After five years as a director at Vend, yesterday was my final meeting.

I’ve written here about a number of milestones in the Vend journey to date: from the bike ride where I first got the pitch, the announcement of our first investment round, Vaughan describing the early history in his own words, notes from our recent visits to San Francisco, watching the team expand, and more recently the search for a “real” board.

So, I thought one final post would be appropriate.

It’s been a privilege to be part of the team from the beginning. It doesn’t feel like that long ago we were putting together cheeky advertisements and videos and hoping that people would notice.

A billboard concept. Probably better this never saw the light of day…

POS Advert

Steve, the potty mouthed, bigoted and small minded, crappy old 1980s cash register, finally meets his fate…

An early recruitment video…

Thankfully, people did notice. Suddenly we had retailers from all around the world using our software to run their business. And more and more of them over time!

As the team expanded, it was great to be able to spend some time in Auckland, Melbourne, San Francisco and Toronto with the local people who are working hard to make Vend a success.

We have also been joined along the way by some of the very best SaaS investors – from here in New Zealand and from Germany, Australia and the United States.

When you look back on this sort of thing it always seems like time is short. So, it’s easy, generally, to be motivated by negative FOMO – the fear of missing out. But, in this case, my overwhelming memory will be TOBI – the thrill of being involved. Thanks to Vaughan and the team for having me along for the ride so far. And, especially to Miki, Barry and (too briefly) Claudia and Sarah for your support as part of the board over the last couple of years.

Vend is in an exciting period as the team works to put the business on the right track for the next phase of growth. To everybody involved now, I remain an enthusiastic investor and will watch with interest to see where you can take it from here. You have a huge opportunity to be part of something special, and I know you’ll make the most of it!

Estimating Tenure

The key number that Dr SaaS needs to calculate in order to diagnose your SaaS business model is average tenure, or, if you prefer, the life expectancy of a new customer when they subscribe to your service.

To calculate the lifetime value of a customer (LTV) you just need to know how much you earn on average from them each month (ARPU), how much it costs you on average to acquire a new customer (CAC), how much it costs you on average to service a new customer (CTS) and how long you can expect them to remain a customer (Tenure). ARPU, CAC and CTS are all easily derived from your financial statements. But, tenure is not so straight forward.

The way this is typically calculated is using the current churn level, which is the rate at which existing customers currently cancel:

Churn = Cancelled Subscriptions / Total Subscribers

For example, if you have 500 subscribers and 12 cancel during the month, then your churn rate is 2.4% per month.


Tenure = 1 / Churn

For example, if your churn rate is 2.4% then your average tenure using this formula is just under 42 months (or 3.5 years).

This sort of makes sense: if you have 12 cancellations every month then after 42 months all 500 subscribers will have cancelled.

However, in practice this approach often ends up over estimating the actual tenure, as Jason Cohen (aka A Smart Bear) explains in his great blog post on this topic:

“It’s impossible to see ahead to timeframes beyond a few years for a young company and perhaps 4-6 years for a mature one. In those timescales you expect drastic changes in market conditions — a strong new competitor appears or dies, the economy slumps or soars, a disruptive technology changes the landscape, etc.

That in turn will cause material changes in pricing, retention rates, reorganized customer segmentation, usage levels, service levels, and so forth.

Computing expected months with the ad infinitum approach leads you to over-estimate the total revenue you can depend on.”

This skew is especially evident if your churn rate is low as the inverse value increases rapidly at this point:

Alternative Tenure Calculations

So, what to do about this? Jason suggests two alternative approaches – either capping the number of months or using a discount rate.

Tenure = 1 / (Churn + Discount)

This option does product lower estimates of churn when the churn rate is low (and as he points out who cares about what it does if the churn rate is very high, as in that case you likely have much greater problems to deal with than what tenure formula to use!)

Alternative Tenure Calculations

In Dr SaaS we use a third option which gives values somewhere between these two extremes when the churn rate is less than 2%, but lower values when the churn rate is higher.

Tenure = ln(0.5) / ln(1 – Churn)

While this looks complicated, it’s just using the churn rate to work out how many months it takes before half of the new customers in a given cohort have cancelled.

Consider the example above, where tenure is estimated to be 29 months (compared to 42 months for the simple formula):

Month 0: We start with 500 subscribers
Month 1: 500 * 2.4% = 12 cancellations, leaving 488 subscribers
Month 2: 488 * 2.4% = 12 cancellations, leaving 476 subscribers
Month 3: 476 * 2.4% = 11 cancellations, leaving 465 subscribers
Month 4: 465 * 2.4% = 11 cancellations, leaving 454 subscribers

Month 28: 259 * 2.4% = 6 cancellations, leaving 253 subscribers
Month 29: 253 * 2.4% = 6 cancellations, leaving 247 subscribers

At this point, 50% of our original 500 subscribers remain, so we take this as our average tenure.

You can see how this looks on the graph:

Alternative Tenure Calculations

If you want to try this out with your own subscriber numbers, check out Dr SaaS:

What do you think?

How are you currently calculating tenure? How does it change your results if you use this formula instead?

We’re interested to hear any suggestions.

Dr SaaS Demo

I want to walk through an example of getting a diagnosis for Dr SaaS, to show how easily you can do this for your SaaS business, and what sort of analysis you can expect to get out of it.

To get some numbers to use for this, I’m using the Demo Company which is available in Xero (if you’d like to play along at home, log into Xero and look at the bottom of your My Xero page for a link to your own copy of the Demo Company).

For the purposes of this demo I’ve changed two of the accounts to better match the sort of expenses you might have in a small SaaS business: “Subscriptions” is now “Staff Costs” and “Rent” is now “Hosting”, otherwise the data is straight out of the box.

Step 0: Getting Started

Visit and click the big orange button on the homepage:


Enter your name and email address, and you’re underway:


Enter your company name and select the period you wish to analyse. In this case I’m going to use the details for the last quarter ending Sept ’14:


Step 1: Revenue

This step collects details about subscribers and revenue, and calculates growth, churn and average revenue values.

We will need to make up the subscriber numbers, as there are no details about this in Xero. When you’re doing this yourself, you can get your subscriber numbers from your customer database or billing system.

Firstly, enter the number of subscribers you have at the end of the period. This should include both free and paid subscribers, if you have both. We will say 598 subscribers at 30 Sept.

Secondly, enter the number of new subscriber you acquired during the period. This should count every new subscriber who joined during this time, even if they have already cancelled. We will say 186 new subscribers between 1 July and 30 Sept.

Thirdly, enter the number of cancellations during the period. We will say 98 cancellations between 1 July and 30 Sept.

At this point we already have our first metrics calculated, showing the growth rate, churn rate and average tenure:


Finally, enter the amount of revenue received during the period. To get this you need to open Xero and run the Profit & Loss report:


The revenue number is shown at the top. If you have other revenue you should only count recurring revenue from subscribers at this stage (other revenue will be entered separately in Step 3 below).

In our example, there are sales of $13,733 showing in the P&L, so we enter that value here.

This then calculates the fourth metric on this page, the ARPU or average revenue per user. This is the amount you earn on average from each subscriber each month:


Already we have some useful information – the growth rate is 4.91% per month, which is okay, but churn is 6.41% which is very high and means that on average each new subscriber only sticks around for just under 10.5 months, on average we earn $8.05 per subscriber per month, which doesn’t leave a lot to play with.

Step 2: Profit

This step collects details about how much we spend acquiring and servicing subscribers, and calculates our cost of acquisition, cost to serve and also profit margin.

Before you can complete these inputs you need to split the various costs showing on the P&L report into three categories:

  1. Acquisition costs – i.e. all sales and marketing costs, commissions, discounts and related staff costs.
  2. Service costs – i.e. all support and hosting costs, payment processing and related staff costs.
  3. Operating costs – i.e. everything else! (note: we will enter these costs in Step 3 below)

In our example, we will treat “Advertising”, “Entertainment” and “Printing & Stationary” as acquisition costs, and “Hosting” and “Telephone & Internet” as service costs. We will also split the “Staff Costs” into thirds and say one third of this amount is spent on acquisition and one third on servicing subscribers. It can help to get out your highlighter at this point, to keep track of which expenses you have entered:


Those add up to $2209 and $1531 respectively, so we enter those values here:


You can see from the bubbles at the bottom that we’re currently spending $11.88 to acquire each new customer and 90c per month to service them, which leaves a profit margin of 74%, which isn’t bad. But, again, the problematic value which is highlighted is the cost of churn, which is the effective amount spent each month to overcome churn – i.e. we currently spend 9.45% of all of the revenue we earn each month to acquire new subscribers to replace those subscribers who churned.

Step 3: Runway

This final step collects the remaining details about expenses, and also how much cash is available to cover these expenses, to calculate a burn rate, breakeven target and cash runway.

Firstly, we enter the details of other revenue and other expenses not already included above:


From this we can see that the calculated breakeven target is 118. This shows how many subscribers are required in order to cover all of the operating costs and other costs, given the current business model. In our example, this is good news as we already have more subscribers than this.

However, we have not yet considered the other costs which are not included in the P&L, such as capital expenses, capital raising costs, and tax as well as foreign exchange movements. To get these we will need to look at the Cash Summary report in Xero, which shows that during the period we spend $1652 on computer equipment, so we enter that here.

Finally, we need to look at the cash on hand in the Balance Sheet report in Xero.

In our example, the bank account has $16,666 at 30 Sept:


At this point we get the calculated burn rate and runway. In our example the burn is negative, so the runway is effectively infinite, but assuming you’re not yet making a profit these values will show how much cash you burn each month and how many months you have left assuming current burn rates.

Step 4: Diagnosis

That’s it. We’re done!

The final page shows the summary diagnosis:


This graph can be a bit confusing at first, but is actually simple. It shows the lifetime value per subscriber.

The green revenue bar, on the left, shows the total amount of revenue you can expect to earn from a new subscriber on average. In our example, this is $84.29. The value is based on the average tenure of a new subscriber (derived from churn rates) and the average revenue per subscriber.

The two red bars in the middle show the cost to acquire and cost to serve, again over the lifetime of the subscriber. In our example these are $11.88 and $9.40 respectively.

The bar on the right shows the remaining gross profit – i.e. revenue less costs. If you have a profitable business model then this bar will be green, as in the example above. Otherwise this will be red, meaning you are effectively losing money on every new subscriber!

Below this is a brief description of your unit economics. This will be customised to the values you have entered:


The second part of the diagnosis highlights specific metrics which are relevant to your situation. In our example, it shows the gross profit value of $63.02 and growth rate of 4.91%, which are both positive, and also highlights the high churn rate of 6.41%. Again, the metrics that are displayed are different for each company, depending on the values that have been entered and the things you should be focussing on.

From this page you can choose to save the diagnosis, which allows you to share details with others on your team, enter multiple months to keep track of the full history, and setup a goal for one of your metrics that needs focus. You can also enter values for multiple businesses and switch between them easily – great for people like me who spread their time across multiple companies.

Please, try it out for yourself now:

We’d love to hear how you get on.



Magic Dust

Realising that nobody else knows the secret formula for your venture is an important and valuable thing to learn.

Sadly, I don’t have any magic dust that I can sprinkle on any given start-up to make it successful. Nor does any potential advisor, mentor or investor, even if they promise otherwise. Not even if they have worked on something successful in the past – indeed, this often seems to create a bias, where we incorrectly assume the things we did will also be relevant to your situation.

To have any chance of contributing much of value, I would need to dig-in for a long period of time, and really get to know you and your business, and to understand your current circumstances and constraints.

This will take longer than one coffee meeting (but, thank you for the invitation).

If you think you need some help with the thing you’re starting, but don’t even know what that is, look for somebody who knows you, believes in what you’re doing and is prepared to work with you for years to make it happen. And, before you even ask them, make sure that’s true for you too.

Or, if you think there is something specific I can do to help you, please ask.

Track Record

Early-stage investment is a dark art.

Very few investors are forthcoming about their results, and so there is a lot of misunderstanding about what is “normal”.

The common belief seems to be that you just need to invest in ten companies and one of them will be a winner. I actually don’t think those sort of results are typical. My observation is that most investors have a selection bias which skews their returns significantly in one direction or another.

Either way, I thought it might be helpful to others to consider my own experience to date.

My first early-stage investment post Trade Me was Xero, in March 2007. It was a relatively easy decision, as I was also joining the team ahead of the IPO later that year. With the benefit of hindsight, I probably should have stopped after that, and I could now claim a 100% hit rate!

Instead, in the seven years since then I’ve made fifteen further direct investments. I’ve also made follow-on investments in six of these.

In total I have invested just over $4m so far.

This is the list in chronological order 1:

Sonar6 is the only real exit to date, after they were acquired by Cornerstone On Demand in April 2012. As an investor in their later rounds, that represented a ~2x return for me.

Four are sold or confirmed dead (in a couple of those cases there were very small amounts returned to investors, but only cents on the dollar so more of a gesture than something that should be considered a return) and one other is missing presumed dead, so 31% by count or circa 16% by value invested have resulted in a loss of the money I invested. That compares favourably with the 40% figure that Fred Wilson has written about although, as he says, that’s maybe just a signal that I’m not taking enough risks.

Thankfully, I’ve invested larger amounts in the companies which have been successful thus far. This is partly good luck and partly, I think, a function of the approach I take – typically investing a small amount at a reasonable valuation initially and then more later, once the company has proven they can execute and start to scale and as a result have significantly reduced the risk.

Xero is clearly the biggest success on that list at this point. The small portion I’ve sold has repaid my initial investment several times over, and the majority that I retain is worth more than all of the others combined, even after the recent share price correction. I remain very long and patient.

I’m also very optimistic about Vend and Timely, which take a large chunk of my time today. I am chairman at Vend and a director at Timely.

Atomic, Revert and Respondly are three recent investments, so far too soon to tell, but I’m very excited to be working with all of them too.

And, the jury is out on a few of the others – I’m hopeful there may be a couple more winners in that list too.

Overall, the portfolio doesn’t owe me anything at this point.

On top of that I’ve also gained some useful scars from the early failures. This is what I mean when I say get started and be prepared to suck for a bit. I suspect that is actually the only option, if this is something you’d like to be good at.

Some of the biggest lessons, so far:

I’ve learned it’s better to invest in the best companies, not the most companies. As the list gets longer I feel less and less inclined to do more and more.

I’ve learned that investing passively from the sidelines doesn’t provide much personal satisfaction, even if it goes well for the company, so my recent investments have been in ventures where there is an opportunity to get involved in some capacity in addition to investing cash 2. The downside with this approach is that time is finite. At the moment I have my hands full to overflowing with the things I’m already invested in and working on, so I’m focussed on those rather than looking to add anything new into the mix (and, as I have to constantly remind myself, focus means saying no or, aspirationally, having an assistant to say no on your behalf).

I’ve learned that you can achieve a lot more if you’re not worried all the time about who gets the credit, but if you take that approach you have to assume that you won’t get the credit and be comfortable with that.

I’ve learned that as a potential investor aimless networking, coffee meetings and events are a black hole, and you can spend all your time there, but it doesn’t really help you to find the best investments or contribute much of value to the companies you’re already working with.

Time will tell how this goes.

It will be interesting to look back at this list in another seven years and see whether the companies that I think are the possible winners now actually worked out that way, or if there were any surprises. And, who knows if there will be more names to add to the list which at the moment are nothing more than an idea.

I continue to make it up as I go. No doubt there are many more lessons to come.

Perhaps this post will prompt other local early-stage investors to talk more openly about their own results and experiences too. If so, please add a link in the comments below.



  1. There are a few things excluded from this list, where my investment was a token amount. I have excluded the projects I’ve worked on with the team at Southgate Labs, such as Triage. They are not material to the calculation because my investment there has been predominantly time rather than cash. I’m also a small investor in the latest Movac fund, however it would be generous to count any of their investments as mine in this context so they are also excluded.
  2. In practical terms this means spending a day or so each month working with the founders on whatever they need help with, and being available in between those times via video or email to answer questions or be a sounding board – usually for things like strategy, planning, finance, legal and recruitment (or the opposite!) Ironically, these days, less and less of my time is spent on product or software development, even though being good at that was what earned me the right to be in this position in the first place, although I’ve typically invested in ventures where those skills are well represented in the team already.