Thoughts on Growth — May 18, 2018

Thoughts on Growth is Reforge's newsletter of must-know updates and perspectives in growth. By subscribing, you'll join a few thousand PMs, marketers, UX folks, engineers and analysts at today's top tech companies. Check out today's edition below.

1. Building growth moats without network effects

Build Competitive Moats without Network Effects v2 (1).jpg

MATT HEIMAN, Investor at Greylock Partners: 

Many great technology companies have benefited from network effects: Facebook, LinkedIn, WhatsApp, Snap, eBay, Airbnb, and Uber to name a few.

However, there are real world problems to be solved and businesses to solve them that will not be candidates for a network effect approach. The nature of these problems or products simply doesn’t allow for the value proposition to be directly tied to the number of others using the product.

Important businesses will be built in areas without hard-coded network effects, and there are ways to become extremely valuable without them. If a product just doesn't have network effect potential, ask the question: what kind of moat can you build?


1) Economics of Scale - The more business a product does, the more negotiating leverage it has to continue procuring what it needs to do business. And, when a product can negotiate lower costs, that savings can be passed on to the consumer. Similarly, scaling lowers per-customer costs by spreading fixed costs over a larger customer base.

Examples: Walmart, MoviePass

2) Brand - Good branding establishes trust and familiarity with your customers. This is critical in emerging or sensitive categories where trust is a key factor in customer decision-making. In established categories, brand is part of the formula that keeps the winners winning.

Examples: Coinbase, Disney

3) Switching Costs - Customers become more invested in your product and get more value from it after they've integrated it into their workflow. Switching is painful once they've built a usage habit, integrated it with other tools, and generated and collected data in it.

Examples: AWS, iPhone, Spotify


2. Do you have enough data to predict lifetime value?

  Image data from Eric Seufert

Image data from Eric Seufert

Calculating LTV can be difficult because it requires looking at a long time range. And, even when a product has great retention rate it can take time to get a sizable sample of users through a 180 day funnel in order to zero in on LTV.

ERIC SEUFERT, head of Platform at N3TWORK, former VP User Acquisition at Rovio and creator of MobileDevMemo, presents his model for vetting an LTV model in soft launch.


  • Daily New Users - Your cohort of new users coming to your product
  • Waiting Period - The length of the time range you want to observe
  • Retention rate - Your rate of retaining customers through the time range
  • Starting LTV - The daily value of a user


  • Cumulative LTV - Your predicted lifetime value of a retained customer
  • Confidence Interval - The level of confidence you can have in your numbers, based on your sample size


1. Only launch apps with a Day 90 LTV that supports marketing.

I see many developers choosing this option: they attach their marketing viability decision to their app's Day 90 LTV and use that threshold to decide whether to kill an app in soft launch.

In other words: if their Day 90 LTV -- which is verifiable in a reasonable amount of time in soft launch, as per above -- doesn't support profitable user acquisition, then the app is killed. As the app collects more data from older cohorts in global launch, they adjust their marketing bids against longer-term LTV values that they feel have been quantitatively substantiated;

2. Use LTV curve ratios from comparable apps to estimate a longer-term LTV.

Developers releasing apps similar to those they already have in market may have seen a reliable ratio emerge between points on those apps' LTV curves (eg. Day 365 LTV is 4x Day 30 LTV). This is a fairly common approach, but I think it underestimates the impact that small nuances can have on late-stage monetization for apps.


The 'Starting LTV' value is just a randomly chosen cumulative LTV value that gets assigned to the first day in the Days variable (so in this case, the Cumulative LTV at Day 1 is $0.10). This value gets multiplied by two at each subsequent Day value.

Again: in this case, Day 7 -- the next value in the Days list variable -- has a cumulative LTV of $0.10 * 2 = $0.20, Day 30 has a cumulative LTV of $0.20 * 2 = $0.40, and so on. These values were chosen arbitrarily.

As a simple reference: most casual freemium games might use $0.05 here, mid-core games might use $0.07, and core games and subscription apps with longer lifetimes might use $0.10.


3. A framework for evaluating growth problems worth solving

Problems worth solving.jpg

HITEN SHAH, Co-Founder @ Crazy Egg and Product Habits, details his framework for finding the biggest problems worth solving:

  1. Market size matters - Big opportunities come from problems that are big enough to build a business on.
  2. Innovate with a purpose - You can have the most ingenious solution, but it doesn't matter if people don't have that problem.
  3. Solve big problems - Novel solutions are fun, but people pay for solutions to their big, painful problems.

With dogo and Draftsend (previous products we've built), we didn’t take a step back to think about the market and the opportunity we were heading into.

  • Who will pay for the product?
  • How many customers need this product?
  • Is the market currently growing?
  • Which segments of the market are growing the fastest?
  • Will this market grow in the future?

With dogo, we had created a product that was episodic and in a small market category where people wouldn’t actually be able or willing to pay for the service we offered. With Draftsend, we focused on the innovation, but not once did we think about the market, nor did we think about the slew of document sharing competitors who were being sold or closed down.

Don't make these mistakes when building out features for growth. Treat it like starting a product company from scratch, and look at how much of the total user base you're impacting, and whether your innovative solution actually moves people through the funnel or deepens retention and monetization.


4. How Strava leverages the “emotional layer” for product stickiness

Emotional_Transactional (1).jpg

Most growth teams focus on the transactional layer of their customers' experience, and concentrate their optimizations there.

The transactional layer is how a person uses your product, like:

  • posting, reading or answering on a forum product
  • completing and logging a purchase in a sales tech product
  • making a booking (or creating a new listing) in an online travel marketplace

But, the emotional layer is why a customer uses your product. The emotional layer that increases product stickiness, deepens monetization, and opens up viral opportunities by creating natural word-of-mouth.


Strava has cracked the emotional code behind why their best-performing customer segment love their product.


Strava started out targeting the fast-growing avid cyclist segment (people who cycled more than 50 times per year and were somewhere between occasional amateurs and hardened pros). GPS devices also were becoming common, generating volumes of data related to athletic activities. But, most GPS data wasn't being used to give users insights on their own performance.

Strava recognized this need, which was even more relevant for the “avid cyclist” segment, and performance logging became its core transactional layer.


Strava’s founders, Michael Horvath and Mark Gainey, identified the emotional layer early on, when they were in a college rowing team. They wanted to translate the feeling of accomplishment and camaraderie and add it to the transactional layer.

Horvath and Gainey realized that these feelings were missing for the cyclists who worked out alone — cyclists who would wake up at 6 AM and climb a seemingly impossible route after months of training wanted the feeling of being able to race against others, or themselves, and of improving their performance.

Strava’s emotional layer:

  • speaks to the desire to engage in friendly competition
  • generates powerful social motivation to log results, check in with the community, and work out again tomorrow

The transactional layer gives them something to do, but the emotional layer gives them a reason to do it — and build a daily habit.


Thoughts on Growth is Reforge's newsletter of must-know updates and perspectives in growth.