Executive In Residence Balderton Capital
Well, our next speaker doesn't really need a lot of introduction, but he's got a new title this year, and that is Dave Kellogg, one of the SaaS Metrics Brothers. So with that, Dave, I'm just going to hand it off to you. Thanks, Ray. Thanks, Growth, as we call you on the podcast. For those who don't know us, my name is Dave Kellogg. I'm an EIR at Baldur's Sheep Capital, an independent consultant and author of a blog called Kellblog. And today, I'd like to talk to you about how to present SaaS Metrics like a pro by avoiding these 10 all too common mistakes. So a little bit of background on me. In the past, I've been basically CMO of three companies, the CEO of two, the GM of one, over 25 years of operating experience, largely in the zero to 100 billion range, but I've also got all the way up to a billion and actually beyond when I was at Salesforce. So today, I'm focused on working with Balderton Capital. I've been a director of nine companies in total, advisor to over 20, investor in a bunch as well. So pretty active and have a pretty broad view from the operator, the go-to-market, the CEO, the board level. So that's the perspective I come from. Hobbies, this is tongue-in-cheek, but one of my hobbies is S1 diving. So when I read the Klaviyo S1, they had this kind of very perverse definition of NRR, and I spent some time on the weekend building this spreadsheet to see if I could replicate what they were doing. So anyway, that's one of my hobbies. That's why we run the podcast with Ray, the SaaS Talk with the Metrics Brothers, and that's why I want to talk to you today about the way, how to present SaaS metrics like a pro. This is kind of the complimentary presentation to one I did a month or so back at SaaStr, which was the strategic use and abuse of SaaS metrics. So I view these two presentations as kind of a matched pair. The SaaSter one is more strategic, things that go wrong at a strategic level. This one's more tactical. There is some overlap, but if you want to see the slides, there's a link in the footer. There's also a link within that link to a video. But today, as mentioned, we're going tactical. We're going to try and leave strategy to the other presentation and talk about tactics. And before you switch off the live stream, wait a minute, going tactical, does that mean we're going to do nothing but nits and formatting? Is this content really unimportant relative to the other one? Are we going to be in the weeds literally as they were in the picture? The answer is yes, but only a little bit. We're not going to go that deep in the weeds. And second, remember, tactical mistakes can become strategic. If you've never seen this slide slash analysis, this is actually an analysis that Edwin Tufte did of a slide that we used at a presentation before the Columbia re-entered and subsequently, as we know, burned up. And he shows about 15 things that are wrong with the slide, and they're all tactical. You're mixing units, you're doing multiple deep levels of indentation, you're using imprecise characterizations like significantly, right? And those are all tactical mistakes, all tactical mistakes, but you kind of add them up and you get one heck of a big strategic problem. And that's the way I feel about this stuff, and this is why I dive into the detail on SaaS metrics, because if the foundations aren't good, then the whole thing can collapse on top. So one last story, I'll tell you a tale of two ill-fated journeys. This is actually an FAA briefing called Fly the Aircraft First, about which I did a blog post. It's the story of an actual doomed flight in 1972, where the entire crew at that time, which was three people, because you had a person who managed the fuel on board as well, two pilots plus the fuel person, they were so single-mindedly focused on the malfunction of a landing gear position indicator that nobody looked at the altitude. And literally the plane descended in and crashed in the Everglades, because they were so focused on a non-critical task that they failed to detect and arrest the descent. Nobody ever said, hey, what about our altitude? And I feel like we can do this in SaaS. I worked one time a ways back with a SaaS startup that was a hot company in a greenfield market. The whole team was naturally focused on growth, right? It's greenfield, there's competitors, we need to grow. That naturally led to a strong focus on CAC. By the way, most of the leadership team came from a sales and marketing background, which they were also naturally focused on CAC. And they spent a lot of time in metrics, analyzing questions like what channels are the best marketing or what segments are the best marketing conversion rates? What segments of the business have the highest average sales price, the shortest average sales cycle, the highest win rate, the highest competitor takeout rate? They're looking at all these go-to-market metrics, and nobody ever said, hey, what about churn? Right? So this is another way that we can go wrong with SaaS metrics, kind of selective attention on what's interesting as opposed to what's important. So as a reminder, in the kind of ops and finance community, which this presentation is really aimed towards, also the operations in general, as well as the RevOps role, I put it in simple terms that our job is to light shit up, that that's what we do here. Our job is to say, hey, what about the altitude? Hey, what about churn? And we need to do that in kind of a dispassionate, factual, trusted, and complete way. Right? We can't get so focused on the indicators du jour that we forget to look at the other ones. So I think we actually perform a very important function in helping run the company or fly the aircraft. So all that said, as a preamble, if we want to help the business present metrics either to each other, right, like a quarterly business review meeting, or to the board at a board meeting, or to investors when you're out fundraising, we should avoid these top 10 mistakes that I see people make when working with and presenting and analyzing SaaS metrics. And in this presentation, we're going to go through them one by one. And we're going to have a couple of slides on each. So the first mistake is amateur presentation. So it looks like it was made by Mrs. Johnson's kindergarten class. The second is cherry picking, where they only pull the metrics that look good, and therefore give an incomplete and often varying picture, because what looks good varies from one quarter to the next. The next, number three, is miss benchmarking, where we use SaaS benchmarks, which is a good thing, but we benchmark against the wrong ones, and therefore draw the wrong conclusions. I made this mistake myself. I've made most of these mistakes, but I particularly had experience with that one. Number four, omitting context, probably the most common mistake in my mind next to amateur presentation and cherry picking. So the third most common, but just leaving out context. So just talking about this quarter, and forgetting to talk about last quarter and the quarter before that or plan or the most recent forecast, or surrounding metrics, right? And it's very important to have context when discussing these metrics, if only because your average investor looks at hundreds of companies per year, and your average board member probably sits on 10 boards. So they're not going to remember your context. I doubt that you could remember it yourself. Number five, piecemealing. So kind of instead of looking at SaaS metrics in little groups, like I like to do, they'll look at the piecemeal, which again, can lead to bad analysis and incorrect conclusion. Piecemealing is where you just drop a bunch of SaaS metrics on top of someone and kind of forget to layer them and forget to structure them in a kind of top down way, such that you can enter. I call this sometimes the wall of numbers, and I did a blog post on that. Number seven, being a smooth operator, too much use of smoothing. There's a time and a place for smoothing in SaaS metrics. But to the extent smoothing is used to obscure rather than highlight, it's a problem. Number eight, forgetting the question. Sometimes we could get like so wrapped around the axle on the best formula or the right formula or how Bessemer does it, or how Icahn or Meritech does it, that we actually forget the question we're trying to answer. And we can often speed up the whole conversation if we just go back and say, wait a minute, how are we interpreting this metric again? Number nine is missing the investor point of view. SaaS metrics get used, as I mentioned, in three places, quarterly business reviews, board meetings, and fundraising presentations. And I think some people make the mistake of only talking about SaaS metrics when they're fundraising, which is a huge problem in my mind because they're not using them to drive the business day to day. But the counter mistake is to get so focused on operations that we forget investors have their own and fairly unique view on SaaS metrics. Number 10 is kind of tongue-in-cheek, but I've seen it happen, I call it retinal burn, where we fail to disclose a shocking number and disclaim a shocking number before showing it. And it kind of burns in everybody's brains and creates this lingering perception, almost like post-traumatic metric shock syndrome or something. So we have to be very careful about what we show people and how we show it. So we're going to go through these one by one. The first one is the amateur presentation of SaaS metrics. Here's an example of a fairly innocuous amateur-esque presentation. It's not that amateur. It's got no typos. It's not showing 18 digits of precision. But at the same time, it's not very good. As a metrics person, you look at this, the first thing I say is more than half the screen is wasted with the graphic, that doesn't mean anything. Metrics are in text. If you see metrics in a word table or in text, it always terrifies me, because it means you're not using calculations and cross-checking. So here, they're just in flat text. The format is regular. This is a little more subtle, but expansion ARR, we're comparing to plan. New ARR, we're comparing to plan. Churn rate, we're comparing to forecast. Then we have arbitrary characterizations, like one of my personal favorites is always non-regretted attrition, meaning people quit, but we didn't care, to which my answer is always were they on a performance plan, and if they weren't, that is regretted. But in any case, is there a common and consistent understanding of what non-regretted means? Usually it's arbitrary and inconsistent. I would dare say you can't really see it on the slide, that there's a lack of agreement on what rows should be there. They kind of vary by meeting. There's a lack of context. We don't have any prior history. We don't have both the plan and the forecast, so we can't do sequential or year-over-year analysis. We'll talk more about that in mistake number four, and then not shown but common, low information density charts. So there's a giant chart on the screen that shows four data points that could have been a row in a table. Franken slides, where we're clipping parts of dashboards and pasting them together, where there's often no consistency of metrics across the different copy-paste operations. Yes and or random precision, so saying churn is 18.12%. High cognitive load, just the wall of numbers, also covered in mistake, I think six, dumping. So all of those things to me constitute amateur presentation of SaaS metrics. And the way, in short, to get away from the amateur presentation is to use standard templates. So we're showing the same things every time with history. We have rules about how much precision we're showing. We only show churn as with basically no decimal places on churn, one decimal place on CAC ratio. We never, ever show pennies on dollars in the millions. Typically, we show kilodollars. All those things are just to say, hey, I'm a professional. I'm not presenting metrics in an amateur way. Number two, and by the way, we could do a whole talk just on that first bullet, but I'm going to make this an overview, and we can drill in later with other blog posts and presentations. So number two is cherry picking. This happens a lot, and it's usually kind of a form of selection bias, which is you just pick based not on what's important, but on what you want to show. So in this example, if it was a pretty bad quarter on new ARR, but it was a disastrous quarter, but that 71% is actually an average of say 100% on expansion, and 50 something percent on new logo, that you do two things. You cherry pick through abstraction, which is you don't show the breakout between new logo and expansion, because you're afraid of showing the disastrous 52% new logo performance. The other thing you do is you just leave stuff out. You don't show new ARR growth, you're not showing ending, well, you are showing ending ARR growth here. Yeah, you're showing year over year growth here, which is good, but you could just leave out things you don't want to show. There's also just misleading characterizations. Like if you showed this slide and said we're 93% of plan, I would beg to differ, because yes, you're 93% of ending ARR, but that's benefiting from what I call kind of a keel effect where you have this giant keel on the boat that stabilizes the number, it damps the variance, because you're dividing everything not by just the new ARR number of 3.5 million, but by starting ARR of 10.9, so it kind of damps out volatility in metrics. I'll call that keeling. So another thing you can do is smooth these, we'll talk about that in mistake seven. But anything that kind of cherry picks the metrics, to me is a big mistake. And the way to get around this, and as I mentioned, disaster strategic presentation is templates build trust. Templates build trust. So we build templates, we agree on what we're going to show, and then we show that every quarter whether the numbers are good or bad. In fact, we have no discretion, because we've agreed on the template. So that's cherry picking. Number three is miss benchmarking. So here's a company, they had a Q2, that was 55% ending ARR growth, 109% NDR, net dollar retention 25% rule of 40 score, 1.4 CAC ratio based on net new, so a net new CAC ratio of 1.4, and ARR per FTE full-time employee of 175K. So that was their quarter. And they go compare this to benchmark one. And you can say, well, gosh, you know, ending ARR growth on benchmark one was 30%, NDR was 104%, rule of 40 was 23%. So we're doing pretty well, right? We're significantly above benchmark on ending ARR growth, we're a scooch above it on net dollar retention, we're a fair bit above it, almost 10% above it on rule of 40. And we're right on the money on CAC ratio, and this particular benchmark didn't have ARR per FTE. So in this one, we're looking actually pretty good. And the slight confusion here, let me clarify, these five metrics are what Iconic calls their enterprise five. And I think they're a good set of metrics. So I'm using the Iconic enterprise five metrics. And I'm comparing them both to Iconic's benchmark, 75th percentile, 5100 million on the other side versus benchmarks, which is in the left table. The results here, though, however, the point remains unchanged, I just don't want to confuse you, is that on the right, we compare it to something else. And those benchmark bars, those benchmark figures are much higher. Their benchmark is saying, hey, ending ARR growth should be 100%, NDR should be 125, rule of 40 should be 55, CAC ratio should be 0.7, which strikes me as very good, and ARR per FTE should be 215. And the point here is that when I compare it to one benchmark, I look pretty darn good. Everything's green. When I compare it to the other one, everything is almost dark yellow, right, arguably red, but I didn't want a color coded red, make it hard to read. So what's going on here? And the answer is that this is one form, there's actually several forms of mis-benchmarking going on here, but one of them is the comparable set, which is benchmark it is a broad based mix of bootstrapped PE and VC backed companies, very useful, right? Both of these are very useful benchmarks, you just need to understand what you're comparing to and compare appropriately. Iconic is like super elite. They're a high end growth firm, they invest only in very successful growth companies. That benchmark also includes 13 high performance select public companies. So I'd say benchmarking is really kind of surveying, you know, the average SaaS company across a very broad base, whereas Iconic is kind of surveying, you know, Ivy League applicants or Ivy League acceptees, right? And the issue is, is there no right or wrong answer here, but if you want to go public, if you want to raise money from Iconic, you should probably benchmark against Iconic. And if you want a different outcome, you should benchmark against benchmark it, right? So the goal here is to make sure you're comparing yourself realistically to people who want to accomplish the same things you do. Segment is also a way to miss benchmark, right? And most benchmarks let you segment by company size, capital raise, growth rate, target market by SMB versus enterprise, average sales price, sales motion, PLG versus sales led, right? And it's actually very important when you're benchmarking to make sure you're comparing to appropriate people, not only in the comparable set of the whole benchmark, but actually the segments that the benchmark presents. And that can be hard, by the way, if you're doing a multi-year financial plan. And the reason I use different sizes on the two benchmark tables above was to raise that point, which is say the company just did 45 million and next quarter is going to break through to 50. Well, gosh, which size range do I benchmark against? And if I'm building a three-year or five-year long range plan, I need to remember to change the targets because the targets do change as I cross these size buckets. And then finally, I also did another trick to make this confusing, which is on one of the benchmarks I compared to 50th percentile and the other one I compared to 75th. And again, you just need to be mindful about picking which one are you trying to say, hey, we're better than the average or median SaaS company. Are you trying to say, hey, we're top tier, we're in the top 25% of all SaaS companies. And that's not something to forget, right? So you actually have three easy ways to miss benchmark, right? Miss benchmarking by comparing to people with different aspirations, by not segmenting appropriately, and by being either vague or misinterpreting the percentile. So that's number three. Number four is lack of context for metrics. If you ever read the Hitchhiker's Guide to the Galaxy, you know, there's a widely held tech meme that, you know, the answer to all things is 42, right? The supercomputer deep thought revealed that the answer to the great question of life, the universe and everything is 42. And my point here is, if you just walked into a room and somebody said, hi, Dave, the answer is 42, what would you make of it, right? Well, I'm not sure what the question is. I'm not sure what the question means. But the answer is 42. I mean, is that actually helpful? And the answer is no, that if you just walk in and go, you know, look, ACK is a great example of this. And your acquisition cost is $32,000 or 42 to keep in the meme. It sounds very high, but what's your ASP? What's your lifetime? What's your NDR? Right. What's your churn rate? Right. I don't know. Just telling me a number without knowing things doesn't help. One reason I like ratios, because they got to build in scaling, but even then I need to understand, oh, your CAC ratio is 1.5. How fast are you growing? Because if you're growing very fast, that's actually quite good. If your growth is flat, it's actually pretty bad. So rich context can include history, should include history, either trailing five or trailing nine quarters. This is a finance thing, but it allows you to look at seasonality, right? If you get five periods, I can compare to the year before, or nine periods, I can get to the year before and the year before that. Plan relative performance, do the math, right? Always do the math. Don't meet me as a board member or an investor, do the math. Forecast relative performance, growth, either sequential or year over year. Benchmark comparison performance, right? You can put a lot of columns into a table. Constant currency analysis. Hey, for an international company, let's say it's a kind of flat international company, it's growing A or R at 5%, but on a constant currency basis, maybe it's shrinking a toot, right? Especially when you're kind of hovering around zero, currencies can really influence things. So that's the kind of context we're talking about including, and this is just one example of rich context, that here I've got a slide. It's one of my, like the first slide of a board deck I would use. It shows the leaky bucket waterfall, and so some metrics associated with the leaky bucket. It talks about CAC and churn, and it gets in kind of number of customers, buyer NPS, and how many developers and employees, and employee satisfaction. It's pretty kind of balanced scorecard, a holistic view of a company, but it's presented with nine quarters, right? Trailing nine quarters of history as context. It presents growth as context on ending A or R, and new A or R, below that. And it also shows plan relative performance for the first quarter, right? So there's a lot of context on this slide, which means I can kind of conclude and deduce a lot because you've given me a lot to work with. And roughly the answer is, and conversely, if you give me a little to work with, I can't do that much with it. Piecemealing number five, not looking at metrics holistically. This is like comparing every kid in a school to the tallest, smartest, fastest, most musical, most artistic, most athletic, all at once, right? And this is, BCs do this, right? Because they look at, you know, across their whole portfolio, who is the best CAC, who is the best CAC payback period, who has the highest Rule of 40 score, and it's kind of a form of bludgeoning with metrics, right? Because it's unfair, and it's wrong, that no one is best at all those things, and that it really should be a function of your strategy to say, hey, our strategy is going to be a small land, big expand, so I would expect a high new logo CAC, and I would expect an awesome NTR. And then we should look at it through that lens. So let's not look at it piecemeal. So the solution to this problem is to think about how, what of your metrics kind of should travel together or go together, and then analyze and present them together, like group them in rows on your tables, put them on slides in your presentations. So right there in front of the person is the context they need. That's not just about the history of the metric and, you know, relative performance, but more importantly, it's about how it relates to other closely related metrics. Examples of this, CAC is a function of growth rate, right? So any high growth company is carrying a lot of ramping and unproductive resources, they're investing in marketing to generate sales that won't happen for 6 to 12 months, right? So CAC and growth rate kind of should be looked at together. New logo CAC as a function of lifetime. Wow, you've got a, let's just say a CAC ratio in this case of, you know, 2.0, that's high, but the average lifetime is 10 years or 12 years. Wow. Well, maybe it's worth it. Or conversely, maybe the lifetime isn't that impressive, but the expansion rate is. So again, your new logo CAC is, you know, 1.8, well, that's high, but your NDR is 135%. That's amazing. So we're paying a lot to get these people who, you know, inflate like balloons once we get them. And that's not a bad thing. And you'll only see that if you kind of weave it together with a narrative that doesn't look at each metric in isolation and just go, well, our CAC of 1.8 is really too high and we need to improve it. But the narrative being our CAC of 1.8 is really high and we do want to get it down, but given an NDR of 135%, we think it's okay. And we think it makes, you know, the unit economics, if you will, work. Number six is kind of just dumping metrics where you literally just dump a wall of numbers on somebody. And the solution is correct on this slide. The problem is wrong, but the problem is simple. You just put a wall of numbers up. There's no structuring. There's no layering. You just drown and dump metrics all over the audience. And the solution, you know, jokingly is like ogres, you should have layers, right? Show me the blended CAC first and then separate it into new logo versus expansion. Show me churn overall and then segmented by loss versus downsell. Show me the CAC blended for the whole company and then segmented by SMB bid market versus enterprise, right? So it's this top down kind of layered approach to presenting metrics that avoids dumping. Now, the solution here in funnels, this is a lot of funnel analysis and go to market is to do hops. So rather than show me every single stage of the marketing and sales funnel and give me a wall of conversion rates to look at, hop, right? MQL to S1, stage one opportunity, S1 to S2, very important transfer point, S2 to close. And let's just look at those. And if one of those is off, then we could have drill down slides, appendix slides, live spreadsheets, if you want to, where you can drill in and look, but kind of, in some ways, this one is just about audience empathy. Don't overwhelm them with a wall of numbers, bring information in a structured way. Number seven is what I call smooth metrics operator, you're a smooth operator, i.e. the excessive use of smoothing. And I just want you to react to the first table up there and go, okay, the LTV to CAC, 7, 6, 7, 6, 8, 6, 5, 5, 2, and then plan with 7, 3, okay. So 32% plan. Now look at the second table, where we go 7, 3, 5, 6, 6, 1, 2, 4, whoa, plan of 7.3, we're at 33% plan, what the heck is going on with LTV to CAC in 2Q23? Which signals the iceberg ahead alarm bell better, right? The first table or the second table? And the answer is the second row of the second table is what I, as a board member, want to see, right? Sometimes the variance is noise. And I picked LTV to CAC because there's an argument that seasonality matters in this metric, and you really should look at a trailing 12-month group. I buy all that. If you want to present it smooth, they're trailing 12 months, go ahead and do that, but also show it quarterly. Because sometimes the variance is noise, sometimes it's the signal. And by the way, it's way easier for me, given a row of numbers, to smooth them. If I want to smooth them, I can smooth them myself. It is virtually impossible to unsmooth them. So in the example I'll give here is just say a competitor wants a new low-end edition product that's causing churn, i.e. lifetime shrinkage, because people are defecting to it in large number, i.e. they're not renewing because they're going to this new low-end product. By the way, that new low-end product may also put pressure on our CAC because we're having to cut prices, right, which increases our CAC ratio. Therefore, LTV to CAC starts to plummet, right? And the answer as a board member is, how long do you want to wait to find that out? And if you're doing everything smooth on a trailing 12-month basis, it could be up to a year until the full kind of horror of that new reality is clear from the numbers. So I'm actually fairly opposed to smoothing. If I never saw a smooth metric, I'd be okay with it. But if you really feel the need to smooth, well, you should think real hard about also showing the unsmooth. Number eight here is just forgetting the question we're trying to answer. We get axle-wrapped in formulas and calculations. The calculations get political. Hey, marketing is understating the cost per opportunity. Or sometimes we forget how we interpret the metrics we're calculating. Hey, when we talk about churn rate, what are we concluding from that? Are we using it as kind of a proxy for customer satisfaction? Like, hey, the customers are happy, churn rates are low? Or are we using it kind of as a financial way to evaluate the install base because we're trying to do math to figure out how much our install base is worth, right? If you have a question about how to calculate it, stepping back up a level to say, how are we going to interpret it? It can be very helpful to help you answer the question about how you want to calculate it. It might also help you answer the question is maybe there's a better metric to calculate that way. Like, maybe we should use NPS, right, or renewal intent to, well, NPS, let's say, if we want to know how happy the install base is, use NPS. If we want to know propensity to renew, we should ask a renewal question. If we want to know financial value, we should probably use NDR and just look at historical expansion. So cost per opportunity is another one. It can be a way to measure demand gen efficiency versus incremental cost, right? Are we trying to analyze our business to figure out overall how effective is our demand gen compared to other companies? Or are we trying to answer the question, hey, marketing, if I need 30 more opportunities, how much money do you need, right? And that will affect whether you calculate cost per opt, i.e. the cost part of that on including both fixed or only variable costs. And then finally, attribution. You know, I've seen people get very axle wrapped on marketing channel performance where they're actually trying to answer the question is, does our, you know, go-to-market model work? Or should we keep doing outbound SDRs, right? And I don't need to dive in deep and do all this difficult channel attribution problems. I can just step up and say like, hey, how much did we spend on demand gen last quarter? How many opti's did we get? Maybe do some phase shifting. How much did we spend on SDRs? How many opti's did we get? And I can get a first order answer. And sometimes people are so buried in the detail, they forget to start at the top. That's number eight. I'm going to move quickly through the last two here, number nine and 10. Nine is just forgetting the investor point of view. You know, first remember three things. One, investors use metrics for screening. They look at roughly 100 deals for every deal they do. It's why they like compound metrics. And your goal, right, with any kind of teaser deck is simply to land in the right pile. You're not trying to close a financing transaction, you're trying to make sure you end up with the right pile on their desk. Second thing to know is investors have their own fairly rigid ways of looking at things, right? You know, 100 deals every year, you start to build your own framework and system for evaluating them. And well, you know, you're not really interested in changing that because it's been developed over time and experience. So I think as an entrepreneur, it's best to kind of seek first to understand and maybe seek only to understand how a given investor looks at metrics. Because if you try and have a battle with them to change their worldview, you know, you could risk winning the battle and losing the war. See, you should look at cash payment period on a cash basis, right? You could spend a whole meeting fighting about that. And they would say interesting discussion, but we're passing on the investment, right? Because you forgot to tell them why they should invest in your company. So I was guilty of that a couple times myself wouldn't surprise you. And then number 10, the last one's a bit of a joke, which is actually based on a real story where a poor CMO put a chart up that looked like this. And the CEO instantly saw, oh, my God, trade show opportunities cost $25,000 each. They burned into the back of his or her retina. And it stayed there for years, this retinal burn, like, you know, every time you see the word trade shows, like, oh, God, they cost $25,000. So I almost feel this has got to be the post traumatic shocker. Like you, you get a trauma and you see some shocking number, and somehow it sticks with you and you remember it for a really long time. So your job when you're presenting metrics is to avoid shocking people. So if you're going to show a chart like this first, try not to in this case, you may have no choice. But if you're going to show one like this, disclaim it first. So that way, when it hits, you've already talked about attribution, and why in the first touch attribution system in this particular segment, trade shows show up very, very poorly. And then because if you show the number first, after it's burned into the retina, it's literally too late. So, look, let's wind this down now. If you want to be a metrics pro, and you want to kind of present match, present and analyze SaaS metrics in a really good way, you need to avoid these 10 all too common mistakes. One amateur presentation, master the basics of details and formatting and consistency and templates to cherry picking templates, build trust, right? So use templates as a way to build trust, present the entire template every time, never let managers convince you to leave this row out this quarter, should you replace this row with that row, templates, build trust three, miss benchmarking, make sure you compare to the right segment, the right percentile, and using the right overall comparable set, so that your comparisons are meaningful. For omitting context, again, user empathy, reader empathy, make it easy for them to analyze the slide and learn something. Don't just walk up to them and say 42, right? Show them a table like the one I showed you. Five, piecemealing. Remember, certain metrics naturally go together, should be interpreted together. So present them together, cluster of rows at a table, sections of a slide. Six, dumping. Don't drop a wall of numbers on people, structure it. Ogres have layers, so should your metrics and funnels, hop over them in order to make it simple, because if there's a problem, the hop will reveal it and you can always drill down later. Seven, avoid the excessive use of smoothing, I might even say avoid smoothing in general. Number eight, if you get stuck, go back to the top and say, what question are we trying to answer again? Number nine, remember, investors have their own fairly rigid views on how to interpret these things, and you're better off understanding their view than trying to change it. And number 10, avoid retinal burn by disclaiming any shocking numbers before you put them up, because if you try to disclaim them later, after the brawl has broken out in the meeting, it's going to be too late. I want to thank everybody for coming, and I think we may do a few questions right now here. Ray, back to you. Hey, thank you as always. So much information you packed into 30 minutes. So there's going to be a lot of questions, but here's the one that I think is most important to me. When you first enter a new company, you're the new CEO, you're the new CFO, I've always thought sitting down with your investors and looking at what metrics are important to them and having them define how they do the calculation is a great first step. Is that a good first step, even for an existing CEO where they need to go back and say, I think we need to get on the same page on how we're calculating these metrics. So I think yes is the short answer. I think the slightly longer answer is, I like to get at the first order, what ones should we show? I mean, the most important to me, to be honest, is what's the summary slide? What slide one of the deck where the CEO presents 10 metrics on the table, what 10 metrics do we want there? And then I would say for each VP who presents at the meeting or C-level who presents at the meeting, same question. And I would put considerable time into what metrics you put on that slide, because that says what's important to us. And then after that, I would say, how do we calculate them to make sure they're meaningful and we understand them. And then tertiarily, I would tend to delegate it to the VP and the ops people to say, hey, for the drill down, it's on you. But so I think that's the thing most people forget, they're kind of random in their approach, they're not layered. And they should really start with the summary slides, whether that's the summary for the board meeting, which may not be the same thing as the summary for the QBR. But I would start with the board, do that first. And then I do the same thing with the e-staff at the QBR. But for those people who may not yet be following with you, which I'm sure there's not too many, what's the best way to reach out and follow your work, Dave? Sure, probably Twitter. Twitter is probably the best way. I'll add a slide to the deck when we post it online that'll have some contact information. Twitter, Kellogg, KELLBLOG. The public email I have, which usually gets through all the spam filters is Dave Kellogg, no dots, Dave Kellogg, two Gs, at mail.com, not Gmail, DaveKellogg at mail.com also works. Perfect. And there's one other place I think everyone should be following you on their favorite podcast app under SaaS Talk with the Metrics Brothers. And who would my Metrics Brother be, Ray? I can't remember. Well, I have to say I'm Ray Growth Rike, and that was Dave CAC Kellogg, the Metrics Brothers. Thanks, Dave. Thanks, Ray.