Tracking What Customers Love

focus, lens, product-market fit is fluid, how to find product market fit

Product-market fit is fluid. Just because you’ve attained it once doesn’t mean you’ll have it forever. The market is constantly changing. And that means the intersection where supply meets demand will always be changing as well. That said, regardless of how and where you move to, you’ll always have a subset of your customers who aren’t happy. Who might miss the old ways. Who might wish for something else entirely.

To put it into perspective, I’m going to quote Casey Winters (his blog), the current Chief Product Officer at Eventbrite:

“Product-market fit isn’t when your customers stop complaining, it’s when they stop leaving.”

Retention and its Touch Points

If you run a business, you’re going to have a leaky funnel. Your job is to minimize the leaks. Double down on not just adoption, but especially retention. What does that mean? Engagement and the often, overlooked category, for many early-stage teams, re-engaging those that have become inactive over a set period of time. Whether 30 days or 7 days. It depends on what solution your product is providing for the market and how frequently you normally expect them to use the product. For example, for most consumer apps, as investors, we expect a minimum of usage for 3 days out of the 7 calendar days a week. So I characterize inactivity aggressively as after a month of inactivity.

In the past few months, since the health and economic crisis began, the conversation has shifted from ‘growth at all costs’ to profitability. And similarly, from an overemphasis on adoption to a better understanding of retention.

Speaking of retention, 2 days ago, the afore-mentioned Casey Winters and Lenny Rachitsky published their homework on the the dichotomy between good and great retention, which you can find here and here, respectively. Their research provides some useful touch points about “golden” numbers from some of the smartest people in the industry. Of course, as their research suggests, everyone’s “golden” number is different. At different points in time.

So, how are you tracking how lovable your product is?

One of my favorite ways to track what keeps users coming back for more is the Depth vs. Breadth graph. Plotting how long people use certain features and how often they click into it. You can easily substitute length of time (depth) with the number of actions taken for each product feature you have. Or as you grow into having multiple product offerings, this graph works just as well.

depth vs breadth graph, retention, product features

Below are just a few examples of breadth and depth metrics:

BreadthDepth
# of logins/week# actions/session
Session countSession time length
D1/D2/D7/D30 sessions# concurrent devices logged in
Platform-specific sessions
DAU/MAU
# paid users/ # total

The above graph should also help you better optimize your features/offerings. For instance, let’s say you’re a startup in your growth stages. Going by Reid Hoffman‘s rule of thumb for budgeting, spend:

  • 70% on your ‘popular‘ product offerings,
  • 20% on your ‘niche‘ product offerings,
  • And 10% exploring your any hidden gems in your ‘broad‘ quadrant.

In closing

If you have your finger on the pulse about what your customers love about you at all times, you’ll be able to create a more robust product. As a final note, I want to add that while this piece has been dedicated to what your customers love, please always keep in mind what they hate as well. And why they hate what they hate. Who knows? You might discover a larger secret there.

Photo by Paul Skorupskas on Unsplash


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A Small Nuance with Early Growth Numbers

startup growth
Photo by Ales Me on Unsplash

My friend, Rouhin, sent me this post by a rather angry fellow, which he and I both had a good chuckle out of, yesterday about how VC is a scam. In one part about startup growth, the author writes that VCs only care about businesses that double its customer base.

The author’s argument isn’t completely unfounded. And it’s something that’s given the industry as a whole a bad rap. True, growth and scalability are vital to us. That’s how funds make back their capital and then some. With the changing landscape making it harder to discern the signal from the noise, VCs are looking for moonshots. The earlier the stage, the more this ROI multiple matters. Ranging from 100x in capital allocation before the seed stage to 10x when growth capital is involved. But in a more nuanced manner, investors care not just about “doubling”, unilaterally, but the last time a business doubles. We care less if a lemonade stand doubles from 2 to 4 customers, than when a lemonade corporation doubles from 200 to 400 million customers, or rather bottles, for a more accurate metric.

After early startup growth

Of course, in a utopia, no businesses ever plateau in its logistical curve – best described as it nears its total TAM. That’s why businesses past Series B, into growth, start looking into adjacent markets to capitalize on. For example, Reid Hoffman‘s, co-founder of LinkedIn, now investor at Greylock, rule of thumb for breaking down your budget (arguably effort as well) once you reach that stage is:

  • 70% core business
  • 20% business expansion – adjacent markets that your team can tackle with your existing resources/product
  • 10% venture bets – product offerings/features that will benefit your core product in the longer run

And, the goal is to convert venture bets into expansionary projects, and expansionary projects to your core business.

Simply put, as VCs, we care about growth rates after a certain threshold. That threshold varies per firm, per individual. If it’s a consumer app, it could be 1,000 users or 10,000 users. And only after that threshold, do we entertain the Rule of 40, or the minimum growth of 30% MoM. Realistically, most scalable businesses won’t be growing astronomically from D1. (Though if you are, we need to talk!) The J-curve, or hockey stick curve, is what we find most of the time.

The Metrics

In a broader scope, at the early stage, before the critical point, I’m less concerned with you doubling your user base or revenue, but the time it takes for your business to double every single time.

From a strictly acquisition perspective, take day 1 (D1) of your launch as the principal number. Run on a logarithmic base 2 regression, how much time does it take for your users (or revenue) to double? Is your growth factor nearing 1.0, meaning your growth is slowing and your adoption curve is potentially going to plateau?

Growth Factor = Δ(# of new users today)/Δ(# of new users yesterday) > 1.0

Why 1.0? It suggests that you could be nearing an inflection point when your exponential graph start flattening out. Or if you’re already at 1.0 or less, you’re not growing as “exponentially” as you would like, unless you change strategies. Similarly, investors are looking for:

ΔGrowth Factor > 0

Feel to replace the base log function with any other base, as the fundamentals still hold. For example, base 10, if you’re calculating how long it takes you to 10x. Under the same assumptions, you can track your early interest pre-traction, via a waitlist signup, similarly.

While in this new pandemic climate (which we can admittedly also evaluate from a growth standpoint), juggernauts are forced to take a step back and reevaluate their options, including their workforce, providing new opportunities and fresh eyes on the gig economy, future of work, delivery services, telehealth, and more. Stay safe, and stay cracking!


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