Chapter 8
Finance: Cash is King
Chapter Contributor(s)
John Harvie, former CFO SC Ventures
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The key to surviving is having a flexible model.
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Too much information can be dangerous
Everybody hates people who change their minds, but that’s a fact of business, especially when the business model evolves from something that looks like a back-of-the-envelope calculation into a strategy for world domination. Plus, managing future estimates can be challenging – more so if the alpha and beta tests of the startup don’t validate the business model. Meanwhile, the key to surviving is having a flexible model. For our ventures, this meant embedding scenario-based thinking in our modelling to help ‘flex’ assumptions as the world around us changed.
Writing about accounting is always going to be a challenge. But doing accounting in SC Ventures was more than that: it was an adventure. Every day, there was something new; there was stuff I hadn’t even practised since I qualified. That was when the realisation hit me: being in the banking world was like living in a bubble. The real world was different, and bridging that divide was the challenge; it wasn’t going to be an easy ride.
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Banking is like living in a bubble. The real world is something different. Bridging that divide is the challenge.
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What was expected of the CFO in such a setting? Essentially, anything with a number landed on my desk. There were profit and loss statements, balance sheets and invoices; I was helping to set up the payroll, applying for bank accounts, valuing unlisted private equity, managing Basel requirements, assessing which accounting software to use, and issuing preference shares in India. Other questions that came my way: what are the accounting requirements for crypto? Do I need a nodal account? Are escrow accounts caught by specific legislation? What is a PFLP (private fund limited partnership)? What is an ESOP (employee stock ownership plan)? Oh, and could I open a cost centre?
The outward perception might have been that it was Amateur Night at the Apollo. The reality was that we were in the business of experimentation. We weren’t the first to do this: every major bank had an accelerator and some a venture building arm, and several even had their own fund. But very few had all three under one roof, giving us the unique opportunity to find new tools to track, monitor and report – increasingly systematically – on each of these efforts. Along the way, there was a series of lessons, which I will explain in detail in this chapter.
Avoiding analysis paralysis
We focused on simple data in two categories: ‘people’ and ‘things’. These included:
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Metrics (only those that would make a difference)
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How much cash we had, and how much we had spent (and, ideally on what)
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How many people we had, and how much they cost per month
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What other outgoing items we had, and how long we were committed for
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The run rate and its pattern – was it, flat, or getting worse?
Let’s be frank: most people are number blind, and most users of financial or numerical information are either uninterested or find it poorly presented. We learnt that, more often than not, the solution to this was to draw a picture. It sounds simple, but boiling down complex issues into digestible chunks is an art. We often used the following methods:
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The waterfall model: Used by investor relations departments the world over, this method creates a ‘bridge’ between one number and another, with slices of explanations in movement.
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Materiality: We applied the 80:20 rule, focusing only on the numbers that made a difference.
Keeping a close eye on the burn
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(The average cost per employee) x (the number of people) offers a guide to the run rate.
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Of all financial metrics, cash burn is the single most important one for a startup. Without cash, it can’t pay its bills or its staff. Accurate forecasts of the burn rate, through the use of the ‘run rate’, are critical to making sure that the available cash lasts. Early in the venture life cycle, people are the largest running cost: the more staff, the higher the cost. This number includes their salary, medical insurance, employer taxes, payroll processing, bonus payments, and so on. By implication, the average cost per employee, multiplied by the number of people, offered us a good guide to the run rate.
Aside from the spending on people, there was also spending on ‘things’, including third-party vendors and suppliers (important in a startup context). The trick was making sure to monitor that we got what we paid for. If you had work done on your house, and it wasn’t finished, you wouldn’t pay. The same applies here.
Paperwork and processes matter
When a venture is heading towards an alpha or beta launch, the devil is in the details. Our details were endless: How would the venture get paid, and in which currency? Where would the money go? What about GST? What are the processes, and what’s the risk involved?
Having all the details from the start is probably unlikely, but by the time the beta launch happens, most, if not all, of the answers to these questions should have been fleshed out, factored in and, ideally, automated. Having any kind of ‘manual workaround’ is fine for an alpha or even beta launch, but full-scale production must be automated otherwise the venture won’t scale. And scaling requires a well-thought-out process and all the artefacts that go with it. When you log on to Amazon or your online bank account, they might look slick on the front end, but how well do they work on the back end? The litmus test we used was to consider if someone buying into your venture looked ‘under the hood’, what would they find? Everyone assumes their idea is great, but the reality is that most deals get passed over because potential investors see beyond the ‘gloss’. And if the paperwork and processes aren’t articulated well in a data room, your venture isn’t going to go far.
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If the paperwork and processes aren’t articulated well in a data room, your venture isn’t going to go far.
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Getting the model right
While technology might play a remarkable role in reshaping modern life, only a sustainable business model – one made to last on solid foundations and sound financial modelling – can help to achieve that.
Financial model building is an art that few can execute well. Simple models have dates: ideally in months for the first six months, then in quarters, with the outer years being no more than five years at maximum. Down the side are the ‘drivers’: customers that drive revenue; people and things that drive cost; and underlying metrics such as customer acquisition cost, minimum viable product build, costs to launch, and so on. Phasing these into a time frame allows a financial ‘story’ to be built to allow challenges. Revenues have to cover costs at some stage; without a defined and defensible plan, any venture is, at best, a bet. Having to apply the discipline of getting the model right gave our ventures a chance to ‘pause’ and sense-check the environment.
TAKEAWAYS
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Avoid analysis paralysis and only track the metrics you need.
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Keep an eye on the burn and the biggest burn for any startup will be its staff.
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Attend to paperwork and processes so that you have the answers for investors.
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Get the model right so you can sense check what’s happening.