How to solve the cold-start problem for social products

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Social products need mass before scaling growth
I often write on the topic of how social products can scale growth, resulting in inbound emails to the effect of “how do I get my product to go viral?” The problem is, until you have a strong baseline of engagement, it’s nearly impossible to have a metrics-oriented discussion on growth and virality. So you have to get that first, before you can talk about the next step.

The focus should be on creating that baseline – a small-to-medium sized network of highly engaged users in a big market, that’s growing. Maybe this is 10,000+ active users organically gaining hundreds per day, at a 20%+ DAU/MAU. If you can hit that, then it’s much easier to talk about how to scale it up. I’ve written Zero-to-Product/Market Fit in the past to talk about some of the steps you might take to reach this stage. Similarly, I have some slides for this topic. (And if you’re at this point, don’t hesitate to email me)

There’s a unique aspect to social products in getting to this baseline, which is how can you solve the dreaded cold-start problem? If your product is inherently social, but you don’t have a critical mass of users, then it’ll naturally fail. How do you get beyond that? This is different than productivity or SaaS products because you don’t just have to get the product right- you have to get your initial user network to be large enough and active enough too.

Here’s a few ways I’ve collected over time on how to approach that problem:

Single user utility
This is one of the most common ways to approach the cold-start problem. Give people a value-proposition that gets them creating/curating content within your network, and as a by product, it’ll help bootstrap the network around the user. I think of Pinterest as the quintessential example here, where you can use it as a tool to collect/gather/organize content around a particular project you’re pursuing- decorating a new apartment, planning a wedding, or switching to a new diet. As you’re doing this, then you use the common mechanisms around finding friends, Facebook sign in, etc., to build a network around the user. If you can get this to grow fast enough, and build the right social feedback loops, then users will find themselves blending a single user value prop with a network value prop over time.

Linkedin is another classic example here, where initially they could market themselves as a way to put your resume online. But of course, once you go through their onboarding flows, you’ll quickly find out that people are connecting and reaching out to you via your profile, thus cementing the network value proposition.

A blog network like Tumblr is another great example. People like making their own websites, and you can use Tumblr for that – plus you get the themes, tools, and domain for free. But once you’re set up, it becomes easy to get reblogged and followed and all of a sudden you’re part of the network product.

The trickiest part of using this strategy is that you’re asking users to switch their mindset from one value proposition into the other. Managing that transition isn’t easy. You may find that users actually want their single user value prop to be private, and nonsocial by nature. Or you might find that if you don’t get the social feedback loops right, you may not be able to convert your one-off users into network users fast enough, and it feels like you are maintaining two separate products. And it might feel like your product isn’t really working if the majority of your users aren’t involved in the network.

Publishing into an pre-existing network
A variation of the single user utility is one where the primary functionality of your product is to share into a pre-existing network. The classic example of this is Instagram, which provided the initial value prop of photo filters and sharing to Facebook, which can be used even if none of your friends are using the service. However, it spread virally over time, which brought more people to Instagram, and this was then used to bootstrap a separate network based on following celebrities rather than the bidirectional Facebook friend model.

(I published a guest post Social Products with with utility, not invites, as a longer exploration of this idea)

The main challenge with this model is twofold: One is the “two value proposition” problem as stated before: Initially, a large % of your users might view Instagram as “that app I use to post to Facebook” rather than a destination in itself. The second challenge is that you can have a platform dependency that may not end well if your “host platform” decides to cut you off.

Small network requirements
Not every product has to have a single user value proposition, and in fact, it can complicate things to feel like you have to design for multiple use cases at the same time. Instead, a different approach would be to focus on building a product that has a small critical mass requirement. In fact, you could look at the following categories and assess their critical mass requirements:

  • Skype: 2+ people
  • Group mailing list: 5+ people
  • Social network: 10+? 50+?
  • Social+mobile+location based: Lots :)

So one strategy is, how can your product be useful for just a small handful of people? That way, if you have a big launch, you can get lots of active pairs of users, like families or couples, and you can hold on to your audience. But if your product requires a very large critical mass of users, then maybe it will be very hard to get there.

Local network saturation
For products that require a large critical mass to get started, I’m already skeptical. But if you must, going after some kind of hyper-connected vertical is a good way to start. Rather than getting 1,000 users randomly and who don’t know each other, instead you focus on getting 1,000 users who are densely connected already. That way, you can saturate the network and hold onto that group of 1,000, and then go from there.

Although the following companies also often had attributes such as single user value prop and small network requirements, it’s useful to think about starting within a niche: Yammer did this within a company. Yelp did this within San Francisco. Facebook within Harvard, and Twitter within the tech community at SXSW. Snapchat within SoCal high schools.

Perhaps there’s a niche hyper-connected pre-existing network that matches with your product, and if you can retain those users, then you can build a much larger network from there.

Why big unfocused launches often fail
The above provides a clue on why big social product launches on Techcrunch or DEMO or whatever often fail. The problem comes down to the fact that for social products, you often need hit some metric of connection density to succeed. A “minimum network density” metric, if you will. And when you think about it like that, you’d much rather have 100,000 users with a density of 30 connections/person, than 1,000,000 who have a density of 2 connections. Because ultimately, those million users will churn out because they won’t have the content and feedback loops necessary to stay engaged.

Big launches fail because they might pump up the total users number, but don’t help much with the network density number. If anything, they might lower the average. So if you want to go with the big launch, make sure that it either targets a network that’s hyper-connected, and who will onboard nicely into your product. Or make sure there’s a very strong single user value prop, and even if they can’t find anyone they know in the product, that’s OK.

OK, good luck my friends!

Published by

Andrew Chen

Andrew Chen is a general partner at Andreessen Horowitz, investing in startups within consumer and bottoms up SaaS. Previously, he led Rider Growth at Uber, focusing on acquisition, new user experience, churn, and notifications/email. For the past decade, he’s written about metrics, monetization, and growth. He is an advisor/investor for tech startups including AngelList, Barkbox, Boba Guys, Dropbox, Front, Gusto, Product Hunt, Tinder, Workato and others. He holds a B.S. in Applied Mathematics from the University of Washington

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