Many a startup has been onto a fantastic opportunity, but lost runway by simply focusing on the wrong opportunities or optimizing the wrong aspects of their product. In the early days, you have low sample sizes and an immature understanding of your market — and ultimately, that’s the environment where great entrepreneurs are made.
Ultimately, there’s an antidote to the madness of early stage startups: metrics.
A strict understanding and adherence of a select few startup metrics will make navigating the early waters of product market fit significantly easier. There are essentially 3 metrics you need to care about in the early days: CAC, ROI, LTV.
The most important number in your early days is this: how much $ does it cost you to acquire a single paying customer?
The simplest definition of product market fit says that you can acquire customers for less money than they make you in the long run — if it costs more money to acquire a customer than they actually bring to your business, you’re going to run out of money pretty quickly.
Well — not exactly. In simple PPC cases — for instance, running a single Facebook ad and measuring your signup rate, acquisition costs are easy to figure out. It becomes a lot more complicated when you start layering on multiple campaigns — especially when those campaigns aren’t as straightforward as digital ads.
For example, what if you’re running case studies on your blog, video interviews on YouTube, and a mix of Facebook & Google ads to drive traffic? How do you know your CAC? You need to have extremely solid analytics set up, and solid attribution down your funnel.
There are essentially 2 CAC calculations that are essential to know: your channel-specific CAC and your overall CAC.
Both of these are important, for different reasons. Channel-specific CAC is simply your customer acquisition costs for each channel that you’re trying out. Overall CAC is your overall net acquisition cost across all viable channels.
Now you’re probably wondering — shouldn’t overall CAC be the same as channel-specific CAC? Shouldn’t we remove all but the cheapest acquisition channels?
In theory this makes sense, but in practise it’s flawed because CAC doesn’t take volume into consideration. Here’s an example — let’s say you spend $250 each in Reddit ads and Facebook ads. Reddit spends the whole $250 in 1 week to get you 15 customers (CAC of $17), whereas Facebook spends the whole $250 in 1 week to get you 10 customers (CAC of $25) — in this scenario, you should eliminate Facebook and put all $500 into Reddit ads, so your overall CAC is also $17.
However, what if Reddit was only able to spend $125 of the $250 you allotted to get you 7 customers? You still have a CAC of $17, but there’s a crucial issue here — Reddit isn’t reaching a big enough market to max out your ad spend. In this case, it doesn’t make sense to put all $500 into Reddit ads because it’ll be months before you’ll get a high volume of customers — instead, you’ll have to eat a higher overall CAC of $21 in order to get a high volume of customers and get the most out of your ad spend.
In other words, your overall CAC has to factor in time — a very cheap channel isn’t useful if it takes an inordinate amount of time to get a small amount of customers.
Ultimately, knowing how much it costs to bring in your customers is meaningless if you don’t know how much money those customers bring to your business — whether it costs $5 or $5000 to acquire a customer is irrelevant without knowing how much money that customer will bring to you over their lifetime.
Lifetime value is an average calculation of how much money each customer brings you over the lifetime of their usage. This can be calculated from everything from a subscription product (from the day the average user signs up to the day they leave), to an ecommerce company (total value of products purchased over a meaningful customer lifetime).
There are various ways to calculate LTV, but the simplest is to just get historical data on the total $$ earned from each customer and average it out. It’s essential to break this down by channel, so you can figure out which channels are profitable — relying on CAC alone is not enough, because different channels will bring customers of different quality and LTV.
Simply put, your ROI is your LTV divided by your CAC — once you know how much it costs to acquire a customer, and you know how much that customer brings you over the duration of their time with you, you can figure out how much money each dollar of ad spend is bringing in as profit.
A crucial mistake growth people at early startups make is treating CAC as the be all end all without factoring in tertiary costs in their model. There are several costs associated with advertising that aren’t factored into CAC — for example, the price of tools you’re using, salaries for your marketing team/contractors, etc.
The common startup mantra is to ignore these costs when calculating viability because they disappear at scale.
In theory this makes sense, but again, in practise it’s flawed — most SaaS tools have variable pricing models which can run extremely expensive with higher usage rates. Contractor & staffing costs for marketing teams also tend to scale linearly — and the bigger the marketing team, the less efficient it is.
This why a ROAS (return on ad spend) of 2–3x is recommended — it’s not enough to just break even on your CAC, because your actual ROI will be negative due to the incidental costs. Instead, it’s essential to give yourself a large buffer on your acquisition costs to account for all other tertiary costs associated with advertising.
There’s a whirlwind of different metrics out there you can focus on, but ultimately these 3 are the linchpin of a good startup operation. It’s essential to put resources into making sure all of these numbers are tracked properly and regularly, so you can watch your ROI rise over time — a hallmark of a strong company is one which becomes more profitable over time.
When all 3 numbers are synergized, you’ll know you’ve found product market fit, and have a very harmonious framework through which to judge future growth experiments.
Originally posted on Medium.
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