SaaS Financial Plan 2.0

Almost exactly four years ago I published a financial plan template for SaaS startups based on a model that I had created for Zendesk a few years earlier. I received a lot of great feedback on the template and the original post remains one of the most viewed posts on this blog up to this day.

In the last few weeks I've finally found some time to create a "v2" of the template ... just in time for a little Easter gift to the SaaS community. ;-) I'd recommend that you read this post first since it includes some important notes, but if you prefer to check out the template right away click here to download the Excel sheet.

The original v1 model was a very simple plan for early-stage SaaS startups with a low-touch sales model. As I wrote in the original post:

It's a simple plan for an early-stage SaaS startup with a low-touch sales model – a company which markets a SaaS solution via its website, offers a 30 day free trial, gets most of its trial users organically and through online marketing and converts them into paying customer with very little human interaction. Therefore the key drivers of my imaginary startup are organic growth rate, marketing budget and customer acquisition costs, conversion rate, ARPU and churn rate. If you have a SaaS startup with a higher-touch sales model where revenue growth is largely driven by sales headcount, the plan needs to be modified accordingly.

The new version comes with a number of improvements:

• Support for multiple pricing tiers
• Support for annual contracts with annual pre-payments
• Much more solid headcount planning
• Better visibility into "MRR movements"
• Better cash-flow planning
• Charts galore :-)

The downside of these improvements is that the spreadsheet has become significantly larger and more complex, but I tried my best to find the right balance. Also, the vast majority of the numbers in the sheet are calculated and the number of input cells is fairly limited.

The spreadsheet should be pretty self-explanatory but I've included a number of comments in the spreadsheet. Make sure to check them out - some of them are important in order to understand the model (in case you're not familiar with that Excel feature, hover over the little red triangles).

Here are a some additional notes:

1) General comments

• The sheet is hot off the press and given the large amount of formulas I can't rule out that there are bugs. If you find one, please email me at and I'll fix it ASAP.
• Blue numbers indicate data-entry cells. Black and grey numbers are computed.
• The model contains a lot of simplifications. Don't expect that it will perfectly fit your specific business - consider it a starting point.

2.) "Summary" tab

• The "Summary" tab contains only two types of input cells: Your starting bank balance and cash injections from financings. Everything else is calculated, mostly using data from subsequent tabs.
• As with all input cells in the model, consider the values that I've put in to be dummy data. Fill those cells with your own data and assumptions.
• The model doesn't take into account interest or taxes (except for payroll taxes).
• The "Revenues" line shows your end-of-month MRR for the respective month. This is not compliant with the US GAAP definition of "revenues", which uses different revenue recognition rules, but since SaaS companies live and breathe MRR I think it's the right approach for a SaaS financial model.

3.) "Revenues" tab

• The model assumes that you have three pricing tiers. I've called them "Basic", "Pro" and "Enterprise". If you have more or fewer pricing plans you can of course adjust the model accordingly (with some effort). It is further assumed that all Basic and Pro customers are on monthly plans and that all Enterprise customers are on annual plans.
• The model assumes that you're getting signups organically and via paid marketing and that you're converting a percentage of them into Basic customers and Pro customers. You can change the key assumptions such as your organic growth rate and your conversion rates in the grey area on the left.
• The Enterprise customer segment follows a different logic, based on the assumption that Enterprise customer acquisition is sales-driven as opposed to the marketing-driven low-touch sales model for Basic and Pro customers. The key drivers in the Enterprise segment of the model are your revenue targets, sales team quotas and your assumptions for churn and upsells.
• The spreadsheet shows the impact of e.g. Basic customers who upgrade to Pro and Pro customers who upgrade to Enterprise, but to keep things simple it doesn't support each and every possible movement between plans. For example, I didn't include the option for Basic customers to upgrade to Enterprise straight away or for Enterprise customers to downgrade. If this is a relevant factor in your business, you can of course accommodate for that by adding a few extra rows.
• For Basic and Pro customers, the model allows you to project ARPA development using a given ARPA at the beginning of the planning period along with assumptions on monthly ARPA increases. For Enterprise customers, the model assumes pricing increases at the time of renewal but not during the term of the subscription. Depending on your specific pricing model you'll have to modify that, e.g. to allow for Enterprise customers to add more seats continuously.
• In order to be able to calculate churn for Enterprise customers in the 1st year of the plan, it is assumed that existing Enterprise customers have been acquired over the course of the previous 12 months. This is of course a somewhat theoretical assumption and you need to adjust the model to include your actual numbers.
• As you can see in one of the charts below the numbers, the model allows you to calculate your "MRR movements". It's worth pointing out that the model currently doesn't show "Expansion MRR" and "Contraction MRR" separately but only the delta of the two, which I've called "Net Expansion MRR". In order to calculate Expansion MRR and Contraction MRR separately I'd have to add a couple of additional rows. To avoid making things too complicated, I decided against doing that for now. Fortunately ChartMogul (a Point Nine portfolio company, sorry for the plug) makes it super easy to drill down into all of your MRR movements.
• Please note that the CAC data and "CAC payback time" calculation are based on pretty crude simplifications. A solid planning of CAC payback times, CAC/LTV ratios etc. would require a lot of additional input data.
• The rows with the "Thereof bonuses..." label contain matrix formulas. Handle with care. :)

4.) "Costs" tab

• In order to adjust headcount planning in the G&A, R&D and marketing departments, change the assumptions for start date, base salary and bonus in the grey "Assumptions" area. You can remove, change or add roles in column H.
• With the exception of the VP of Sales role, sales staff headcount planning is done on the separate "Sales Team Hiring Plan" tab (re-using a model that I've built for this post). It calculates the number of sales people that you need based on the growth targets for your Enterprise customer segment, the quota of your sales people and a few other variables.
• Headcount planning for the Customer Success team is (again with the exception of the VP) done formulaically as well, based on assumptions on how many customers a customer success team member can handle.
• It is assumed that there's only one team, which I've called Customer Success, which does both customer support and customer success. Many SaaS companies have different teams for the two functions; if you're one of them you can adjust the plan accordingly. 
• The costs for the Customer Success team are attributed to CoGS. This is debatable – if your Customer Success team plays an important role in converting signups or upselling customers you should consider allocating at least a portion of these costs to S&M and include those costs in your CACs. Please note that changing the "cost type" in column I will not automatically move the costs to a different category on the "Summary" tab so you'll have to do that manually.
• The model assumes that payroll tax is the same for all employees. This may have to be adjusted, e.g. if you have people in different countries.
• Regarding the cash impact of expenses, the model assumes that:
• payroll taxes are paid monthly
• bonuses are paid yearly (except for the sales team)
• sales team bonuses are paid quarterly (since bonuses/commissions play a much stronger role in sales compared to other departments)
• The model (somewhat simplistically) assumes that there are no capital expenditures. If you make investments into things like servers, computers or office furniture you should add these expenses accordingly.

If you've made it this far and haven't downloaded the Excel sheet yet: Here it is.

If you have any questions, comments or suggestions, let me know in the comments or email me. And if you like the model, tweet it out. :)

Finally, big thanks to Chris Amani, Sr. Finance Director at Humanity, as well as to Pawel and Dominik of Point Nine, for reviewing drafts of the model and for providing valuable feedback.

Key Revenue Metrics for SaaS companies

Thanks to Nick Franklin for reviewing a draft of this post!

When I talk to SaaS startups and take a look at their metrics, it still happens quite often that some of the numbers aren’t quite clear to me and it takes some time to clarify things. I’m not referring to sophisticated reports or analyses but to the much more mundane question of what exactly people mean when they use a term like “revenues”.

It’s maybe not surprising that there’s sometimes confusion, given that there are several different ways to express revenues of a SaaS company and even more ways to label them: revenues, sales, turnover, MRR, CMRR, ARR, cash inflow, cash-in, billings, bookings, GAAP revenues, income and so on. That said, I believe most SaaS companies can focus on a small number of revenue metrics which aren’t overly complicated.

If everyone in the SaaS world can agree on the same nomenclature, I think that will make communication between founders and investors more efficient and will save all of us some time. So let’s take a look at the most important revenue metrics in SaaS.


MRR

Monthly Recurring Revenue (MRR) is, as the name suggests, revenue that you anticipate to recur on a monthly basis. If you’re selling monthly subscriptions, MRR is simply the price paid each month for the subscription. If your customers are paying you for more than one month upfront, you simply divide the amount you received by the number of months in the subscription period.

Say you’ve acquired two new customers. Customer A has signed up for a monthly subscription at $100 per month and Customer B has signed up for an annual subscription of $1100 per year. In this scenario, customer A increases your MRR by $100 whereas customer B increases your MRR by $91.67 ($1100/12).

This simple metric is the most important metric a subscription business needs to calculate, which is why ChartMogul (which for disclosure we’re an investor in) is highly centered on MRR. If you focus completely on MRR and calculate it correctly you’re in pretty good shape, so feel free to stop here and ignore the rest of this post. :-)


ARR

Annual Recurring Revenue (ARR) follows exactly the same concept. The only difference is that it measures your annually recurring revenue as opposed to your monthly recurring revenue, so your ARR is 12x your MRR.

Since both metrics are interchangeable, it doesn’t matter if you’re tracking MRR or ARR. I personally prefer MRR, but I can’t tell you why. Probably just out of habit.


Cash inflow

Cash inflow or “Cash In” is the amount of money that you’ve received in your bank account. In the example above, it’s $100 for customer A and $1100 for customer B. A related term from the accounting world is “Accounts Receivable” and refers to cash that is legally owed to you but which you haven’t received yet. Since SaaS companies are typically paid upfront, at least for a month of subscription if not a year, you usually don’t have to worry too much about this and can focus on cash inflow.


Revenues

Revenue means MRR plus any non-recurring revenue such as implementation fees, setup fees or charges for professional services. Let’s say you’re charging a customer $1000 for a data migration project that takes one month to complete, plus another $3000 for onboarding consulting in the customer’s first three months. In that case, the customer will increase your revenue by $2000 in the first month ($1000 for the data migration and $1000 for consulting) and another $1000 in month two and three each. But since these revenues aren’t recurring, don’t include them in your MRR.

Note that this definition of “revenues” is what I believe is usually the right way to look at revenues at the management and board level whereas the numbers which your accountant will produce for your financial statements will likely look slightly different. The reasons are a couple of subtleties in the way software revenues are recognized based on US GAAP and other accounting standards, which brings me to...


US GAAP Revenues

Since I’m not an accountant and don’t even have an MBA we’re now entering territory which I’m not very familiar with, so proceed at your own risk. :) US GAAP Revenues means revenues in accordance with the “Generally Accepted Accounting Principles” adopted by the SEC. Your US GAAP revenues will usually be close to your revenues based on the definition I outlined above, but there can be some differences. For example, US GAAP revenue is typically calculated using a daily recognition model as opposed to the more practical monthly model. That means that if a customer signs up for a subscription at $100 per month on January 15th, according to US GAAP you should only recognize $50 of those $100 in January, despite the fact that that customer is adding $100 to your January MRR. Another difference is that as I’ve learned when doing some research for this article, apparently you may have to recognize things like implementation fees over the subscription period as opposed to the period in which the implementation service is being provided.

This topic is a science of its own, and if you’re interested you can read this 150 page manual from Deloitte about software revenue recognition, but the good news is that you don’t have to worry too much about it. Find a good accountant who understands SaaS and let him figure it out.


Bookings

I’ve seen several definitions of the term “bookings”. Broadly speaking, bookings are the total dollar value of all new contracts signed, but it’s not clear if the number should be annualized for contracts that are larger than one year, nor if non-recurring revenues should be included. Even worse, if your contracts have different subscription periods (e.g. one month and one year), the bookings number can be very ambiguous and misleading. I would therefore recommend to not use this metric and largely agree with the Bessemer Cloud Computing Law #2 which famously stated that “booking is for suckers”.


Billings

The term “billings” refers to the amount that you have invoiced and that is due for payment soon. If your ARPA is low and most customers pay you via credit card and/or your bigger customers usually pay you on or about the time of subscription or renewal, this metric isn’t important. If you agree on longer terms of payments with your customers, it can become important for cash flow planning purposes.


Committed MRR

Committed MRR or CMRR is a projection of your MRR in the next month or future months based on your current MRR, adjusted by guaranteed expansion MRR and anticipated churn MRR. SaaS companies sometimes have customers that start with a low price but have already agreed to a price increase in the future. CMRR is a great way to track and show this type of guaranteed expansion MRR. If you adopt the CMRR metric to show guaranteed expansion MRR, make sure that you also take into account “guaranteed churn” in order to make it consistent. That is, subtract MRR which you expect to lose from customers that you expect to stop using your software in the near future.


Closing thoughts

I believe that most SaaS companies do well by focusing on MRR and Cash Inflow plus, depending on the nature of the business, revenues and CMRR. The only thing I’d add is that if you’re selling annual subscriptions but you don’t get the full payment upfront (or similarly, if you’re for example selling 2-year-subscriptions but get only one year upfront), you should monitor your MRR broken down by contract length. That’s because there’s obviously value in selling longer subscriptions vs. shorter ones but that difference won’t show up in your MRR nor in your Cash Inflow in these cases. If you think there are any other revenue metrics that I’ve missed, please let me know.

Finally, I’m well aware that while all of these metrics are easy to understand conceptually, there are still a lot of devils in the details. The purpose of this post is to come to a common understanding of the key revenue metrics – how to deal with some of the many special cases that you’ll inevitably see (discounts, refunds, currency fluctuations, metered charges, etc) might be the topic of another post.

Announcing our investment in ChartMogul

The big guy who's lifting Nick is Michael Hansen,
Zendesk's first employee and a co-investor in ChartMogul

As reported by TechCrunch, we’ve led a seed round in ChartMogul. We’re thrilled about the investment. The decision to invest in ChartMogul, which has developed an analytics solution for subscription businesses, was a very easy one. Here’s why:

1) ChartMogul was founded by Nick Franklin, an early Zendesk employee. As employee #6, Nick has headed Zendesk’s activities in the EMEA region for two years before leading the company’s expansion into Asia for another (almost) three years. I knew that Nick has done a fantastic job at Zendesk and knew that he was an extremely entrepreneurial, hard-working, well-rounded, smart and nice guy. So when Nick told me a few months ago that he’s leaving Zendesk to start his own startup, I was sad for Zendesk but also very keen on learning more about his new gig.

2) ChartMogul is solving a problem which we at Point Nine know very well. We talk to SaaS startups on a daily basis, and almost all of them either have significant trouble getting comprehensive, accurate and consistent metrics or they had to make huge investments (especially into developer man-months) to get reasonably solid data.

When I put together my SaaS metrics dashboard almost two years ago, I drastically underestimated how difficult it is for companies to retrieve all of the relevant data. It sounds very easy in theory, but as we (and many SaaS founders) have painfully learned over the last years, in practice it’s very hard. I’ve heard from several SaaS founders that when they’ve found my SaaS dashboard template, they loved me for creating and open-sourcing the dashboard. But that love turned into hate when they found out, often over months, how hard it is to fill the template with real data. :-) The difficulties include getting and consistently matching data from multiple sources, dealing with complicated billing scenarios, addressing all kinds of exceptions and many more – I’ll let Nick follow-up with an in-depth post on that topic.

ChartMogul is solving that pain. You connect ChartMogul with your billing system (Stripe, Braintree, Chargify or Recurly) and at the click of a button, the product will show you almost any SaaS metric that you want to see, including the SaaS KPIs from my dashboard. But ChartMogul is not only a productized version my dashboard template. Since you can slice and dice all the data that you see on the screen, ChartMogul allows you to get many more insights. If you’re a SaaS company, go check it out!

3) We’re convinced that SaaS will continue to grow very fast throughout the decade and beyond, so the company is addressing a large and growing market. What’s more, while ChartMogul is initially focused on B2B SaaS companies, the solution is equally relevant for any kind of business with subscription revenue, which expands the company’s TAM even further.

So if you happen to provide a subscription service for “authentic T-shirts from the best bars”, curated items for nerds, emergency supplies or, well, dope, ChartMogul’s got you covered. ;-) (seriously - these services all exist, and many more)

Three more ways to look at cohort data

I've just added three new charts to my Excel template for cohort analysis.

The first one shows the MRR development of several customer cohorts over the cohorts' lifetime:



Each of the green lines represents a customer cohort. The x-axis shows the "lifetime month", so the dot at the end of the line at the bottom right, for example, represents the MRR of the January 2013 customer cohort (all customers who converted in January 2013) in their 9th month after converting.

Here are some of the things that you can see in this chart:

The second chart is based on exactly the same data but shows MRR for calendar months as opposed to cohort lifetime months, and it uses a slightly different visualization:

One of the things you can see here is the contribution of older cohorts to your current MRR (something to keep in mind if you're considering a price increase and are thinking about the impact of grandfathering):

The third chart shows cumulated revenues minus CACs for different customer cohorts, i.e. it shows how much revenues a customer cohort has generated less the costs that it took to acquire the cohort:

The purpose of this one is to show if you're getting better or worse with respect to one of the most important SaaS metrics: The CAC payback time, i.e. the time it takes until a customer becomes profitable. Note that for simplicity reasons the chart is based on revenues. If you use it in real life, it should be based on gross profits, i.e. revenues minus CoGS.

What you can see here is that the first cohorts cross the x-axis (a.k.a. become profitable) around the 6th lifetime month, whereas newer cohorts are crossing or can be expected to cross the x-axis further to the left, i.e. become profitable faster.

If you want to take a closer look, here's the latest version of the Excel template, which includes the new charts. Or even better, download it and pay with a tweet! :)

Measuring your SaaS success

I recently participated in Marco Montemagno's SuperSummit and gave a webinar about the topic "Measuring your SaaS success". Thanks, Marco, for inviting me!

Below are the slides of my talk. Since some of the slides aren't self-explanatory I've added some notes, see the yellow bubbles. If you want to dive in deeper, check out this post, which the talk was based on.

Measuring your SaaS success from Point Nine Capital

A KPI dashboard for early-stage SaaS startups – new and improved!

[Update 01/17/2015: There's a new company called ChartMogul (which we invested in) which makes it easy to get a real-time dashboard similar to the template below. Check it out!]

[Note: This post first appeared as a guest post on the blog of Totango. In case you don't know Totango, it is a powerful analytics product which gives online services the information they need to increase user engagement, conversion and retention. If you're a SaaS company you should check it out. Thanks to Guy Nirpaz and his team for publishing my post, which I am republishing here.]

In talking to a pretty large number of SaaS entrepreneurs in the last few years I've observed that there's a considerable amount of uncertainty around metrics: Which KPIs are the most important ones, what's the right way to calculate churn, CACs, MRR and other key metrics, how can I estimate customer lifetime value – these and other questions come up all the time, and the answers aren’t always obvious.

I've tried to address some of these questions in a couple of blog posts:

• 9 Worst Practices in SaaS Metrics
• Excel template for cohort analyses in SaaS
• The 8th DO for SaaS startups - Stay on top of your KPIs

I also put together a template which I thought SaaS startups could find useful and which also makes it easier for us as a VC to communicate what KPIs we're looking for when we talk to SaaS entrepreneurs. Needless to say a template can only be a starting point, as every SaaS startup is different and needs to build its own, customized dashboard. Nonetheless it seems like the template, first published in April of this year, struck a chord with many SaaS founders and investors: The blog post got more than 60,000 page views (which I assume is quite a lot for a niche topic on a VC blog, at least if you’re not Fred Wilson :-) ) and I get requests for the Excel file every day.

In the meantime I’ve put some more work into the dashboard. You can download it here. (And if you like it, tweet it!)

Here’s how the charts look like with some sample data in it:

Click for a larger version

The main improvement of this version is that it now includes different pricing tiers and annual plans. This makes the spreadsheet considerably larger, but I feel it's necessary if you have multiple pricing tiers and contract lengths, and you can collapse a lot of the rows to get a concise view of the top KPIs.

I hope you find it useful! If you have any questions, comments or suggestions, please feel free to email me at christoph@pointninecap.com.

I'm selling my SaaS dashboard and all it costs is a tweet

My financial planning sheet for SaaS startups, my KPI dashboard for SaaS startups and my "9 Horror Worst Practices in SaaS Metrics" slides got a fair bit of popularity lately and two hundred or so people emailed me and requested one of the original Excel files.

That brought me to the idea of selling them. But fear not, I don't want your money, all I want is a tweet. Or a Facebook post. (And if you really want to get one of the files without tweeting, drop me an email and I'll send it to you, although I can't guarantee that it's not bad for your karma.)

So...here are the "pay with a tweet" links:

Financial planning

Dashboard

Worst Practices

9 Worst Practices in SaaS Metrics

9 Horror Worst Practices in SaaS Metrics

As mentioned in my last post, I recently did a talk about SaaS metrics and I said I'm going to upload the slides. The slides don't contain a lot of text as they were not meant to stand on their own, but I've added a few additional notes to make them a bit more useful. 

You can view the presentation on SlideShare.

PS: Last week I held a session about the same topic at Seedcamp in London – thanks Philip and team for inviting me!

KPIs for VCs

Example for a Geckoboard KPI dashboard

Last week I spent a day in Stockholm to attend a metrics seminar organized by our friends at Creandum. It was a great event with talks from people of some of the best Internet companies from the Nordic region such as Spotify or Wrapp. Thanks Johan, Joel, Daniel, Frederic and everyone at Creandum for setting it up and inviting me!

I did a talk about SaaS metrics (I'll post the slides shortly), and in the Q&A session Andreas Ehn asked a really good question:

"As a VC, what are the most important KPIs for yourself?" 

Ultimately our #1 KPI is the return that we deliver to our LPs. If you're new to the world of venture capital, LP is short for "Limited Partner" and means the people and funds which have invested in our fund. That return is expressed as a return multiple or as the internal rate of return (IRR). As it obviously takes a lot of time to build (and eventually sell or IPO) great companies it will of course take many years until we know our final performance. Like most VCs our fund is set up for a lifetime of ten years.

In the meantime we (and other VCs) track our performance by:

1) Adjusting the value of our portfolio whenever a portfolio company raises a new round of financing from a new investor at a new (hopefully higher) valuation. While there's no guarantee that we will ever sell our shares at these "Fair Market Valuations" (FMVs), the assessment of the portfolio based on current FMVs is usually the best way to measure success. Valuations are usually marked up on an ad hoc basis internally (i.e. when a new round closes) and reported to LPs on a quarterly basis.

2) Monitoring our portfolio companies' key financial data, KPIs and operational performance. This is the best near-time proxy to long-term success, and so we're constantly looking at these things. We usually get either access to live dashboards or monthly reports and I'm hoping that we'll soon find the time to create a beautiful Geckoboard dashboard with the top KPIs across the entire portfolio (requires some work because we get data from portfolio companies in a variety of different forms and shapes).

Besides these pretty obvious ones there are a few other KPIs that we're looking at:

Number of deals that we're evaluating

It's not a KPI in the sense that there's a direct "the higher the number, the better it is" correlation, as quality of deal-flow is of course more important than quantity. But there is a connection between quantity and quality, and since we're using Zendesk to track each potential investment it's easy to monitor this number (for what it's worth, we're currently at deal #3,773 since we started using Zendesk about two years ago, and in the last 30 days 148 new ones have been added). 

Response time for investment inquiries

For founders it's important to get fast responses, even if the answer is "no". Depending on our workload sometimes we're fast and sometimes we're slow. There's still a lot of room for improvement, so this is a KPI that we're going to keep a closer eye on in the future.

"Rating" of our responses

Zendesk allows you to let your end users rate the customer support experience for every support ticket. We're not using this feature yet, but I'm wondering if we should do it in order to keep track of how successful we are in leaving positive impressions with the entrepreneurs that are pitching to us.

How well are we at picking the right investments?

Of all the potential investments that we look at, how well are we at picking the winners and avoiding the losers? And how well are we doing when it comes to allocating follow-on investments among our portfolio companies? We're not yet using a simple set of KPIs to track this, but we're regularly reviewing our past deal flow, trying to understand when we were right and when we were wrong and what we can learn from it.

Finally, there's one other KPI, and while it's again not something you can quantify on a short-term basis, it's just as important or even more important than our fund performance in the long run: It's the concept of Net Promoter Score applied to us. What it means is that when we ask our portfolio founders two simple questions – "Would you raise money from Point Nine in your next startup?" and "Would you recommend Point Nine to other founders?" – we want to hear two wholehearted YESes.

PS: Just like Web startups have their vanity metrics, you can also hear VC talk about vanity metrics – i.e. metrics which sound good but don't mean much. I'll leave that for another post.

A KPI dashboard for early-stage SaaS startups

[Update 12/20/2013: I have extended the dashboard to include multiple pricing tiers and annual subscription plans. Check it out here.]

[Update 01/17/2015: There's a new company called ChartMogul (which we invested in) which makes it easy to get a real-time dashboard similar to the template below. Check it out!]

Over the last few years I've helped quite a lot of SaaS startups to create or fine-tune their KPI dashboards. While every situation is a bit different there's also a lot of overlap, which made me think that it would make sense to publish my template (not without polishing it a bit). I hope other SaaS startups will find it useful, and it will also make it easier for us to communicate what KPIs we're looking for when we talk to SaaS entrepreneurs.

Not surprisingly the dashboard looks quite similar to the financial planning sheet that I've posted some time ago. Below are two Excel screenshots, and 

here is the Google Docs version.

If you prefer the Excel version, which looks a bit nicer, click here to download it. (And if you like it, tweet it!)

The sheet contains some notes on the right side. I was going to note a few additional things here but it's gotten really late here in Europe so I'll leave that for another day. If you find any bugs, let me know and I'll fix them tomorrow morning. :)

One comment, though. Although I've developed this sheet on my own, I've learned a lot about SaaS metrics from David Skok, who I am very thankful for. David created a SaaS dashboard as well, it's a bit more sophisticated and has a slightly different focus, but it's quite similar. Check it out, and if you have not read his brilliant articles about SaaS yet I highly recommend that you do so. They are an absolute must-read for every SaaS entrepreneur.

Like this post? Follow me on Twitter.

Know your user cohorts

One of the most important tools to better understand the usage of a web application – or a service, a game or a mobile app, it doesn't matter – is a cohort analysis. In fact, it's almost impossible to get a really good understanding of a service's usage without looking at activity and retention numbers on a cohort-by-cohort basis.

And yet, most startups that we're talking to haven't looked into cohort analyses yet. Often the reason is lack of resources. If you're a young, bootstrapped startup and you have to decide if you want to use your developers' scarce time to improve your product or to get better statistics most founders will decide for the product. That's understandable. Nonetheless I would like to argue for a high quality standard of metrics early on, since the insights that you'll get by understanding your metrics will often be highly actionable. And of course it will make your conversations with investors who want to understand your numbers much easier. At the minimum, I think you should try to make sure from the beginning that you collect the data that will allow you to do more sophisticated analyses later.

Back to the original point, why is a cohort analysis so crucial? Let's take a look at the following chart of an imaginary startup:



Looks like the company is growing nicely, hm? No exponential growth, but constant, linear growth. Now take a look at this chart:



It looks like the number of active users is growing even steeper. Great! 

But now let's take a look at the underlying cohort numbers in this Google Sheet.

The number of new signups are contained in cells D5 to D14, and the cumulated number of signups are in cells E5 to E14 (I used that one to make the chart look better :-) ). The number of active users, which the second chart shows, is contained in cells H15 to Q15.

In case you're not familiar with cohort analyses, here's a quick introduction:

• Each row represents a signup cohort.
• In the "right-aligned" cohort analysis at the top you can see the number of active users of each signup cohort for every calendar month. So, for example, I5 is the number of users who signed up in January 2011 and were active in February 2011, and I6 is the number of users who signed up in February 2011 and were active in February 2011. Accordingly, if you go down to the "Total" numbers in row 15 you'll see the total number of active users for each calendar month. These are the numbers which form the activity chart above.
• In the "left-aligned" cohort analysis at the bottom you can see the number of active users of each signup cohort for every user lifetime month. Example: I20 shows the number of users who signed up in February 2011 and were active in March 2011 (=user lifetime month #2 of the February 2011 cohort).

Row 29 and 30 calculate the monthly drop-off rate and the percentage of users who is still active n months after signing up. Here's where it gets really interesting. Our imaginary startup has a monthly drop-off rate of 50%, which means that after 6 months only 4% of the users are still active! That's not easy to see if you're just looking at the charts above, is it?

Note: In the example that I'm using, a user who registers in month x qualifies as an active user in that month. The assumption is that he logs in at least once after registration and that that log-in makes him count as an active user. That effect completely distorts the real activity numbers. If you're signing up a growing number of users it means that your activity numbers can basically only go up regardless of any real usage activity. So - if you're talking about "active users" it's best to leave out the users who have signed up in the timeframe that you're talking about. That is, if you're talking about the number of active users from last week, include only the users who signed up until the week before.

By the way, while I've used "activity" in this example you can of course use cohort analyses to track other aspects, too. As a SaaS company, for example, you should have a cohort analysis for retention/churn. As an online shop, you should have a cohort analysis for repeat purchases.

Avoiding Parkinson's Law of Triviality in your financial plan

In the last few years I've seen a lot of financial plans, and since we started Point Nine in the middle of last year that volume has been skyrocketing. I've seen everything from just a few numbers in an email to extremely sophisticated Excel spreadsheets with dozens of tabs and tens of thousands of cells, and I thought I'd offer some advice on what I think a good financial plan looks like.

To begin with, among the worst financial plans are those that you get if you take a template from a business plan competition or a bank in Germany and don't customize it to your particular business. These templates are usually very detailed on the costs side, listing everything from magazine subscriptions to stationary and postage, but the revenue projection is just one line – a pure estimate that is coming out of nowhere. Parkinson's Law of Triviality comes to mind!

The best financial plans of early-stage Internet startups in my opinion:

• are relatively simple – just one Excel tab or a few at most (a later-stage company will often require a more complex plan but in the beginning you can keep it simple)
• are based on the key drivers of your business (your conversion funnel, your projected ARPU, churn etc.)
• make your assumptions transparent and easy to change
• contain very few hard-coded numbers which would make the plan hard to revise (an exception to this are historic numbers, of course)
• avoid Parkinson's Law of Triviality – spend more effort on what really matters and lump together stuff like tiny expense categories
• contain a few extra lines for sanity checks (anyone who will seriously review your plan will perform them anyway, so why not make their lives a little easier?)

If anyone is interested in further details, please let me know in the comments section, email me or send me a tweet and I can add some more color and post an example.