Finding what works for *your* startup
Pride only hurts, it never helps. - Marsellus Wallace
Finding what works for your business means that things will blow up, and that’s ok - let go of your pride and trust your instincts.
This post is about learning to forget the modern startup paradigm and reverting back to the core. It’s about sharing simple ideas that have helped some of the best entrepreneurs I know.
Define your own metrics
This one sounds obvious, but it’s actually hard to do in practice given the amount of noise out there.
For instance, the notion that “LTV/CAC should be >3x” tends to be meaningless for early stage startups since most of them do not yet have data showing what lifetime actually looks like. A better approach is to use CAC Payback models like this one and come up with your own retention profile by looking at empirically observed cohort data.
The insight here is this: next time you catch yourself trying to figure out what the definition of a metric is by searching online, try instead to take a blank page and write down what that metric means *for your business* - if an equation doesn’t make sense, then just change it to something that does.
Having a written record of important definitions will make you feel better and more importantly it will create clarity your investors and team will love. Remember that in the land of extreme uncertainty, there are no objective metrics, it’s about finding the one that works for you.
From systems of record to systems of engagement
This makes intuitive sense - value delivered to customers is best measured by how intensely they engage with a product - not by how much they spend on it.
Now why does this make sense? Because the frontier that once existed between employees and consumers is no more… Consumerization of business software is real and there is no turning back.
So how do you measure engagement? The first step is to define *THE* metric that best captures how your customers engage with your product - aka your North Star (eg. DAU/MAU, # of action taken in a day, etc.). A blank sheet may be needed here too ;)
Product-market fit wonderland
Massive waves in tech have recently challenged the ways in which products are developed and brought to market. In particular, token and AI enabled-startups have attracted an unprecedented level of momentum, but sometimes with little evidence that they can solve a valuable problem.
The danger in hype cycles like these ones is to fall in love with technology as opposed to obsessing on customers - the latter is still very much relevant today despite being one of the oldest business adages.
In the case of tokens, the ICO mania didn’t alter our fascination vis-à-vis the power of decentralization in society. At the same time, we still firmly believe in the immutable laws of product-market fit - *tokens need to fill a market need to be sustainable*. Very few have articulated the need for token as well as Kik did for Kin. In short, prior to launching Kin, Kik experimented with Kik Points to support its hypothesis that an audience for a chat-based economy actually existed inside Kik - and that this market could be addressed with a token like Kin. Note that Kik Points monthly transaction volume was a wowzing 3x that of BTC, on average:
Similarly, we’re excited to see AI becoming an ingredient of all modern software. We keep finding (and backing) founders who go vertical and develop a product edge with open sourced technologies, as opposed to focusing purely on the bleeding edge. Here again, successful founders embrace the same philosophy to achieve product-market fit - by focusing on a valuable problem and then layering on AI into their product (as opposed to starting with a technology that’s looking for a problem to solve).
There is a fine line here since customers may not actually know what they want/will want. The trick then becomes to focus on customer pains:
Listening to customers’ feature requests leads to an existing product (or paradigm), while listening to their pains helps shape new products (or shift paradigms).
Capital allocation premium
Another common characteristic we’ve seen across top performing management teams is their ability to conduct experiments and evaluate outcomes against measurable KPIs - at an increasingly high frequency.
In other words, deploying capital and constantly optimizing its allocation is a key determinant of success for startups - the earlier a team finds its groove at this the better. Looking broadly at businesses in North America over a long period of time shows that active capital allocation overperforms relative to passive capital allocation (see below).
This trend is especially true for sales and marketing, which is *THE* most important area of investment at an early stage - often representing more than 50% of total expenses. As laid out above, CAC Payback models are our go-to frameworks to help support decisions around when to step on the gas (or brake) with sales and marketing dollars. The Magic Number is also interesting since it better lends itself to benchmarking. A Magic Number > 0.7 is healthy. How efficient are *your* sales and marketing efforts?
I keep being amazed by the quality of people and depth of knowledge within the Inovia community. One of my main area of focus in the coming months will be to accelerate how learnings are transferred from public companies to growth stage startups, and from growth stage to early stage ones.
More on this coming up, stay tuned!! 💥🚀
