This was a challenge over the last week or two, but came to an end this week, so a good time to blog about it.
A huge focus on startups are Unit Economics – how much do you spend to acquire and retain a unit (of customers – so 1 customer) and more interestingly what is your return on that investment (ROI). The theory is covered in any business course in university to calculate return on investment which is profit over cost, but what is disappointing is that this does not at all compare to what really happens in real life scenarios.
How do you solve complexities around repeat projects, allocation of retention costs across segments and months which did not have any repeat projects, as well as acquisition costs?
Do you calculate monthly cohorts, half yearly or do you calculate monthly cohorts and average them?
How do you accurately calculate unit economics for the company, when you already have a segment view? (weighted average or just a company UE?)
Long story short our investors had a super sophisticated and painfully manual model which calculated the Unit Economics for us and I was meant to update it. It took me about half a day of updating to realise that this will take a couple of days to just update as the model was so large and even worse, hugely prone to human error.
This is when I went away and it took me a day to replicate the model (learn all its ins and outs along the way) and fully automate it so the next time we just need to do a data pull and copy and paste, which should take 30 mins to update.
This was the first part of the journey.
Even more interestingly was the next exercise of trying to come up with a Unit Economics for our Forecast. This was a real life brain teaser (unlike the management consulting case studies), which opened up a whole new layer of complexities around the correct way to forecast repeat projects and more importantly allocating repeat costs across them.
In sum, it’s a huge financial model with a billion different levers and an informative and insightful outcome.