Thread by @borrowed_ideas

Thread: How to measure network effects

Unless you have been living under a rock, you have heard about network effects.

It is not binary rather a spectrum which implies some companies have strong and some have weak network effects.

a16z had a good post on how to measure it.

#1 Organic vs. Paid users: What % of your new users are organic?

As the userbase increases, the value of the network should increase and hence, a company should be less reliant on paid acquisition.

If network effects are local e.g. $UBER, this dynamic can vary by geography.

#2 Sources of traffic: As the network grows, how much traffic/transactions on the network are
generated internally, arising from the network itself vs. from external sources?

See the OpenTable example.

#3 Time series of paid CAC: How much do you need to spend to acquire supply?

See the difference between ridesharing vs OpenTable

#4 Prevalence of multi-tenanting: How many of your users also use other similar services? How many users are
active on similar services?

A marketplace for dog walkers/pet owners can move into pet health/food/adjacent products after developing the core network.

#5 Switching or multi-homing costs: How easy is it for users to join a new (and even a non-existent) network? How much value can users get as a new user from joining a different network?

See $SFIX, $UBER, $FB examples.

#6 User retention cohorts: Is your user retention improving for newer cohorts?

Newer cohorts should have better retention than older cohorts when the network was smaller.

Caveats: in some cases, early adopters are the "ideal customers" who may exhibit higher retention.

#7 Core action retention cohorts: Is retention, as defined by users taking a core action for the product, improving for newer cohorts?

See Nextdoor example.

#8 Dollar retention & paid user retention cohorts: Are newer cohorts retaining better on a dollar basis, for every given time period, than older cohorts?

See $ANGI example.

#9 Retention by location/geography: Are participants in the oldest markets— for businesses with local network
effects — better retained, than those in newer markets?

See Care.com example. Also applies for $UBER, $LYFT etc.

#10 Power user curves (aka L7 & L30 charts): Are users shifting to the right side of the power user curve? In other words, are they becoming more engaged over time?

While DAU/MAU is more common metric, power user curves can be a better way to gauge engagement.

#11 Match rate (aka utilization rate, success rate, etc.): How successfully can the two sides of the marketplace find each other?

See ridesharing, LinkedIn examples.

The metric "zero" could also be relevant for $MTCH.

#12 Market depth: Is there enough supply and does it fit users’ needs?

See the nuances between homogeneous and heterogeneous supply with examples of Lime (homogeneous) vs Airbnb (heterogeneous)

#13 Time to find a match (or inventory turnover, or days to turn): How long does it take for supply and demand to match?

See Thumbtack, OfferUp, job marketplaces example.

#14 Concentration or fragmentation of supply and demand: How concentrated is the marketplace on the supply and demand sides?

The greater the fragmentation on both the supply and demand side, the more valuable and defensible marketplace you have.

#15 Pricing power: How much are you able to charge for your product? What would your customers be willing to pay to stay on the network?

Greater network effects should lead to greater pricing power in either or both sides (demand and supply) of the network.

#16 Unit economics: How is the business doing, basically?

Isn't this the Holy Grail? See the example of Instacart, and again the emphasis on understanding whether the network effects are local or not.

End/ For public market investors, the unfortunate reality is we won't have access to much of these data.

It can, however, still help us understand the economics of the business and have better questions in our head.

Link: a16z.com/2018/12/13/16-…