The following is a recap from our Office Hours on June 24th hosted by Tomasz Tunguz who reviewed Redpoint’s 2020 SaaS GTM Survey Results with Nick Giometti. You can also view the deck and Top 10 Learnings here. The conversation has been lightly edited for clarity.
Travis Bryant: Good morning, afternoon, and evening, wherever you might be. We’re coming to you from the Bay Area. It’s the next installment of Redpoint Office Hours, and today, we have Tomasz Tunguz, our Managing Director, as well as Nick Giometti, an associate from Benhamou Global Ventures. We’re going to be talking about SaaS metrics. We’re really excited to have you here. I’m Travis Bryant, I lead our Founder Experience team, which is the set of services and practices that we deploy to support an entrepreneur through their journey with us.
We’re excited to increase the cadence of office hours. This event’s coming on the heels of a successful event we had with Hollie Wegman a few weeks back and we’ve moved to this virtual setting and excited to use some new capabilities from a tool called Airmeet, and I would imagine most of you are new to the service, so I just wanted to share a few notes upfront on logistics.
All of you are muted and video off by default, so you can interact with us by chat. There’s a global chat feature. You also have the ability to chat individually with any other attendee. If you do have a specific question, please use the question feature, you see the icon on the right-hand side there. I’ll be monitoring those and sharing those questions with Tomasz and Nick, and we’ll work to tackle those during the discussion. We’ll do our best to get to the majority, and if you see a question that you like, you can also vote that up, so that helps us with the ones that are most interesting for the audience.
I’ll turn it over to the second most casually dressed person in the group of three here, to Tomasz, to introduce Nick and the session.
Tomasz Tunguz: Awesome. Everybody, welcome to SaaS Office Hours, we’re thrilled to be back. Thanks to Travis, and welcome, Nick.
This is a program where we typically invite SaaS luminaries from all over the world to share insights. We’ve done them for over three years now, and we started them in our Redpoint San Francisco office and have now migrated to this virtual platform with Airmeet.
Today, we’re going to be reviewing the result of the Redpoint 2020 Go To Market Survey. We sent out this survey in February, and it’s taken a bit longer for us to produce the results, but a bunch of big important things have happened, so we’re happy to provide them to you today.
We’ve gathered questions from everybody and then people are able to submit questions and actually vote up which ones they’re most interested in, and then we can tackle them in that order.
Nick, thank you so much for helping me out, helping Redpoint out with the incredible data analysis. Nick is currently an MBA student at Northwestern, he’s an intern at Benhamou Global Ventures, and I’ve had the privilege of working with Nick for the last three or four months on this project. He’s had an incredible career at BlackRock in global fixed income, where he managed a huge credit book, and he dusted off his Python and pandas skills to help me with this analysis, and I learned a lot from him.
We have something very important in common, we both went to Dartmouth undergrad, and we both rowed, so there’s definitely kinship of oarsmen there. So thank you so much, Nick, really appreciate all your help.
Nick Giometti: Of course, no, thank you very much. I’ll go ahead and share my screen here.
Tomasz Tunguz: Fantastic. Okay, so we’ve got about 60 slides to run through for everybody; we’re going to hit on the high points and then fall back on questions for any sort of clarification.
For the respondent demographics, we had about 507 people respond to this survey, and what you can see, and what you’re going to see across all of these different charts, is they’re actually pretty broadly distributed.
This is the percentage of respondents by average contract value, and you can see about 15% are 150k and above, but everybody else is effectively in the mix from between 0k to 150k.
In terms of ARR, most of the respondents actually skew sub 20, so we’ve got something like 80% of respondents were sub 20 million in ARR, although we did have a handful of companies north of 20 million.
In terms of capital raised, you’ve got this funny distribution here. You can see that about 25% of the companies that responded are bootstrapped. Then you’ve got kind of a chunk between one to 30 and then, sort of a dip in the 30 to 50 and then, a 50 plus. You shouldn’t read anything into the dip in 30 to 50. This is actually sort of a mistake on my part in bucketing because if you’re a fast-growing company, you’re going to raise about three million dollars initially. In the seed, you might raise 10 to 12, and then, by the next round, you’re going to raise something like 25 to 30, which would automatically put you into the 50 million dollar bucket. It’s a little bit hard to actually end up in that 30 to 50, and so next year, we’ll actually change that bucket.
The last point on the demographics is that the respondent base actually spans many primary buyers. These were all the different buyers. We asked respondents to say whether or not they were the primary target in their sales conversations, and you can see operations, marketing, and IT are the three largest operations. Operations is a little bit of a catch-all because you can have both IT operations and then logistics operations or different kinds of operations, but the net point here is that we’ve really got a nice representative sample, we feel, of respondents.
This is a meaningful change from the survey we ran last year. Most teams have actually moved to annual billing. This chart actually shows you, by ACV, the fraction of respondents that are annual, usage-based, multi-year, and month-to-month billing. As you would expect, in the zero to 5k ACV, month-to-month billing is, by far, the most common. You just want to get people signed up on a credit card, and lots of bottoms-up businesses look that way, but the second a company moves into 5k and above in ACV, and they’re starting to employ inside salespeople or account executives, they’re immediately moving to annual.
One of the main reasons for that is just, when you get an annual prepay, the customer’s effectively lending you money at the 0% interest rate. One of the main objectives of this survey was to figure out, how do startup teams, and the Go-to-Market teams in particular, evolve over the life of a company?
We were able to aggregate those statistics for you. If we look at employee headcount as companies scale, across the top on this table, you’ve got the ARR buckets, which will be common throughout the presentation, and then, on the first column, you’ve actually got the functional roles. In this case, engineer means number of people writing code. Salespeople means number of people carrying quotas, and marketing is the entire marketing team. What you find is, in the beginning, engineers outnumber everybody at least two to one, if not four to one, and then, as the company scales, the ratio between sales and engineering sort of asymptotes to one to one. That makes sense because, in the beginning, you’re building lots of product and primarily establishing a product, and then, as the company scales, you’re really kind of commercializing it.
One of the things that was surprising to us was marketing scales sublinearly to the rest of the organization, and we’ve got more data there, but it was a bit of a surprise.
The other thing we asked in the survey was “What is the title of the most senior leader within each of these teams at different stages?” You can see in the zero to one, most companies are employing leads. In the one to five million, VP of Sales is the first VP to emerge. In the five to 20 million in ARR, on average, all of these functions are headed by VPs, and then, it’s really only in the sales org that you see an evolution into hiring a C-level executive at the 20 million in ARR and above.
To give you a little bit more detail, we’ll be publishing these slides on the web, but, Nick, if you go to the next slide, we’ve actually broken it out. Here’s the distribution by title across ARR, so there are a handful of companies actually employing CROs at the zero to one million, which kind of seems a bit anomalous, but you definitely see, if you follow that bar across the charts, that as a company gets into the 20 to 50 and then, 100 million dollar plus, you really see a meaningful shift. You can see that the 50 to 100 million in ARR, we don’t have that many data points, which is why that’s sparse.
On the next slide, we’ve got the most common leader titles for marketing leaders, and so, you can see, basically, the same evolution. It’s only really at the point where you get to about 100 million in ARR, that most companies are employing CMOs, and if you go back and look at the transcript of the conversation that we had with Hollie Wegman, who was the VP Marketing at Segment and was at MuleSoft and also Salesforce, she really talks about how a CMO is an organization builder. It’s not really somebody that you hire to solve a functional problem, like solving demand gen or a product marketing issue, it’s really just sort of a team and a company builder, and so, that’s why you see companies sort of waiting at least until the 20 million in ARR, until you see a meaningful share of hiring a CMO.
Another reason to do this is that, as we saw, the marketing teams don’t scale linearly with revenue, and so, though the marketing team tends not to need a C-level leader until it’s actually quite large.
This is the same slide for Head of Customer Success. You can see that customer success, relative to the others, sort of trails. Most companies only hire a VP in the five to 20 million, and it’s very uncommon, it’s actually the least common across the group, to have a C-level, like a Chief Customer Officer, which was a surprise.
It was a surprise to me because if you think about a company, let’s say like 75 or 50 million in ARR, the new bookings of that business, let’s say a company’s growing about 70% at 100 million in ARR, that means they’re going to book 70 million dollars in incremental revenue this year, on a base of 100 million in ARR. Managing the existing 100 million is, you could argue, equally important, because that’s 100 million dollars of existing business that the company has, relative to the 70 million in bookings. However, one of the main reasons that you don’t see a C-level Customer officer is, a lot of the times, organizations actually fold this responsibility into the CRO, and the CRO starts to manage both sort of the pre-sales process, in terms of demand generation and development reps, the account executives, and then customer success, and that’s been a successful model.
This is a box and whiskers plot. There’re a handful of these plots within the presentation. The way to read this is, on the y-axis, this is the engineer to AE ratio as a company scales in ARR. The white line within the box is the median value for that population. The top line in the box is the 75th percentile, and the bottom line is the 25th percentile, and then the whiskers, where the little lines going top to bottom, span the outliers. This basically means most companies in the zero to one, to one to five million in ARR typically have two engineers per salesperson. You can see that sort of asymptote to one to one as a company scales.
We also looked at the ratio of inside account executives to SDRs and outside executives to BDRs, and what we found is that most teams actually operate with a one to one or two to one inside account executive to SDR ratio. You do have a bunch of companies, and we actually cut off this chart because there were some at like 20, that operate with some pretty meaningful leverage across those teams, but it’s, by far, the most common to have one to one mapping between an inside account executive and an SDR.
If we go to the outside and the BDR, you’ll see that it’s basically the same thing, except it’s more concentrated on the one to one, and this makes sense. If you’re an inside sales account executive, you can process many, many more leads, and the values of those leads are less, and so, you’re going to have to go through more leads in order to help your account executive attain quota, compared to a BDR, who’s supporting an outside AE with a much larger ACV.
Let’s introduce this concept of span of control. Span of control was a concept that was created in the early 1900s, and the span of control is, how many reports does a typical manager have? There’s been a rule of thumb in most industries that a span of control of seven is optimal, and that’s because a manager needs to have one-on-ones with each of the reports, needs to have enough time to be able to help them and also do their day job. We looked at the span of control over inside and outside execs and how many account executives does a typical sales manager manage, and the average is about 4.7, and you can see the distribution here.
If we go to the next slide, we actually have better performance. Answering the question, does a smaller span of control actually increase attainment? The answer is yes. Let’s break apart this complicated chart. If we take the top left chart, these are for respondents whose sales teams attained less than 50% of quota. The left bar are people who had a span of control of seven or less. The right bar are people who had a span of control of seven to 14. 15% of companies who have spans of controls seven or less attain less than 50% of quota. Twice as many respondents who have a quota from seven to 14, achieve less than 50% of quota. In other words, if you have a span of control of seven or more, your team is twice as likely to underperform as a team with a smaller span of control.
Conversely, if we look at the bar right underneath it, which is 80 to 95% of quota, you can see that a team with a small span of control, less than seven, about 27% of them actually achieve 80 to 95% quota attainment. While less than half of those achieve that same level of quota if they have a larger span of control. This shows some level of correlation between smaller teams and higher performance. I actually blogged about this recently and had a handful of different, very large companies email me saying they’ve actually observed this data in practice, and so, at least there’s some anecdotal substantiation.
Then we went into sales quota benchmarking, so how should AEs be performing, what’s the ramp time, what should their quotas be, those kinds of things. For inside sales quota, these are the percentiles and the quota numbers, so you can see the 50th percentile is about 500k a year and 75th percentile is 720k.
We found that there’s this funny step up. Across the bottom, you have an average contract value. On the y-axis, you have ARR quota. You can see, if you span between zero to 15k in ACV, your median, even your 75th percentile, is going to be about half a million. The second you get to about 15 to 50k ACV, there’s a step up that’s actually statistically significant and different to a 670k quota or 750k on the 75th percentile. This is interesting because what you’ll see in the future chart for BDRs or for outside sales executives is that it’s way more linear, but in the inside world, there’s a discontinuity there, which is unusual to find.
If we look at outside sales quota, these are meaningfully higher, and it makes sense. The 50th percentile is about a million and the 75th percentile is 1.625 million. As I said before, this is the same distribution of outside sales quota growing with ACV. You can see that there’s basically a linear relationship between ARR quota and the ACV, which does suggest something interesting — that the number of leads an outside AE can close within a given year is relatively fixed. Therefore, if you’re targeting the enterprise, having greater ACVs is actually a meaningful benefit because for effectively the same spend on an outside salesperson, you can actually generate more ARR, all things equal.
This was a surprising chart. We asked everybody, “How long do you allow your AEs to ramp?” The 50th and the 75th percentile for inside was only three months, and the 50th and the 75th on outside was five and three months. This is peculiar because if I join a company as an inside rep, and I have 90 days to ramp, and a sales cycle is 75 days, then I’m not going to get that many at-bats before going through an entire sales cycle before I’m fully ramped and to show that I can actually achieve quota. If we look at an outside ramp of three months, the sales cycle for the 150k ACV is probably going to be nine to 12 months. There’s just no way an outside rep can actually get through any number, I mean, literally zero sales cycles within that certain time period. It’s typical to see a six month ramp, but even then, you really need to take a look at how long your sales cycle is going to be in order to determine whether or not the ramp is fair.
The other mitigating thing that you could do for an incoming field rep or an outside rep is actually have warm leads for them to manage while they’re ramping, and in that case, you might be able to justify a shorter ramp, but otherwise, you’re sort of kind of setting up some of these reps for failure.
If you look across the entire respondent base, we found that 50% of teams achieve better than 80% of quota. It’s either super impressive or it’s really optimistic, and you’re going to see that in the data. Even across our own portfolio, I’m not sure we have 50% of teams achieving better than 80% of quota. This is on a dollar basis. Maybe we do, it just was very surprising. Nick and I were talking about it before, and thought that there might be a selection bias, and the people responding to the survey are the businesses that are doing better, but there’s definitely a positive sort of skew to the data.
What we do find is, as companies scale, attainment improves. You can see here, on the y-axis, you have the fraction of respondents within a certain ARR bucket, who achieved the certain amount of quota, and it makes sense that as companies scale, attainment improves, because only if your attainment is good are you going to be in the 100 million dollar bucket. This is a classic example of survivorship bias.
It does stand to reason that if you can continue to attain or beat your quotas, you are going to grow really fast. What we do find is, if you really want to be top decile, consistently hitting 95% of quota is where you’re going to be. Most teams are fairly evenly distributed across the other buckets of quota attainment.
We did some statistical analysis to see if there were any sort of impacts on attainment with ACV, ARR, ramp time, and actually, the one that’s missing is buyer. For instance if you’re selling to lawyers, should you have different quotas or different attainments, and we couldn’t find any meaningful relationship there, so the data does hold broadly across most of the SaaS ecosystem.
Moving on from AE to SDR and BDR benchmarks. We’re going to run through them, just a quick terminology clarification. SDR, sales development representative — those are the people who are generating pipeline for inside teams, and BDR are the ones handling it for outside AEs. One of the canonical debates in managing an SDR or BDR team is, “How should I goal that team?” And there are two predominant metrics.
One is meeting count. Nick’s got to generate 20 meetings per month in order to hit his quota. The other one is pipeline value. Nick’s got to generate 30k in ARR this month, and there’s sort of a philosophical difference. The downside to meeting count is, if I were the SDR, I could fill Nick’s calendar with a whole bunch of meetings that are super small and don’t fit within our ACV band. Nick is then spinning his wheels in conversations that aren’t going to lead to anything, and he’s going to get super frustrated. The downside to pipeline value is, often, it can be difficult for an SDR to establish the value of a contract in the first conversation, and so, they’re constantly guessing or estimating what the pipeline value is. If you’re looking to hit a particular number, it’s easier to inflate a number in order to hit a goal, if there’s no downside.
What we do find is that it’s a 60–40 relationship, so about 60% of teams both inside and outside, across SDR and BDR, are using meeting count, and about 40% are using pipeline value. We find, on the next slide, that there’s actually no meaningful difference in goal attainment.
If you look at the distribution of SDR and BDR quota attainment, on the left hand side, that distribution is there for meeting count. On the right hand side, that distribution is there for pipeline value. They’re basically the same. What it means is, you can have the conversation about which goal you want to have, but it doesn’t matter. Just pick one and go.
One of the analyses we wanted to do was to understand whether or not better SDR quota attainment leads to improved AE attainment. In other words, if you manage your SDR team really effectively, do AEs actually benefit? The answer is yes. When SDRs hit their quota, and in this case, 95% plus of quota, about 50% of AEs actually achieve theirs. If you look at the bottom middle box, when SDRs achieve 95% of quota, you can see the tallest column there is 95% plus. That means that 50% of AEs attain their quota when SDRs hit their quota, so yes, SDRs and BDRs work. That’s the net of that slide.
We also did a bunch of analysis in terms of marketing, just to help everybody understand how marketing teams are built. The first data point that we collected was soft marketing costs. So, soft marketing costs are all the dollars that you spend, as a marketing team, not on salary — events, webinars, ad spend, all those sort of campaign dollars that you’re putting together. This is a question we often get as board members, but we’ve never had an answer before. Here, we have a million to a five million dollar ARR company spending about $300,000 a year on marketing spend. As the company scales, you can see a 20 to a 50 million dollar company, the median is spending something like $2.5m. Even if you get to a 100 million dollar company, the median company’s spending about 10 million dollars a year on marketing program spend. Therefore, you can come up with the rule that most companies actually spend about five to ten percent of their ARR on soft program costs.
In terms of marketing structure, marketing teams actually tend to be smaller, with tighter spans of control. Even if you look across the entire base, you’re really looking at an average around three, which is about 30 to 40 percent smaller than a sales team. It makes sense if you think about the first table where we showed that marketing teams scale sublinearly to total headcount, that you would expect the spans of control to be smaller.
These are the marketing headcounts by different functions. We asked, for all these different companies, “How many people are in analyst relations, how many are in comms, public relations, community relations, content marketing, demand gen, and product marketing?” What you find is that most companies basically flatline every discipline except for demand gen and maybe content marketing to some extent — but demand generation is the only one that’s really scaling as the revenue increases, which is interesting.
Then we asked marketers, “What are the most important marketing channels that you have?” We also asked, “Is it content marketing, events, outbound prospecting, press, referrals, SEM and SEO, and webinars?” The top three — content marketing, events, and outbound prospecting, were the ones that Heads of Marketing said were very important. You can see sort of the buckets on the bottom — lowest, low, moderate, somewhat, and very.
Referrals are also super important. These are the channels that marketing relies on. What’s interesting is you can see that events are really important, but webinars, only about 10 percent of marketers actually think they’re very important. I think if we were to run this survey again, that there might be a switch there, just because of the move to virtual events.
Going back to this point about optimism and reporting metrics — we were surprised to find that most companies reported less than a 13 month payback. In previous analyses, and looking at the Pac Crest Survey, and then even looking at publicly traded companies, the median of all the other surveys, that we’ve seen, is around 14 months for privately held companies, and for publicly traded companies, it’s about 18 to 20 months.
As you get bigger and bigger, the paybacks increase. This was super impressive. We asked for the median payback that was gross margin burden, so maybe there’s some error in the calculation there, but we were pretty impressed. There are clearly a lot of super capital-efficient companies out there.
What we did find with ACV is that the bigger the ACV, the longer the payback. This is a distribution of payback period by ACV. If you look at the bottom middle chart, for 150k ACV plus, you can see there’s still some 10 to 13, about 25% of respondents have attained a 13 month payback. You also have another 40% that are roughly 14 and higher, and that makes sense because the larger the contract, the more investment the customer is willing to make. Typically, you might see multi-year deals — so, you’re willing to sort of endure a higher cost of customer acquisition because the lifetime value is considerably longer.
We looked across payback periods across different teams or across different buyers, and what we found is that IT, finance, and operations tend to have the longer payback periods, although you can’t really draw statistically significant conclusions. You can see the whiskers on these charts are actually pretty broad, but if you were to guess from the data, you can see that those are the three buyers that are the slowest. Sales, unsurprisingly, has the fastest time to payback. That’s where a sales pitch for improving ROI on sales is immediately valuable. You either increased bookings or you didn’t, and so, you should be able to make that calculation work.
The last part of the presentation is focusing on customer success benchmarks. We looked at the span of control of CSMs, and we found it’s basically around three. You can see, as a company scales to get more and more CSMs, which makes sense.
One of the things that we really wanted to understand was the splitting of CSMs. What you find a lot in customer success organizations is that they end up tiering customers, and they might focus on a named account list or an ABM list, or they might just tier customers by ACV. We wanted to understand what fraction of CSMs, within the entire respondent base, were working on named accounts, so we broke it out.
You can see fewer than about 10% of CSMs are working on named accounts with really small ACVs. That’s probably because some tiny subset of that customer population actually has a pretty large ACV, but as you scale to 150k plus, you’re actually seeing upwards of 75% of CSMs working on large accounts.
Anecdotally, what we find is that, sometimes, customers are actually willing to pay for dedicated account managers, particularly with those larger ACVs. We were curious, if you’re a CS leader, what is the most important metric that you used to evaluate your team and we asked across net promoter score, net dollar expansion, logo retention, and customer sat.
Net promoter score was actually the least. Fewer than 10% of people actually said that NPS was the most important metric. Net dollar expansion’s actually, by far, the most important, and there’s a really good reason for that, which is, if you’ve got fantastic net dollar retention, then your business is basically like a bank account, and if it’s got NDR of something like 120% every year, the existing customer base grows by 20% without you having to do much, and so, it’s no surprise that NDR is the number one metric.
On the next slide, you can see why NPS is not an important metric. That is because 40% of respondents have an NPS greater than 50, which is a staggering result. If we break it out by buyer, you can see that, surprisingly, engineers are the most effusive.
This is a distribution of NPS by buyer, and if you go to the third one on the top, you can see that engineers either absolutely love a product, with 60% of the respondents who sell to engineering had an NPS of 50 or above, or they absolutely hate the product, and there’s nobody in the middle. I’m an engineer and if you’re an engineer, you can clearly understand that perspective.
I want to open it up to questions. There’s one that comes through Travis.
Are you able to discuss how renewals are managed, including criterias that define when renewal responsibility shifts from being under sales to under customer success? Rephrasing this question, an AE closes a deal and then is managing the relationship, and then, at some point, a customer success manager has to be introduced in order to manage that account. There are all different kinds of models, but the most common, and the main point of friction is, at what point does an AE get credit for the expansion of the account?
In other words, if I land with a 5k account and then the account grows within the first 90 days, doubling in size, does the AE actually get commission on that expansion, or does the CSM actually get credit for it? There’s sort of a huge range of different answers here. The best book on the topic is The Sales Acceleration Formula by Mark Roberge. He actually walks through four different AE quota structures and the reasons that he actually evolves them. In part because of churn, because it helps high churn, but another part is around this expansion of the management of customer success.
Just to summarize, in small ACV deals without a whole lot of expansion, what you typically see is the AE closes and passes it immediately to the CS rep because there’s not a whole lot of expansion. In mid-market, the AEs and the CSMs tend to work together for a bit, and then at some point, the AE sort of loses interest. In the really large accounts, you find that the AE is like, “If I’m selling to Apple, I’m not going to let anybody else touch that account within the company. All the communication has to go through me because I own the account,” and that account can be a 25 or a 100 million deal account. So, you end up with these sort of pods that are focused around particular teams and then there’s one owner. Again, if I’m a named account rep and I’m focusing on Apple, if I sell to the iPhone division, and they sell to the Mac division, that expansion’s going to be a meaningful upsell. That’s going to count as new bookings for me, rather than expansion dollars. That’s how I’m going to be compensated.
Okay. Let’s see. Next question.
What was the total number of respondents? 507
You mentioned a narrow range of the number of deals an AE can close per year. It’s going to depend on the size, and there’s a blog post that I will send out to everybody on this, but typically, what you find is, an inside rep can close something like two to three deals a month, and an outside rep, depending on the ACVs, is going to close something like two to six deals a year.
Did you look at differences by length of sales cycle? Very good question. We did not. This was a question that, in retrospect, I wish we would have asked. It would have clarified a lot, but we did not have the foresight to ask that question.
Did you see significant differences in span of control by ACV? No, we did not. We looked at spans of control by ACV and there was no meaningful change in relationship. That’s why we published the one on ARR, because that was the only one that actually had any real delta.
Any data on how a marketing agency scales with ARR? Smaller companies may use more outsourced labor.
Nick, do we have any data?
Nick Giometti: No, I don’t think we captured that in our survey.
Tomasz Tunguz: Yeah. Sorry, we don’t have an answer for you there.
What is the role of an XDR in the product-led growth world? Very good question. Product-led growth are companies that are using products in order to qualify customers and then growing them that way. The most common sort of current example’s probably Notion. People think of Notion and Slack, although Slack’s probably a different story. The role of an XDR is lessened, and basically, what ends up happening in the product-led growth world is that it’s actually the CSM who does this. For example, Nick’s going to sign up for Notion, he’s going to come up to speed, he’s going to have a bunch of questions understanding how to use the Redpoint Notion account, and so, it is the CSM that is going to educate, build trust, and at some point, he’s going to hit either some wall within the product or there’re going to be enough people that our IT administrator decides that we need an account because he wants corporate controls.
The net of it is, the XDR, at least in the early days, and particularly when you’re focused on smaller ACVs, is minimized. What ends up happening as a company scales — and you can read about the flywheel model, which is what Kenny Van Zant pioneered both at SolarWinds and Asana — is at some point, the number of product qualified leads coming in are going to be so large, that you’re going to want sales teams to go after them. Let’s say, a mid-market company like Looker is using lots of different seats with Notion. There’s going to be some flag that says, “Looker’s got 50 seats with Notion, somebody’s got to call them,” and then, an SDR or an AE could call in, qualify, and then pass it up. That would be the path.
Are you surprised that quota attainment numbers don’t reflect more companies with higher attainment numbers? I thought I was reading the numbers incorrectly. It seems like a lot of the companies have low attainment numbers.
Nick Giometti: 80% higher is not bad, I mean, but if 50% or greater, then 50% of the respondents had 80 plus, that’s, pretty solid.
Tomasz Tunguz: Yeah, really solid. Yeah, I was surprised on the high side, to be honest. There’s a lot of volatility in quota attainment, and so, I was expecting sort of a bell curve around the 60% attainment because one of the failures of most early-stage companies is they tend to over hire AEs before they’ve got the pipe to feed them, and so, that kind of brings things down.
We should make sort of a broader point that it is really important to sort of establish a culture of AEs beating quota consistently. First, it establishes a cadence and a culture of winning and momentum. Everybody thinks that they’re winning and knows that they’re winning. The second is that it makes it much easier to hire salespeople.
You’d rather have a smaller number of salespeople who are all saying, “Hey, I’m making money hand over fist at Nick Giometti’s Startup,” and then, it’s going to be easier to hire, rather than having twice as many AEs or 50% more AEs who aren’t attaining quota.
One of your blog posts talks about PMM being the first marketing hire. Does that conflict with the data you see? Probably. Most people would prefer to hire a demand generation person as their first marketing hire because you start in founder-led sales, and then you have way too much demand to handle, so then you hire an AE, and then you’re like, “Okay, I got to feed this AE.”
The problem, and we talked about this with Hollie, is that, what you really want to do is get the narrative arc of the story and the positioning right at the beginning, and that’s why a PMM really ought to be the first person that you hire.
What the best product marketing managers do is architect the customer life cycle journey. When does somebody hear about my startup for the first time? What is the message that I’m delivering them? How do I continue to educate? At what point do they buy, and then how do I get them to continue to grow? They map out different product and marketing materials for that entire journey which is super important, and if you hire a demand generation person, they’re not going to be focused on that at all. They’re going to be focused on optimizing the very bottom of the funnel, which is a great initial injection of rocket fuel, but it’s not enough to sustain longer term growth.
Nick Giometti: I want to say that, compared to some of the other marketing functions — like community relations, comms, PR, analyst relations — everyone did seem to have at least, on average, one PMM, at the initial ARR stages.
Tomasz Tunguz: Yeah, great point.
Any data on XDR deals getting over the finish line on their own? Sorry, we didn’t ask that question.
Differences in spans of control for inside and outside reps?
Nick Giometti: No, not statistically significant, in terms of the difference in outside versus inside in span of control. It looked pretty similar.
Tomasz Tunguz: Let’s see what else.
How is the payback formula calculated for this survey? That is gross profit in period two minus gross profit in period one, divided by the cost of customer acquisition.
Nick Giometti: We didn’t do anything on that but maybe next time.
Tomasz Tunguz: For CSMs, any data on ARR by CSM and accounts managed by a CS rep? We have published that data in the past. We actually ran a survey last year. We will send it out, but we didn’t calculate it in this one, although, conceivably, we could. Right, Nick?
Nick Giometti: Yeah, we could.
Tomasz Tunguz: Cool. Awesome. Well, we’re coming right up on time. Nick, I am so grateful for all your help. You did an incredible amount of work. I’ve got to tell everybody that the data was very messy, and Nick did a fantastic job cleaning it up and getting us into a place where we could report on it.
Nick Giometti: No problem.
Tomasz Tunguz: Yeah, we’re super grateful. Thank you, Nick. We will send out the survey results in a publicly available form. We will also be sending out a feedback survey so please let us know your thoughts on this particular session.
Travis Bryant: Look at this stream of thumbs-ups and blue hearts. You’ve got something to write about in your diary today.
Thanks everyone for joining! We look forward to seeing you in a few weeks. We’ll have another office hours in July that we’re excited to announce shortly. Be well!