Why and how to focus on progress rather than traction (Oversubscribed Weekly #43)

October 21, 2019 by Mike

Mike here. I’m a little late on this issue so this is going out on a Monday (happy Monday!).

Recently, I’ve been spending time speaking with startup studios (organizations that incubate and kill/fund their own ideas) and veteran serial entrepreneurs who are exploring their next startup. I’ve learned a lot from these conversations, and wish I’d understood their approach when building my first startup. I’ve observed that they share one thing in common with their approach that I didn’t appreciate (and that I see a lot of less experienced founders under-appreciating).

Time is your most precious asset as an entrepreneur. 

As a founder, this is not only a rule of thumb that will push you to make better decisions in your own self-interest, but it also feeds into a critical investment criteria that good seed-stage investors prioritize.

A VC from a top firm told me recently that one of the biggest criteria when evaluating a deal is the ratio of progress a team has made divided by the amount of time/money spent. Additionally, Y Combinator loves seeing teams apply multiple times, so that they can see how much progress a team has made since the last application. They’re highly likely to invite teams that have made significant progress to an interview. 

But what does “progress” mean? Unfortunately, many first time founders equate “progress” to “traction.” While progress and traction are highly correlated once you’ve found a substantial amount of product-market fit and enter a growth stage, they’re very different before finding product-market fit. We’ll define traction as growth in revenue, users, or some other key metric, whereas progress is the percentage of the business has been de-risked – or to put it another way, the amount of certainty in the business model.

Before finding product-market fit and discovering a working model, equating progress to traction can be a fatal mistake that both Max and I have experienced. If I had to boil down a single reason why both Max’s startup (Castle) and my startup (Compass) didn’t work out, it was because we equated traction (revenue growth) to progress. We focused on growth while there was still far too much uncertainty to how the model would work, and our growth slowed down our ability to experiment and nail other parts of the business.

Another challenge is that in the early days, gaining traction (e.g. revenue, users, etc.) can take a long time. You’re limited by sales cycles, adoption cycles, and other constraints that you can’t always hack. For example, if you’re selling to Fortune 1000 companies, it’s going to take a long time to show traction in the form of revenue, but that doesn’t mean you can’t make progress.

However, by focusing on progress rather than traction, founders can maximize their rate of progress over time, enabling them to have more opportunities in the span of their careers to find success while also being able to demonstrate what investors value most in founders – the ability to make significant progress with limited resources.

For example, here are two scenarios: 

  • Scenario A: 3 enterprise customers that fit a clearly defined target market on a free beta where they’re using the product every day and getting significant value out of it is (with $0 in revenue) 
  • Scenario B: $10K MRR with 20 customers that are getting a decent amount of value and using it a few times a week

While Scenario B has more traction, Scenario A has way more progress. If I was an investor, I would bet that if you fast-forward 12 months, the company in Scenario A would end up with significantly more traction than Scenario B.

So how do you maximize your rate of progress over time?

You should do what a lot off startup studios do: run finite experiments which test impactful hypotheses, which if proven right would result in a significant de-risking of the business, and which if wrong, give you valuable information which can inform the next hypothesis. These need to be time-bound experiments where the output is a learning that you can document. 

Here are a few examples, which are not perfect, but give you the idea: 

Example #1

  • Company: AI-driven lead gen tool for enterprise sales teams with SDR Managers
  • Hypothesis: there exists a target customer who is willing and able to give us the data we need for our AI to find qualified leads.
  • Experiment: do customer interviews with 10 SDR managers in the next two weeks and see if they can give us the data we need if we were to return a lead list.

Example #2

  • Company: New D2C cologne brand for men
  • Hypothesis #1: Men will subscribe to a cologne subscription (which would give us statistical confidence that we can get LTV ≥ $200)
  • Experiment #1: Survey 100 men about their cologne preferences and include a question about frequency of use and willingness to subscribe to a service
  • Hypothesis #2: We can get CAC under ≤$75 through traditional digital channels
  • Experiment #2: Allocate $1,000 to FB and IG experiments driving traffic to landing pages to figure out what the CAC would be.

If you’re constantly proving your hypotheses correct, then you aren’t being bold enough and coming up with impactful hypotheses that can maximize your progress over time. In the early stages, proving a hypothesis wrong can be just as valuable as proving one correct, and your hypotheses should be bold enough that you earnestly do not know the answer. The faster you find things that don’t work, the faster you’ll find the things that do.

It’s also critical to document your learnings. Even if you disprove a hypothesis, conditions may change as time passes or your business evolves. Having well-documented learnings results allows you to test related hypotheses down the road without needing to relearn lessons you’ve already learned. One startup studio said that when they disprove a a hypothesis and decide to stop pursuing an idea, they put the learnings “on ice” because they may be able to use it down the road.

By taking this approach and running experiments in parallel when possible, you can make significant progress with your startup in weeks rather than months/years. You just have to be willing to listen to the results of the experiments :). 

This method of hypothesis testing also will eventually work its way into your fundraising narrative. When fundraising, you’ll need to clearly articulate what about the business you’ve validated to date, and the impactful hypotheses you’re planning to test with the next round. It’s better to adopt this mindset as an operator well ahead of raising a round than to try come up with this once you need to fundraise. The hypotheses that you’ve validated by the time that you fundraise should be surprising to an investor and make them think, “holy shit that’s interesting.”

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