5 Metrics that Matter When Raising Your SaaS Series A and Series B with Christoph Janz of Point 9

What is it that investors want to see or what is it that really gets them excited? A combination between growth and efficiency. As I'm sure you've heard many times, investors do care a lot more about efficiency than they did when money was very cheap or free some years ago. So they will look at things like a burn multiple at earlier stages and like Rule of 40 or Rule of 50 at later stages. But nevertheless to even get them interested, growth is still the number one thing. And as a rough rule of thumb, a company that has a million or a couple of million in err, I think needs to grow at 2 or 3x to really get a lot of investor interest. Please welcome to the stage Christoph Jans, partner at Point Nine. Hello everybody. It's great to be back at Saster. Thank you so much for joining. I've been to almost every Saster conferen for the last 10 years or so. That usually means taking a 10, 11, 12 hour flight from Germany for me where, where I'm based this time it was just a 25 minute Uber drive because I moved to Mountain View for a couple of months. So that makes it very convenient. So really quick intro. I'm Christoph Jans, I'm one of the partners at Point Nine Capital. Point Nine is an early stage venture capital firm focused on SaaS investments. We've been around for about years, have invested in a variety of SaaS companies, some of which you might have heard about such as Algolia, Contentful, Loom, Zendesk a long time ago or some of the more recent companies. Some of the more recent investments such as Azio, Misteria and several others. We're very geo agnostic, we're based in Europe but we invest all over Europe, in the us, sometimes even Australia. I think the founder of Quiller, a company out of Australia that we invested in, might be somewhere in the audience. All right, so what I want to talk about today is the metrics that matter. Like what is the data, what are the metrics that you need to convince SaaS investors with the right data? I've tried to focus this on series A and series B rounds, which I'm assuming is quite relevant for many of of you here in the audience. A lot of this is also relevant for, for later rounds with maybe some nuances. At the seed stage where we invest things are a bit different. There is, there are a lot less metrics that we can use to make up our minds. So what I'm going to talk about is not, are not all the things that you need to Pitch point nine. This is more like what I think you need as you progress and try to raise a Series A or Series B round a bit later in your journey. I talk a bit about what investors will be looking for, what based on our experience will make them excited about your company and I will share some do's and don'ts. First, maybe let's take a step back for just half a minute. Why should you spend the time to really get your act together before you start fundraising meetings? That's one of the things I feel quite strongly about. If your data is self explanatory, in good shape, easy to understand and aligned with what investors really care about, you will avoid a lot of follow up questions and a lot of back and forth with the investors that you are going to talk to and that are going to run in due diligence on you. That means you spend less time clarifying what does this metric mean? Can you send some more data on xyz which saves you a lot of time and allows you to talk to more investors in parallel and run a tighter process which is very valuable, especially in 2024 where fundraising has become much much harder than it used to be a couple of years ago. Last but not least, if you really have clean, high quality data, that alone increases confidence with investors and I haven't even mentioned it here, I'm hoping that almost everything that I'm going to talk about is data and metrics that you're interested in yourself anyway. Otherwise there would be a pretty problematic disconnect between what investors are interested in and what you are actually what you care about and what you use to run the business. The first thing you need, and that is maybe obvious or maybe a no brainer, a KPI sheet both with your actuals and historic numbers and your projections. Depending on the stage of the company, this can go back for the last two years or so or it can be since launch. If the company maybe didn't really launch or commercialize the first product 18 months ago or so. I think projections should usually be for around two years. I think one year is usually not enough because you raise a certain amount that should give you Runway for 18 months or two years and so it's good to have a budget for that period. At the same time, I don't think it makes a lot of sense at this stage to have plans for years and years into the future. I think two years is about right. Then in terms of the granularity for earlier stage companies or companies with a PLG Motion product led Growth I think that month over month is usually the right choice. Somewhat later stage companies or more enterprise SaaS oriented companies with a longer sales cycle might also find that quarter over quarter numbers work better for them. So what goes into that sheet? You can really use the famous first principle thinking here and start thinking about what drives your business. So you start at the top of your funnel and you move down step by step. What that usually means in a PLG business is you have visitors or website visits at the very top. Then the next step is usually a sign up or a trial and then you have a conversion to a paid customer. In more Enterprise y SaaS the funnel usually looks a little bit different. You typically have MQLs at the top SQLs, maybe you have one or two steps in between and then you also have new customers. And then of course you also need to report and budget for churn, both logo churn meaning the number of percentage of customers that you lose and revenue churn which is the same but in regards to the dollars that you lose to churn then you basically just go down. You cover key financial metrics like revenue, MRR, ARR which in a SaaS company is basically the same thing. Usually unless you have a lot of one time revenues every all the revenue is recurring and ARR is just mrr times 12. So that's simple cogs, R and D S and M G and a profit and loss. You can keep it pretty simple. You have cash in, cash out, burn money in the bank. Probably all the things that you're also thinking about when you think about a financial plan. One thing to I think include in a for a SaaS company is what we could call like the MRR or ARR movements or that waterfall new business, er, expansion ARR on the one hand, contraction and CHURN on the other hand. Sometimes you also have reactivation. So basically these components which collectively determine how much ARR you are adding in every period. A couple of more comments on the KPI sheet and the financial plan. I think the historic numbers and the projections should follow the same logic and the same model. This is something that we quite oftentimes see founders do it like not quite right. So you get a model, you try to understand it, look at all the projections and then you ask for the actuals and it's a completely different model. This just makes it really hard to process. Right. I think you should, you shouldn't make investors understand two different models. That doesn't make much sense. And also keep in mind investors at the series A and B stage, they will look at your historic trajectory and will try to make up their mind if that makes them confident in believing what you would say you're going to do in the future. Any very big changes or sudden improvements are somewhat suspicious. So you have to explain what them really well and you make it really easy if you allow investors to easily connect the dots between the past through the present and into the future. Another thing is that you can really keep it simple. Not too simple. I talk about that too, but pretty simple. What I mean by that is that we see a lot of financial plans with a lot of really unnecessary details. It usually happens when you get an accountant involved, like when you export anything from an accounting system and you don't clean it up massively. It'll have dozens, maybe hundreds of line items of cost categories. But I'm really not interested in how much you're spending on stamps versus stationary versus bus tickets. So you have to group it into categories that really matter and really it should reflect the business and therefore it can't be too simple. So too simple is a business plan that takes projections out of the air and has hard coded numbers for revenue for revenues in 18 months from now. And I don't understand how that revenue builds up. So it should be a logical, easy to understand model with the right level of simplicity and complexity. As a rule of thumb, I have never seen in my life an Excel plan or a Google Sheets plan that had Dozens or maybe 20 pages or tabs where it actually made sense. None. I've seen a lot of these, but I think they are all just really poorly done. If anybody wants to challenge me on that, I'm totally up for it. If you can make the case that there is a business, at least in a startup world that is so complex that you need 20 pages to model that let me know I haven't seen it yet. I think it can be on 2, 3, 4, 5 pages maximum. It's usually one page or tab which has all the relevant metrics and then maybe you have an extra tab for some additional assumptions or maybe where you model like how your sales team grows a few things like this, but I think it has to be digestible by an investor within half an hour. I think SaaS is not such a complicated business model that you need to like totally reinvent the world here. Last but not least for this one, make sure you include the typical benchmarks or health indicators that investors are looking for in SaaS. Things like customer acquisition costs, burn, multiple arrhat and metrics like that. So what is it that investors want to see or what is it that really gets them excited? A combination between growth and efficiency. As I'm sure you've heard many times or experienced in the last two or three years, investors do care a lot more about efficiency than they did when money was very cheap or free some years ago. So they will look at things like a burn multiple at earlier stages and like Rule of 40 or Rule of 50 at later stages. But nevertheless to even get them interested, growth is still the number one thing. And as a rough rule of thumb, a company that has a million or a couple of million in err, I think needs to grow at 2 or 3x to really get a lot of investor interest. If you are at 5 to 10 million ARR, I think it's usually still 2x year over year growth to get a lot of investor interest. I just mentioned the last things here briefly because there are a bunch of other things to cover. 100% NDR is something that investors really care for. Doesn't mean that you necessarily have to be there at the Series A. But I think there needs to be some path of getting there because people are really afraid of investing in businesses that keep churning dollars. In the long run. It's different for more consumary or PLG style companies that have strong virality or that have a way of generating so much new business that they can afford a higher churn rate. And I mentioned this already a little bit before. You need a credible plan based on the trajectory of the past last couple of months, last couple of quarters that is aligned with how much you want to raise. So whatever your budget says you need to, your need in financing needs to be aligned with what you actually want to raise. And this might be particularly relevant for Series B companies you have. I think you have to have realistic valuation expectations. A lot of companies out there have raised a Series A or maybe a series B in 2020 or 2021 and they might not have grown into that valuation yet because the multiples in the markets have just compressed by 80% or something. So I think you have to be realistic about that. The next piece that I want to highlight that I think you should have whether you put it into your deck or into your data room. But in any case you need to have it ready to share before you start the fundraising process are various types of cohort analyzers. A cohort analysis is really the only way to get a sense for churn and retention. Customer acquisition costs, payback, customer lifetime value in a subscription business, I wrote about this a lot for many years. If you if it's not that clear to you, I point you to some blog posts and templates. Later on. You should have a cohort analysis for your customers as well as a as well as a cohort analyzers based on revenue, meaning ARR or mrr. And you could also consider having a cohort analysis based on a usage indicator, like a certain activity that people do in your software. Especially if you're early on. This can be very useful. Let's say you have a number of customers on an annual plan. Most of them started just six to nine months ago. So basically you have no retention data if you look at revenue for those. But if you look at how they behave, how they use the product, how they come back, you get a leading indicator for their future churning or retention behavior, which can be super useful for yourself, but which also might be something that investors will ask you about. There are various nice, interesting, maybe cool ways to visualize the data. I've shown only two examples here. The chart on the left shows the retention for different cohorts at this one month, three months and six months mark. So what you can see in this chart if you go from the left to the right that the more recent cohorts are doing better, that is a great sign for this fictional company here. If you look at the chart on the right, it shows for different cohorts the contribution margin over time. Contribution margin basically just means it's the gross profit of that cohort minus the customer acquisition costs. And so what you can see here is that after how many months after the initial acquisition does each of these cohorts break even on their customer acquisition costs? Couple of more tips and tricks or do's and don'ts on this. Don't mix monthly and annual plans. If you do this all in one bucket it makes more sense because some of the annually and the annual plan customers, they didn't even have a chance to cancel their subscriptions. You have to look at cohort analyzers for monthly customers and annual plan customers separately. Similarly, if you serve very different types of customers, it makes sense to run these cohort analyzers on a per segment basis. And because the averages across vastly different customer types really don't really give you the same insights. And if your overall retention isn't great, maybe because you have some non ICP customers or other some other factors that kind of don't make you look really good. If you do that cohort analysis, you can consider zooming into your best performing customer segments so A little bit of cherry picking in a fundraising process is allowed. You just have to be transparent about it, you have to explain it and you have to. You shouldn't overdo it if you end up with a statement like for our most active users, which we define as those that use the app at least once a week, our wow to MAO ratio is 100%. That is, that doesn't make any sense. So you can do that, but it has to make sense. What is it that investors are looking for here? They want to see excellent retention because SaaS is all about keeping customers in the long run and they want to see this at least in one segment. As I've mentioned, they want to see positive trends and they will look at your at various cohorts to see if they can find some positive trends there. It could be that the more recent cohorts look better than the older ones. It could be like a so called smiling cohort graph which describes the situation when after an initial period of some churn in a certain cohort, the remaining customers spend more and more so that the curve goes up again after some years. The next thing I want to talk about is everything around marketing and lead generation. You'll have to include some data in your fundraising materials about how you are acquiring customers and where you get your leads from. That's usually data on how much you spend, how many leads you get, how many conversions customers you get on a channel by channel or source by source basis. You can see here a dashboard from Gecko Board can of course also be a spreadsheet and you use it to show the costs for every lead or trial or SQL or customer depending on your conversion model for different sources. This is another example from a portfolio company. This is a drills down into one specific channel, in this case LinkedIn. So this starts with the entire universe of contactable leads on LinkedIn and it goes down through the steps like how many persons are reachable, how many emails or LinkedIn messages did we send that month, how many responded, how many responded to the to the second touch, how many booked a demo and these things. So make it clear how you've calculated your cogs, because there is probably not one generally accepted way of how customer acquisition costs are calculated. You can do it in many different ways. So make sure to explain how you do that or add a footnote otherwise that will for sure trigger a follow up question from an investor. Like they will ask are these customer acquisition costs? Are they blended across different channels? How about the organic signups? How do the, how do you factor in those so you can define how you do it and then just be open about it. You can also in some cases exclude some of the initial startup or build up costs in your budget. Because you're in your budget, you're estimating the costs to acquire the next customer and the cost for that next customer might be lower than the first couple of customers because initially you started with nothing. And so the headcount costs were probably very high and for fortune to the number of fleets or customers that you acquired. But you have to be transparent about it and has to make sense to to you and you need to be able to explain it. What investors really are looking for here is they want to see one source or one channel that's clearly working and that seems to be efficient and scalable. It sounds simple, but it's actually pretty hard to prove that. Like the question. If you get the question, how do you know that this channel or this source will scale to another 10x or 20x? It's a pretty hard to answer question because you don't really know it until you've done it. But you can back up your argumentation there, but expect that question to come up next. 1. Sales and pipeline. Investors will of course ask you about how you're doing sales, what your pipeline look, how your pipeline has evolved. This here is a snapshot of a pipeline evolution chart. This one is another visualization that I really like because it really shows you how the dollars are flowing through the different pipeline stages month over month or quarter over quarter. And what investors are looking for here, ideally every quarter is a new record in terms of new pipeline added and closed won. That's not always achievable, but that's what investors of course, of course gets them excited. Another thing that we often observe when we see when we work with companies or when we see pitches is that the pipeline in the middle of the funnel or top of the funnel keeps growing, but the closed one numbers don't really go up in the same proportion. What that usually means is something isn't quite working. Like maybe the ICP definition isn't tight enough, or you're not really disciplined when it comes to removing deals from the pipeline that you should remove. And this is one of the things that you can see in this chart here, because this also includes at the bottom the dollars in pipeline value that you've lost or that you've removed, which is pretty important. Another sheet which I think is extremely helpful, which if you look at it as an investor, really tells you a lot is the performance of your entire sales team. And you can anonymize the names quarter over quarter relative to their targets, including those AES that didn't work out. This is just an example sheet here with some placeholder data where you can see how that company added salespeople early on. There is a lot of yellow and red, so it took some time until this company found the right sales label. That John here, for example, he didn't really work out, so after three quarters he had to go. Whereas Mary didn't achieve her targets in the first two quarters, but then in the third quarter she did. And so she kept performing really well over time. And then you see that in the subsequent quarters, the company added more salespeople and was able to show that most of them got to their target levels after one or two or three quarters. And that is really exactly what investors care about. They want to see proof that you can hire and train AES and get them to reach their quota in a repeatable, pretty predictable manner. That doesn't mean that every salesperson will work out. It's actually quite the opposite. If an investor sees a company with 10 or 20 salespeople and they've never let go a salesperson that almost sells you something tells you that something isn't working because it's unheard of that every single salesperson works out. Last but not least, I think we are on the AI stage here, so we have to talk a little bit about AI before I finish. A lot in AI is less about data and more about explaining what you do and the product and your vision. But there is also some data that investors will probably ask you about. They will probably ask you for usage data because there are a lot of companies out there that had revenue numbers going through the roof and then had a lot of churn kick in as well. A lot of budgets are available now because there is so much experimentation. Investors care really about the details. They want to see your retention both on a revenue perspective based both on a revenue basis, but also on a usage basis depending on the product that you're building. If you like, train your own model or if you fine tune an existing model, investors might want to ask, might ask about the performance of that model, how the performance improves with more data. Is a proprietary, is that data proprietary? Does it give you an edge over time? Like what is also the threshold in terms of what a customer expects in terms of the accuracy of the AI and what you can deliver. And last but not least, because I see I'm running out of time. Something that we are not really used to from the non AI SaaS world is really cost of goods sold. So they will ask you about the impact of using AI on unit economics. I think I at least and I think others too are not that worried about the cost of APIs, usually because those costs are expected to go down pretty rapidly. But if you have an AI product with humans in the loop, there will be the question like is it credible that you can remove those humans and really automate everything in the near future? So what investors want to see when it comes to AI is there real usage and real retention or is it just a fad? If you want to look up some of these some of the posts which are like the basis for this short talk, go to our website www.zero.9.com on a letters, there's a library tab. If you look for things like cohorts, templates, fundraising, you'll find a lot of posts about everything from cohorts to financial metrics, templates and related materials. Thank you so much for your attention.