are you changing the definition to stage 4?
Usually, people don’t catch this early enough so the model ends up being wrong or less accurate.
Finally, let’s get to the definition! HIRO is a standardized definition of “pipeline” with a dynamic quality control metric to guarantee all pipelines are measured and valued equally.
Definition: An opportunity that has a win rate of greater than 25% in a rolling 2x sales cycle length
If your sales cycle is 90 days, then you would calculate for a rolling 180 days.
The best part about HIRO is that it uses your own historical data for win rate to ensure your definition is standardized. If you get better and increase win rates, your HIRO stage will adjust.
The definition uses 25% because it means that you can confidently expect one in four deals to result in revenue.
Let’s revisit the previous example and apply a HIRO pipeline definition. You can see that the website HIRO stage is 2, outbound is 4, product is 2, events is 3, low intent is 4, and partner is 2.
Using the HIRO dates and definition, you’re now comparing apples to apples when looking at the website pipeline created and the event pipeline created.
The HIRO definition is based on stage win rates so you’ll want to calculate win rate by opportunity state. I recommend that you have opportunity stage date and time stamps to do this. The HIRO stage will be unique to each company. I see patterns after doing this analysis with 50+ customers but let the data speak for itself.
You would apply this definition to each pipeline source at the global level.
First, you’ll want to get all the opportunity stage data and know which opps are mapped to which pipeline sources. Or at least which filters to use in your CRM reports to segment opps from each pipeline source.
The methodology: Look at a set of closed opportunities in a specific time period. Then, understand if those opportunities reached each stage during the lifecycle.
You’ll pull all closed opps (closed won and lost), so the date filter will be closed date, for the time period (180 days in the example above) and apply any filters that segment the data to the specific pipeline source.
Sometimes, this is a combination of 2-3 fields.
You’ll end up with a closed opp data set for each pipeline source. You’ll run these steps below for each pipeline source data set.
Quick reminder: Win rate = # of won opps in time period / # of total closed opps in time period
To calculate this by stage, you'll need to know if each opportunity has reached that stage at any time for that closed-won opp. If you have stage dates or stage checkbox fields, you’ll be able to see if that opportunity was in stage 2 (at any point, not in the closed period).
At Passetto, we’ve automated this calculation to examine your historical data and monitor it to adjust any changes in HIRO data in real time. If you are doing this manually, I recommend re-running your HIRO win rates to adjust any needed workflows every 180 days or one sales cycle length.
Once you reach the stage that is greater than 25%, that stage is your HIRO date for that pipeline source. Stage 3 = HIRO Date in this example.
You then stamp a HIRO date field with the Stage 3 date for that pipeline source.
[ If pipeline source = X, Copy Stage 3 Date to HIRO Stage date field. ]
I recommend using a separate field to stamp this value because your HIRO dates could fluctuate over time and change. It could go up if you’re driving more educated, qualified buyers or if your sales teams get better. It could also decrease and push your HIRO stage date back. I recommend re-calculating this every quarter or sales cycle.
The best part about this standardized pipeline definition (HIRO) is it uses your own historical data for win rate, which is a dynamic quality control measure to ensure your definition is standardized.
I encourage you to run this and compare it to your definition of “qualified” pipeline.
Things to consider:
Shifting away from a blended funnel approach and adopting a standardized definition of “pipeline” allows you to see the dynamics in your funnel to more reliably forecast, resource, and plan your revenue programs. An added bonus is that it drives sales and marketing alignment. Who doesn't need help with that?
Sidney Waterfall serves as the SVP of Go-To-Market (GTM) Strategy at Passetto, where she excels in data analysis and crafting revenue-generating strategies. Boasting more than a decade of expertise, Sidney has developed demand generation engines, scaled GTM initiatives resulting in acquisitions, and led sales teams to success. She is deeply committed to optimizing funnel efficiency, leveraging data insights, and driving sustainable growth for businesses. You can follow and connect with Sidney on LinkedIn.
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