All right, so today we're gonna talk about pipeline velocity or sales velocity, however you want to call it. The reason we thought we talked about this topic is, every time we post about it, whether it's myself or Allison or Chris or the Refinery Labs page, or anytime we bring it up with a customer account or in any other audience, we get a lot of interest in this topic, we get a lot of questions about it. And what we've noticed is it's like a pretty hard concept to understand, as well as understanding where to implement it. So what we thought we do is just go through this a lot more detail today.
Allison is going to walk through a presentation where we'll ask our questions, and really what we want all of you to do is ask all the questions that you might have regarding pipeline velocity, and we'll try to see if we can demystify it as best we can. That's not good, so just drop your questions into the chat, feel free to interrupt us as we go. But before I pull up some slides for Allison to go through, I'm going to have our esteemed panelists introduce themselves. We'll start with Ashley.
Hi everyone, my name is Ashley Lewin, and I'm a senior director of demand gen here at Refinery Labs, and I'll pass it over to Carl. Thank you Ashley. Hey everyone, my name is Carl Ferreira, I leave a sales org here at Refinery Labs, super pumped to talk about pipeline velocity. My name is Allison Lewin, and I am a VP of demand generation here at Refinery Labs.
I spent a lot of time over the last year really perfecting the way that we use pipeline velocity to effectively understand root causes of our overall demand generation strategy and how we can utilize the different components of this equation to make some significant adjustments, changes, pullbacks on our strategy as we move forward. So really excited to share some of that knowledge with you all today and answer any questions that you may have. Awesome, thanks Allison. So as Allison mentioned, she's going to go through some slides.
Her and Ashley will give you context how we use this with our customers. I thought Carl would be important to have on there because when you see the components of pipeline velocity, this is a lot of what sales talk about. So I want to make sure that Carl and a little bit myself kind of give context of why this formula is important and how the sales teams look at it. And then I can add any color from my past experiences as well as how we use it within Refinery Labs.
Over to you, Allison. Cool, so let's go ahead and start on that before you begin slides. So advance a little bit forward here. And I always throw this in when I talk about reporting because I have definitely ran into those situations where marketing sales product, customer success, everyone's sort of talking from a different report, a different playbook, a different set of nomenclature.
So before you even begin, it's just incredibly critical to establish those shared KPIs and make sure you're utilizing a single source of truth with your GTM team. So you want to make sure in these examples that I'll be showing you, we are using our pipe data standard nomenclature. So if you've been following along with refine labs, you're going to hear things like pipe inversions, pipe qualified meetings, hero opportunities, but you need to make sure that you're defining and documenting your own nomenclature within your own business. And then just make sure that you kind of highlight your time frame up front and you're always pulling from that single source of truth.
It's just going to help those conversations stay really productive on the actual data and not so much of like how you generated the data or where it came from. So when we think about pipeline velocity, if you want to go sort of the next slide here, just to open it up through the calculation up again. But essentially, it is a calculation that is really looking at a couple of different metrics. So come up with this overarching number, which essentially is just how quickly is pipeline running through your, how quickly is AR running through your pipeline at any given point in the time period that you are measuring.
And it's calculated based off of win rate, total qualified opportunities, ACV, and then divided by sales cycle, divided by the days in the analysis period. For some reason, that last part days in the analysis period really throw people off sometimes. So I'll do my best to kind of sum that up, but any refined lab teammates want to jump in, because even I struggle sometimes. So for example, if I'm looking at pipeline velocity for the year, I'm going to divide by 365 days.
If I'm looking at pipeline velocity for the quarter, I'm going to divide by 90 days, roughly 91, I think is like the universal average you can use. If I'm looking at it monthly, which we do not recommend doing, you can do by 30 days. And then of course, if you just want to see how much revenue is flowing through our pipeline on a daily basis, you would not divide by any days in the period at all. So we always recommend looking at pipeline velocity from that quarterly perspective, which is why our calculations are almost always divided by 90 days.
I will pause there because I feel like that's usually a point where people have a lot of questions. So we can answer some of those. One other thing before we jump into questions is I typically see on the win rate side, this will be like area that trips people up. So win rate, what we recommend is doing the total close one by total close close close, so one and loss, not total one divide by total creative, because that will account for any deals that are so pipeline too.
So if you're kind of struggling with this equation, I would recommend looking at your win rate and how you're calculating that, because I might be throwing it off too. That's a really good point. I also want to highlight that all of this data is fake data that I created, tried to do my best to demonstrate a dip in Q2, which most of us probably experienced on this call, but just to get that out of the open too. And then I see a question came in just to explain one more time, actually, around win rate.
Yeah, I'll say it again too here. So when you're doing the win rate, you'll want to do it in the designated period. So you'll do the total close one divided by total close. So that would be your one in loss.
And again, that is just to attribute it for any deals that are so pipeline and so on. You know, always have to account for that too. Awesome. Yeah, I would echo that.
That's a key thing to understand in general when you're talking win rate is that calculation, meaning taking out the deals that are still open to kind of give you a true sense. You'll see people doing win rate looking back even like in the existing month, and they'll calculate it from the number of wins over this little pipeline. And it'll see the number being really low. And so you need to kind of normalize that by looking at what's close versus what's close one versus close loss.
The other question I have is something I've gotten this question before. We did not talk about this ahead of this discussion. And that is, how do you think of the ACV? And what I mean by that is, you know, some companies will run an MRR model, some will run an ARR model, some might be project based in terms of there's not necessarily guaranteed contractual value.
So I think it they are. I think if somebody signing up for 12 months that revenue is secure, whereas somebody may be running a monthly business and that revenue is really only secure for 30 days, or maybe 60 days that there's now versus like a business like ours where like, like find value can vary based on the type of project or program or running it by customer. So when you think of ACV in that context, how do you think about it? Any rules of them as people kind of think about it for their specific business?
So typically for me, if it's an MRR business, I am multiplying that MRR by 12. So I do like to bring it to an annual revenue number. The other thing that is kind of interesting, I've seen companies that will do like, they'll have an implementation fee plus a monthly subscription fee. So to normalize it, I will just usually do the subscription fee and multiply by 12 or just take sort of like the yearly value of the contract.
I think that's really important to kind of the first slide where it's like, make sure that you define how you're going to define ACV. What does that look like for your company? And as long as everybody is defining ACV in the right way, then you should be able to pick one method and move with it. Yeah, like that.
I think that to me, the takeaway is that consistency, making sure that you've clearly identified how you're measuring these things because really what you're looking at is like, how this number changes over time versus like the absolute number, in my opinion. I mean, somebody can push back on that. But all right, great. Awesome.
We're to next. Before we go to the next slide, just a quick note, I did mention it, but we really recommend looking at pipeline velocity in a quarter over quarter view. A month over month view will oftentimes look volatile, especially beginning of month or in like summer months when people are taking holidays. And then maybe you're closing your deals late September.
So to avoid making some knee jerk reactions or potentially identifying false positives or negatives, we really do recommend looking at this in a quarterly perspective. If you have a less than 30 day sales cycle and there's a lot of urgency at your company to look at this at a monthly view, I always recommend adding in a trend line in your, if you use Excel or using Google Sheets, that can sort of help normalize any of those peaks and valleys, which is really important not to like necessarily, if you're seeing a dip in a particular month, just kind of be aware of your sales. How long is your sales cycle and is that really indicative of what's happening? Awesome.
I unmuted Tatiana who has a question. Tatiana, do you want to come on and ask your question? Yeah, thank you for letting me come on. So just want to understand, the win rate, that is the win rate of everything in the entire pipe or the win rate of just you know, identified, qualified opportunities at the very end of the, of the process.
And also, I know you've got these numbers on the right and I know they're fictitious, but it would have been good to have, you know, the numbers which have gone in, even though they are just dummy numbers, like the win rate, you know, okay, I'm sorry. Yeah, no, no, no, no, it's crazy. Yeah, so just the win rate is that for every single opportunity in the matter how qualified they are. And also, it's a win rate for that period, right?
So the win rate of everything in the entire pipe for the previous 90 days only. I'm just, I just want to understand about that, right? Yeah, okay. So let's actually advance to the next slide because I think you're bringing up a really great distinction.
So what are you calculating pipeline velocity for? Ashley, do you want to speak about the different sort of metrics, how you can sort of segment this? Yeah, so pipeline velocity, when it is the most useful is how you're going to use the data. Like when you think about it from a strategic standpoint or reporting standpoint, I like to do it into a couple different views.
And one of these is, this is the pipeline source view. So this will be all the ways that you're capturing demand and you're, I'm just going to pick on new acquisition here, your new acquisition revenues, you could have like cold outbound partner pipe or declared intent, low intent lead gen events, field, AVM, and et cetera. And so to answer your question on the win rate, so I like to stack these up against each other. And there's another view that we'll get into in the next slide, but we won't advance yet.
And like if you want to do it by points of conversion, like if you're splitting the funnel too. But when I look at it this way, it's really nice to kind of partner them up together. And I think the biggest thing to remember is the philosophy around it. And I like to look at it as almost like a scorecard to measure the different sources against each other.
We're always kind of trying to figure out how they compare against each other. And there's nuances to data, you know, everything is a little bit different, kind of like comparing apples to oranges and pipeline velocity for just that gap. And we're able to compare things on a much even, a much more even playing field. And so for the win rate back to your question here, this will be based on the qualified opportunities that are created within that period and that were closed.
So, you know, if we're doing it in a quarter view, it'll be any of those qualified opportunities that were created and pipeline sources were picking on this view, and then that were closed. You don't want to do it based on the closed date because that could be something that was created two years ago or whatever it is, because you know, sales cycle links can vary for your deal. But I want to make sure I'm keeping myself honest here. Cassidy, Carl Allison, is this how you look at it too?
Yeah, that's how I identify total apps created is by the created date in the period. Yeah, I think you can look at it both ways. I do like it much more cohorted, which is kind of what Ashley is saying, which is I'm looking at, I really want to understand the quality of what was created and closed within a quarter. And so I'm going to do it exactly how Ashley outlined it.
There could be another view where I'm looking at what's closed and I'm just trying to see a trend over time. You just need to know the difference. You just need to know that one's cohorted and one's I would call kind of semi-covert-covert because you're cohorting the opportunity to not cohorting the win rate. So that will give you, I would say not as accurate a view, but it could still be a view that's worthwhile.
But when in doubt, I would cohort it, if that makes sense to people. Yeah, and these inputs are kind of like the really important part of the calculations. The calculation is great to understand what the velocity is. So figuring out how much and how fast of a flow is moving through your pipeline.
So you have that general understanding, but I get really excited about the inputs that make up the equation. And that's where I feel like you can make a lot of your strategic moves and decisions based on. So for instance, if you move that sales cycle length, if you decrease that, or if you improve your ACV, you're going to power that pipeline velocity up much faster and you're going to be delivering a lot more revenue a lot faster. So the inputs is actually where I get the most excited about.
The calculation is great. I think you need to have a general understanding of it, but it's really comes down to these inputs and then how you can move them along as well, because each one of your sources or points of conversion will have varying input lengths. For instance, cold outbound will have a longer cycle, but it may have a higher ACV versus your pipe and so on. So it's just really good to have an understanding of that.
So when you go into whole setting or planning or whatever, you know these so that you can strategically input them accordingly. I think one of the things that jumps out of me is with this slide and going next one as well. And that is maybe you can elaborate, Ashley, Alison, why you use this. And I think just using this as an example, like there are places where you use pipeline velocity and there are places that you don't.
But from a standpoint of comparison, it's an important measure, because I think Ashley said it kind of standardizes how you can compare what is normally different things together on a kind of common metric. Maybe if there's anything you want to elaborate on that. This is one way to do a pipeline source as an example. Yeah, I mean, there's a couple of main reasons.
Number one, this is a really powerful way, kind of Ashley, I like she used the word scorecard. If you are a demand generation leader who is really trying to think of a larger change management strategy within your organization, maybe you've been running a lot of low-intent legion, you've been utilizing your demand creation channels for really more demand capture activities. And you're not seeing that return in the way that you would if you were to optimize around a high intent hand-raiser, such as your request to demo or contact us form. So I like to break apart pipeline source that way to really effectively communicate with leadership why they should consider a more holistic demand creation strategy and really use paid social channels or social channels in general, podcast channels, community channels as these opportunities to create demand and not really try to capture capture email addresses, capture information that's never going to convert.
So that's one major way that we utilize this even within our own clients to sort of help them sunset some of those low-intent legion opportunities. The other way that I use this is exactly how Ashley mentioned too is I look at the individual inputs and as we progress in a few slides, we're going to talk about questions you can ask yourself within each of these different buckets to help assess the health of strategies. So I think it's a balance of kind of the holistic number of pipeline velocity which pipeline source is performing better than another source and then it's sort of the sum of the parts. Like where can I go to make incremental gains to help certain pipeline sources progress forward?
Another good question we have from Nina. When you look at total opportunities, are you thinking of this as an MQL and SQL? I know we may call it a hero, or a PQM. What's your recommendation there when you think about breaking this down?
Yeah, if you can get to your hero, which that means the deal stage that I converts historically, especially on a rolling six-month basis, of 25% of graders, what we call a hero, definitely do that. So it'd be the total hero that creates whatever that deal stage, like they entered that deal stage at date stamp is. If you can't get to that data, which I know it requires a little bit of ops work, it would just be when a deal is created. So it's not MQLs, it's not SQLs, it's purely on the opportunity level.
That's a great question. Yeah, the only thing I want to add is you want to make sure whatever that is, it's consistent. So there's a consistent definition of what is an opportunity and how it's created to make sure you're comparing these things, apples, if you can't get to like a hero win rate or an SQL or something. Yeah, absolutely.
Cassidy, can you advance the next slide? I want to do a couple of calls for this, especially like if you're looking at ways to utilize this equation. Another really great way you could be doing this is say you are performing a split the funnel analysis, figuring out for 2023 where you want to focus your marketing efforts for performance and revenue. One way I really like to do this is to layer on the pipeline velocity results per point of conversion.
So the point of conversion is anywhere that you are going to collect the prospects information when they enter into the pipeline. So here's a couple of examples here. So you know like demo requests, gated content, paid social lead ad forms, so on. I like to break these out and do a pipeline velocity equation on each one of these two because it puts a lot of your performance into context as well.
And you can see all the different inputs to it as well. So for instance, the total ops created, the win rate on the ACB, it allows you to have a really nice pulse on what your performance is and say you need a little bit more leadership buy and to switch from running those lead ad forms to the demo request. When you put this pipeline velocity result up against it and then you put in how much resources or how much spend you've been allocating for those paid social lead ad forms, I feel like it's a very strong persuasion piece, especially when you show just like how much is going through that pipeline versus the efforts that are being put towards it. So you know, when you're looking at different ways that you can utilize this, I highly recommend doing it on a point of conversion level as well, especially if you're looking to split your funnel and get some insights on it.
I really like this example. A couple questions. So Chris and Asa, if we could just go through the definition again, could you just kind of go through the pipeline? Chris was that definition related to the hero opportunity?
Yeah, okay, the hero opportunity. Ashley, you want to say that one again? Of course. So your hero opportunity is any deal stage that has historically converted to close one, a 25% work reader.
If you are, you know, stamping those dates, it definitely requires you to be stamping your dates within your CRM. So if you're not doing that, pause, highly recommend implementing the operational piece. But again, it's any opportunity that is in X deal stage that converts a 25% work reader. It doesn't mean when the deal is created.
Deal creation can mean very different things to many different companies. Like you could be very generous with when you open a deal versus very strict when you open a deal. So it's a really nice quality metric, especially when you're trying to compare multiple industries, companies, and so on to have that kind of like general quality metric right there. Great.
Typically stage two or three, which that lines up with what I see as well. A question from Megan from Total Off-Crated. Is this by the first touch? How they enter the pipeline or most recent?
Is someone if someone does multiple then is it just the first? So I think it's going back to the pipeline source. I need to find kind of the pipeline source or in this case, the point of conversion. But I would do first, but you could do it in many different ways to attribution models are usually a hot topic.
And they tend to be a little bit of a barrier of entry to get these actual, actual insights. So you could run this multiple ways if you really want to. I like to do it on the first point. So the first point of conversion when they enter into your funnel.
So when you're collecting those, the prospects information, you could run this on last touch if you would like. You could do multi-touch if you want to as well. If they interacted with any of these, I've seen that done too. Just whatever you decide back to Cassidy's point earlier, just make sure it's consistent.
But I definitely recommend the first place that you're capturing the information. But yeah. We had a question off of, I assume, Lincoln Live. Will you ever split out different segments like mid-market in enterprise for this?
Actually, yes. A lot of times I will because typically the average sales cycle might be drastically different selling into enterprise versus mid-market versus SMB. Also, if you have, I have a client who has like 15 different products. So we'll separate the pipeline velocity out by product if we want to look at specifically by product.
And then we may just do one altogether to assess the overall health of the in-bum program at a holistic level. So yes, you can always use segmentation to your advantage. I do think it's just again, defining upfront what you're looking at. I think also too, sometimes like an enterprise deal, if it has a longer sales cycle or if it has a higher ACV, you could potentially get some false negatives or false positives within those individual inputs.
So I do like to be cognizant of that. It all just depends though if historically those inputs aren't much different from each other, then it probably won't matter. But if you know that there's a lot of variance between the different markets, then you'll likely want to segment it. Yeah, I would add I've used this in the past to determine what's our strategy going to be?
Is it going to be SMB commercial or is it going to be enterprise based on the early market? And this is a calculation we've actually used in board meetings to kind of prioritize the following years kind of activities. You can do that the same way for a product as well. Industry, different segments, geography.
It's really great. If you look at NOS, Carl, this, if you look at a chart like this, it's hard to say like what is the priority, in some cases, you have more ops created and some ACV is higher. Some have a sales cycle that's faster when rate is higher. This is the constant sales conversation.
This is what we talk about all the time. Number of ops, how big of the deal is, how fast it closes, how often do we win over and over as a sales team? We're like Carl, elaborate on that. Something needs to be able to normalize this data, whether you're looking at points of conversion, pipeline source, product, segment, segment, industry, et cetera.
And I have a ton of better calculation for doing that to have a conversation where you're comparing apples to apples. But that's just kind of my two cents. Carl, any thoughts on that? Yeah.
So Mike brought this up in a couple slides ago where it can look like, if you're looking at the numbers broken out like this without pipeline velocity as kind of that North Star, it can look like something like outbound might perform better than pipe or high intent inbound because maybe the ACVs are a little bit higher or maybe the number of ops is high, better created because sales is just like opening stuff up, right? But we don't know how quality it is until it closes. And you can see kind of that final pipeline velocity number. I don't know if anybody's nerded out on this pipeline velocity formula at all.
But some of the inputs impact velocity at a greater percentage than others. And one of them, one of the most impactful metrics on the pipeline formula is days to close, which are typically really dramatic, dramatically different in outbound versus inbound. Like just closing a deal, let's say in a quarter, if you're using the time period is 91 days and your average sales cycle on outbound is 45 days and your average sales cycle on inbound is 30 days, just that 15 day change increases your pipeline velocity by 20 to 40%. So it's a very outsized impact that it makes just closing a deal a little bit faster, even though when you look at the numbers broken out like this, you're like, Oh, outbound totally wins because there's more ops being created and the ECBs are higher than inbound inbound isn't any good, but inbound's closing 40% faster driving up and bloating the pipeline velocity very dramatically, not to mention all the other things that, you know, are outside of pipeline velocity, like the cost, right?
How long does it take sales headcount, et cetera, all these things back to it as well. But anyways, yeah, different metrics impact the formula differently, which is kind of interesting to dive into that. Yeah, Carl, you stole my thunder. I was going to ask this question of the audience today, if they remember this from math, math class, and that was which in this equation, you know, which has the biggest impact in increasing pipeline velocity, but you already answered it.
Sorry, Cassidy, maybe some people don't know. Sorry for those who don't know. Yeah. Allison, I cut you off.
Go for it. Yeah, no, we have a question earlier that said it would be helpful to see sort of the individual inputs on that first slide. So I'm a big data visualization person. I love to look at charts in different ways.
I love to present data in different ways to try and help the overall clarity of the story of the message I'm trying to get across. And so on the next couple of slides, we've provided some some ways that you can look at these individual inputs and some questions that you can ask yourself to sort of help answer questions about why your pipeline velocity is increasing or is decreasing and some actions that you can take to hedge against some of those fluctuation fluctuations. So maybe we can jump into some different visualizations. Awesome.
So one of the things I really like to do is look at sort of my opportunity volume trends in a couple of different ways. So here I just like to stack up my total opportunities created against the total closed one and total closed last, just to understand individually where the peaks and valleys, where are we creating less opportunities overall in that green bar there? Are we closing lost more opportunities from prior prior quarters? Are we winning more opportunities from prior quarters?
I kind of like to lay this out just to help me understand and visualize because the reason why I chose these three metrics, obviously total opportunities is part of the pipeline velocity equation, but close lost and close one is what helps you get to that win rate number, which is also part of the equation. So I pick that little piece apart even more to understand my win rate, some of my win rate trends. And so this is just one visualization I like to look at and just a couple of questions I like to ask myself when I see pipeline velocity decreasing, rather than just sort of stating like, oh, it's macroeconomic factors. And yes, we know that the economy has been tough, especially for SaaS companies, the last two quarters.
But there are always some other factors that play, other things that we can look at to solve for those problems and so this kind of helps you go a little bit deeper. One of my favorite questions to ask myself is two favorite questions. Have I invested enough in demand creation or am I relying a lot on demand capture? If I'm relying a lot on demand capture, maybe we're in a period of time where people just aren't searching for you, searching for your solution, budgets are on hold, things are on, are being pushed back.
And so I really need to get that urgency in a place where people consume information and make sure that I'm generating enough demand moving forward. So that's another question I'm looking at from a budget perspective, where am I allocating my funds? Another kind of question that I like to ask would be, how we reach the point of diminishing returns? Like this particular fake example, have they kind of peaked on some of those core channels like LinkedIn and Facebook and Google, do they perhaps need to diversify their channel mix to create more opportunities?
We've seen a slight decrease in Q2, but then they were able to recover in this example in Q3. So maybe that is the answer. Maybe it's not, but these are sort of questions that as a demand generation professional, I like to ask myself so that I can arm myself with any roadblocks, any asks, any requests from my leadership team to sort of help move forward in Q4 and beyond. So this is one particular chart.
You want to cruise through some of the others? Yeah, that's great. For sure. Again, just things like your ACV trend.
If you see ACV going down for whatever reason, is this perhaps indicative of, again, a macroeconomic factor? Are people more hesitant to buy sort of your full package and they're opting for maybe a smaller sort of entry level package? Do you need to work with your customer success team so that you can have sort of a land and expand strategy for clients who are buying it a lower ACV than normal? I think these are just all larger questions.
Is anyone who does data visualization know knows? It's very difficult to come up with fake data. So maybe some of the ACV drops in this example aren't as drastic as they potentially could look to answer some of these questions. But in general, these are what I like to ask when I'm looking at ACV trends overall and digging into industries and personas that maybe had a higher ACV should we focus specifically marketing to those groups with personalized messaging and go get more social proof from industries and personas that are maintaining that higher ACV.
So I think there's a lot of value in breaking that down in a little bit more detail as well. Yeah, when I see ACV going down, I ask Carl, is he sandbagging and giving deals away? That's why I go to your sales team asking what's going on? Yeah, typically yes, at the end of the quarter, definitely sandbagging into the next quarter if I've already hit my number.
So it's to be expected by Noun Cassidy. Exactly. And I think it's also, you know, it's indicative of maybe larger trends, people being more physically responsible and more hesitant, more timid. So that's where partnering with your customer success team might have a lot of value for you to all right, we'll bring them in at a lower price point.
But how can we over the next quarter or two quarters once they've validated our product and really trusted us as they're an opportunity to expand forward. And so start thinking of demand, Jen as sort of full funnel full company and not just, you know, you're not new business and you're going to be much more successful. If I could add one thing, Allison, on ACV trends too, you know, I never get freaked out by like lowering ACVs because again, when you think it went lower ACVs, because when you think about pipeline velocity, if I'm closing a lower ACV deal two weeks faster, and there's a land and expand opportunity, like like pipeline velocity is a really good metric to if you're a company that sells like a platform or something, like something multi-product. And a lot of like this happened to us at HubSpot, it was like a constant wrestling match at HubSpot of should we train the sales team to close as many hubs as possible on the first deal, or should we do a land and expand right get them into one hub and then expand them.
High buying velocity is a really important, you know, formula for understanding, you know, some of the math around that it might be great to close a big MR deal up front. It also might take me three x longer because I have to sell into three or four departments as opposed to just closing the marketing team, a marketing hub and worrying about the rest, you know, later on. So depending on again, what you sell and like if you're a platform or a point solution, really important to not be freaked out by one of the inputs, again, highlighting the importance of the entire pipeline velocity metric overall. Well said, I love that.
I think I also saw a question in here on if a company has a diverse product portfolio, should you create different pipeline velocity charts per product? I definitely would recommend that similar to kind of what we spoke about before, especially if there's significant costs to different differences between the products, it'll also help you sort of understand like where are you winning in terms of like a front door to potentially a larger ballistic use case of your company's products and services. So yeah, definitely recommend trying it a couple different ways. The nice thing is that like there's a lot of experimentation here, right?
Once you know the formula and the inputs, you can do this across different industries, geographies, products to really get a sort of full funnel view of what's going on in your pipeline. Allison, there was a question on, Nina asked a question on sales cycle length. So for more of an enterprise motion, we're maybe not closing a deal inside a quarter or inside a month. Do you stretch out maybe the time window in the calculation when you're closing deals in two, three, four quarters takes a little bit longer, six to 12 months sales cycle.
How do you think about the formula differently or how would you visualize it or model it differently? Yeah, so I think it's always good to do quarter over quarter, but then you could do kind of like your year end review as you're going into the next year's forecasting. I have, so on my end, I mostly asked for sort of quarterly reviews. And so that tends to be sort of my go to time period, but I would be interested how if anyone has had experience with utilizing more of like the the average sales cycle time period as a point of analysis.
Yeah, you could definitely do it by like six month averages, like each one H2 as well, if that better fits your company too. I think whatever is most resourceful for you to gather insights from is what you should choose. So this quarter of a quarter is six months over six months, year over year, whatever it may be, just make sure it's consistent across the board. You're still going to get the same insights.
You know, we're really looking at how we can compare different things because as you saw all these different inputs, you know, like Carl, talk about not sweating if the ACV is lower, like, you know, not just measuring out based on what creates the most opportunities, it truly is figuring out what is just bringing in the most revenue that fastest to in a sustainable way. So whatever way makes sense to your company, you could do six months over six months, year over year, just make sure it's consistent. So I have a kind of Ashley come on and ask her question, we find her again. I think Ashley sparked this didn't she on YouTube question?
We should think Ashley actually, we should send her like a hoodie or something. I did, I did spark this on a so my question is, our sales team, when they have an opportunity and it's qualified, they enter the annual contract value revenue in the opportunity on Salesforce. So I can pull a report by quarter for all qualified opportunities. I can include obviously closed one, closed lost, age, sales cycle, lead source, campaign source and self-reported attribution.
My question for you is how is this equation different than pulling that report and finding the average or the sum of pipeline coming through based on that report? Does that make sense? Yeah, I think so. Let me give an answer and continue to jump in.
And this is something I've been wanting to jump in and say, that is, pipeline velocity is a good metric when you want to compare something, but you don't really need it if you're not comparing anything. And so what I mean by that is in that report, you may be asking yourself or your executive team, maybe asking yourselves questions around what source works better, what product works better, what sales team is better than another sales team. And so the calculation helps you normalize that comparison across the dashboard you're already looking at with all your inputs. If you don't need that, if you don't need to compare something or there's not a question to be answered, in which case you're going to make a decision on moving forward based on that analysis, then I would argue you don't need to do the calculation.
So, you have a lot, like what I've heard from you is you have a lot of insights in this dashboard. The question would be then, where the place you'd want to use this is if you're trying to compare something within that data, and you need a common way or a normalized way to make that comparison, that would be the place where I'd use something like this. If you're not, then I think you have all the other metrics that you want within your sales force report of the dashboard that you're talking about, if that makes any sense. Yeah, it does because we're really just, I'm really just trying to show the revenue to the source.
So, how much revenue is coming from website intent, contact form, how much is coming from trade show, webinars, outbound and inbound referral. So, that helps. Thank you. Yeah.
And so, the question that you may get at some point after you get comfortable showing the source and the revenue is that, you know, a CEO may look at this and, you know, one way to look at this based on that input is I'm going to look at which source created the most revenue. Okay, like that's the source we should look at. Pipeline velocity lets you get to another level of depth to say, kind of think of it as a speed of which I'm generating revenue by source. So, it's kind of the aggregate amount and there's how fast it moves through the company.
And those are kind of two different questions. The velocity allows you to answer the second one. So, in that case, you would be looking at not just how much was closed, but how fast it closed. That's really the difference.
The calculation at the top is really how much and the number underneath is how fast. And so, it's, it's going from getting an aggregate number to a speed number that makes any sense. Yes, that does. Thank you so much.
All right, a lot of questions coming in here. Allison, do you want me to flip to the next one while I go through some of these questions? Through this, Allison? Sorry, I'm sorry.
Yes, let's go to the next slide. So, just some deep dive into sales cycle trends. So, like I said, I wanted to show some visualizations that help you pick apart these different inputs, sort of isolate different inputs on their own to take a deeper dive. As Carl mentioned, the sales cycle length has the greatest impact on the overall pipeline velocity calculation.
And so, I just put some ideas down here to help shorten your sales cycle length. Just these are best practices that we recommend even within our own clients. And so, as we know, even the difference between a day or two days can have sort of significant impact on that pipeline velocity. And if you find that you can shorten your sales cycle with different pipeline sources, I think to Cassidy's earlier point, that's why you're doing that comparison.
Where can you make those sort of incremental improvements easiest? Is it going to be on the outbound side? Is it going to be through your inbound hand raised and high intent hand raisers? I think that'll be something that you could really pay attention to is where can you make the most incremental improvements towards sales cycle trends, knowing that you're going to be able to improve that pipeline velocity better.
And then the very last one, of course, is just the win rate. So, I think this is probably small for most of you on the call, but I can't help myself. I absolutely love doing win loss analyses every quarter. This is really important just to be always be doing outside of even just looking at this from a pipeline velocity perspective.
But to kind of bring it home back to that first slide I showed with the different opportunity volume trends, this is where you can really dig into the differences between closed loss and closed one. So, you can start asking some questions and creating some strategies to potentially help you combat some of those closed loss reasons or capitalize on some of those closed one reasons. Excellent. So, I have a few questions.
Dominic has one, we're still early stage and do not close the client every week per month. Is it a good idea to take a step before one opportunity as a last step in order to have more data to work with? We're going to have our opinion on that. So, really, like is there a way to hack this?
So, there's just more data to do the analysis. Or maybe is it just too early to be doing this analysis? I think personally, I would stick with the closed one just because WinRay is really a critical component of this equation. So, if you're not quite there, yeah, I think you could really look at a lot of these inputs individually and begin to action on it similarly as if it was the sum of the parts of pipeline velocity.
But I don't know that I would hack the actual equation. I don't, again, it's not like a number that you're using to do demand planning or necessarily like, it is, I think it probably probably need to maintain that integrity from my perspective. But maybe a next step could be looking at those inputs and just making some changes from there. Yeah, I would add in, this should be a historical reference.
So, it shouldn't be in flight, like current state. So, this will be a historical view at what your pipeline velocity is. So, if you work, I've done this analysis for a really, like gosh, I think they're only a year or so in business, in a very low velocity of sales. I've done this equation for a client like that.
And you're still able to perform it because you should be doing it quarter or year. Your velocity just might be a very small number. That's okay. It means you're obviously early stage.
But just, just to remember, it's just more historical purposes only and to look at it in either a quarter or yearly. So, if you're not closing one every week or month, that's okay. Yeah, Dominic, this actually now is getting more time to think about this. I think what's important for you at this stage is trying to get consistent wins under your belt before you worry about an equation like this.
So, just try to get to, can we win a deal a month? I think we win two deals a month and then we get one three deals a month. And then from there, you'll start building the data and you'll start asking yourself these questions about like which pipeline source is better or which customer segment we want to target going forward once we've found some early product market fit. So, I don't know if I focus on this equation.
I just focus on getting deals across the line in a consistent manner early on. I'm going to start a question here on a previous slide. We talked about, we looked at these opportunities in a time frame. Are we looking at the, I just want to reiterate this.
Are we looking at the 92 opportunities in a given time frame? Are we looking at opportunities that are kind of a continuation like open ops from previous time frames? I think we've answered this. I just want to make sure we circle back on this.
Yes, we're using the created dates. So, they had to have been created within that time frame. There are two ways that you can then do the closed one and closed loss. We talked about kind of an unco-horted or quasi-co-horted view, maybe where you're looking at the closed date for your win and loss volume.
So, that would be quasi-co-horted or you could do the true cohort where everything is based off of create date in that type period. But that's sort of our, I guess, best practice. I know we're about on the time. I would recommend everybody go read Stacy's comment in the chat around how to kind of layer on acquisition costs in pipeline velocity.
I think this is a great example of like where would you want to go next? The obviously next question is, for example, this segment is working better than this other segment. But what was the cost of acquisition to like gain that pipeline velocity? And you want to make sure that's kind of you're taking that into account as well.
That's not in this equation, but it's something you can definitely layer on, and you'll need to layer on at some point if you're making some executive decisions around this data. Great point. All right, any other questions? Cassidy, Josh's comment or question is a really good one that I want to make sure that we cover, you know, we talk about create and capture a lot, and I want to make sure that we're very clear on this as well.
So Josh's comment or question is, does the pipeline velocity cause internal arguments with justifying dark social activities and adding pipeline, which are hard to measure with attribution? How do you capture quantify or quantity versus qualitative data in the pipeline velocity? So I think this is actually a really cool equation to help you with this argument. So especially if you break it down by points of conversion.
So dark social, you know, that will definitely play into your pipe, pipe, point of conversion. So you're declared intent to schedule demo, context sales, et cetera. And it will justify that using the measures of, you know, relaying on those last such attribution tactics, like lead ad forms and things like that are just not, it could be profitable for business, but more times than not, it's not profitable versus like this declared intent. And then the next question is cool.
How do we scale that pipe, point of conversion? And that's where dark social comes in is a way to build that pipe, um, point of conversion. So it's actually a really cool way to create an argument to support it. So just want to make sure we cover that.
Am I missing anything there, Cassidy? I was in Carl. Yeah, I'll just like even say it clearly again, because I go through this a lot with our clients. So as Ashley mentioned, it's important to look at conversion point, not source medium.
And then your conversion point is a more holistic view of a combination of channels. So when we look at a declared intent, pipe conversion, the conversion point is what's called the demo request form, but their attribution may have been captured from organic direct paid search, paid social, organic social. So we're kind of combining source mediums to report holistically on that primary conversion point versus only utilizing the self or only utilizing the channel specific source and medium to determine something as a success. So we're looking at all of the different channels that contribute to that primary conversion point and then going from there.
The only thing I'd add is I like to think of pipeline source or conversion point as intent. So we can always as marketers, we get wrapped up in, um, philosophies, but just take a step back. When somebody requests a demo, they have a specific type of intent. How is that flowing through to pipeline and close one?
And what's the speed of which it does that? When somebody fills out your ebook form, they have a different level of intent. If somebody goes to an event and meets Carl and shakes his hand, there's another form of intent. So you're kind of putting a story around these pipeline sources and what's the intent of the beginning of that relationship?
All those are fine. Just you want to have an unbiased view of the starting point and how it converts the revenue and the speed of which it does that so you can compare apples, apples and make the appropriate decisions on where you want to invest. All right. Thanks everybody for joining.
We'll obviously be flipping this into a podcast. Todd, keep me honest. Are we putting this on YouTube? Yep.
We're putting it on YouTube so you'll be able to see the slides, the video and everything else. All right. Thanks everybody for joining. If you have any other questions, want to follow up, connect with us on LinkedIn or shoot us an email.
Thanks everyone.