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Why consumer product metrics are all terrible

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The reality of consumer products
I’ve never met an entrepreneur who’s happy with their metrics.

Whether you’re talking about sign up rates, retention rates, or how often your users create content – on face value, the metrics always seem terrible. The secret is, almost everyone’s consumer product metrics are horrible, so once you start to compare them with everyone else’s terrible metrics – then at least we’re all in the same leaky boat together!

Other than the exceptional cases, consumers are impatient and disinterested in your product. Even the ones who sign up to try it out, only a small % are willing to stick around to use it more. As we discuss later, a typical product might see 90% refuse to sign up to a product. And then of the ones who do sign up, over 90% of users disengage and become inactive over time. These metrics are terrible, but they’re normal.

The purpose of this discussion isn’t to excuse mediocre engagement or retention, but rather, to have an honest discussion of what most companies are seeing in the market. This will help us plan better, give us more options for our Plan B, versus being total newbs on the issue.

This essay breaks down a few different metrics and the uphill slog we all face as consumer-focused entrepreneurs:

  • Signup rates
  • Retention and frequency
  • Social graph density

And before we start, it’s worth mentioning that every product is different. Mobile apps often have better engagement metrics, but have lower upfront conversion rates. SEO products have the lowest signup rates. But the intention of this essay is to add to the discussion for the kinds of social apps being built right now – social consumer products on web and mobile – and give everyone a baseline for discussion.

Signup rates as low as 1%
Average signup rates are surprisingly low. On homepages, it’s not so bad- sometimes 10 or 20% signup rates are possible. But they can be as low as 1%, or even lower, when you’re talking about non-homepage pages where people are coming in from SEO. In the extreme, when a user arrives on a content-filled landing page after typing in a query like “what is this growth I have on my back?” their primary interest is the content, not the product you’ve created. Similarly, there’s always pressure from Google’s robots to present as much content as possible, rather than hiding it behind a registration wall.

Thats why for SEO-driven products like Stackexchange, Yelp, and others, the conversion to a signed up user is extraordinary low on these content pages, sometimes much less than 1%. This leads to a pretty depressing metric in an era where most social products measure and report their Monthly Active Users, which consist of activity from users who have signed up. Unique users per month seems so 1998 :(

What if you want to raise the signup rates on these detail pages? Of course you can choose to raise this number by gating the content, as Quora has does, but perhaps at the expense of UX:

Screen Shot 2014-05-12 at 1.13.22 PM

 

But these are just the content pages. If we’re talking about the homepage, we’d expect signup rates to be much higher. The reason is that this traffic is usually based on word of mouth, which leads to sign up rates that are 10% or higher, since people are looking for your product in order to try it. Even better, you can send them to a minimal homepage that generates signup rates closer to 20% or 30%.

Over 95% of your users are inactive on any given day
Another metric that’s easily depressing is retention, where it’s common to see that the vast majority of your users, often over 90%, aren’t engaged on a daily basis. Instead, they’ve churned or are only active a few days per month.

The reasoning for this is simple. It’s become common to look at retention/frequency metrics in the form of D1 versus D7 versus D30 retention. Naturally, D1 means, “the number of users active on the day after signing up.” And usually retention curves look something like the below, where there’s fall-off pretty quickly with eventually stabilization around a mediocre number – often a single digit percentage:

Screen Shot 2014-05-12 at 3.11.52 PM

Usually there’s a very steep drop-off over the first week or two, and then it starts to stabilize. But you lose a ton of active users in the meantime, which is a result from multiple factors. This curve combines a few different aspects of your product:

  • First, how many users sign up and actually try out your product (onboarding)
  • And also, within a month, how many days are they active? (frequency)
  • Finally, how useful is your product over time (long-term retention)

Given that frequency is often low – 3 or 4 active days per month isn’t uncommon – when you pair that with crappy onboarding or retention, then very quickly you’ll see that getting 10% of your users to come back every day is an amazing feat. Anything more than 10% of your total users coming back every day is a success case! More often it’s 5%, or even lower.

So what if your metrics aren’t at this level? Sadly, this isn’t something that’s easily fixable with something superficial, like more email or push notifications. As I’ve noted before, a lot of these engagement metrics are more nature than nuture, and getting high usage every day has as much to do with the product category you’re building for as anything else. I’ve yet to see a product with horrible DAU/MAU get fixed using cosmetic changes. If your engagement or frequency sucks, figure out how to tie it into someone’s pre-existing behaviors, rather than asking them to do something new.

Changing engagement metrics might be the hardest thing to do with products, though. You can make your onboarding better, or get people to invite incrementally more friends. But getting them to come back over time, that’s not something that’s easy to solve using optimization techniques.

50% of your users are forever alone
So let’s say you build a new social product, whether it’s a new form of microblogging  or a new messaging app. Of course, the ideal is to have a nice feed full of personalized content. But it turns out, most products are very, very far away from that. How many people have, effectively, zero friends? You’d be surprised to know that often 50% or more of your users don’t know anyone else in the service, meaning that you need to backfill their feed with a bunch of content just from one person, or worse yet, impersonal content.

In fact, one of the most explosively viral products in recently history had a full 65% of their users disconnected from anyone else: Instagram.

Here’s a pie-chart by RJMetrics of how many Instagram users followed, while the product was in their first year:

6

 

And later in the article, this is what they say about it:

Interestingly, over half of Instagram’s users are following exactly one other user, with another 13% not following anyone. We checked into that, and it looks like the vast majority of users who follow only one other user are following the “Instagram Team” account, which was likely automatically added to their list at signup.

This means that 65% of users effectively follow no one.

(Emphasis added.) This is amazing, and ultimately didn’t stop them from becoming a very functional social network alternative to Facebook itself.

When you combine the fact that getting social graphs to fill is very hard, and the fact that only a few percentages of users will author content (known by the 1% rule), then you can imagine why creating a healthy, dynamic news feed is so hard.

Ultimately, density can be solved by more growth. More users mean a denser social graph. But also, a key component of getting people to follow more people is to connect their Facebook accounts, their email addressbooks, and other pre-existing graphs which help them bootstrap their relationships. Or do what Twitter does, in forcing users to follow before creating an account.

Mediocre metrics aren’t an excuse
The point of this essay isn’t to provide an excuse for mediocre metrics, but rather, to point out the harsh reality of the situation. There’s just a stark contrast between how much we as consumer entrepreneurs care about our products, versus our target audience who really doesn’t give a shit about how much effort we put in. As a result, people aren’t signing up, and if they do, they don’t use the product nor have a good experience. It’s very hard.

But even as the average product’s metrics suck, as an industry we’re looking for the unicorns. So just as I say that a 30% DAU/MAU is good, when you compare that to Whatsapp’s 70%, you can see the gulf between good versus great. We all want great, because the tech industry is all about building great products.

On the plus side, even though these percentages all seem small, great businesses have been built from a few percentage points here or there. How many paid subscription services monetize by convincing 2-3% of users to pay? Tons of them. Or who build billion dollar ad-supported businesses just based on getting a few % of users to click on ads? That’s everyone in the ads business. So it can work, but until it’s scaled and is growing faster, these metrics can look like a mess. Until then, keep at it.

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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!

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I write a high-quality, weekly newsletter covering what's happening in Silicon Valley, focused on startups, marketing, and mobile.

How to design successful social products with 3 habit-forming feedback loops

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Social products share a common ancestry and set of problems
It’s been a decade after Friendster popularized the notion of the social network, and we’ve seen hundreds of flavors of social products. Many of them are very different from each other, showing that success can come from many variations. I’ve come to believe there’s 3 main feedback loops that drive the success of these social product designs – here’s the trifecta:

  1. A feedback loop that rewards content posters when they push new content into the network
  2. A feedback loop that rewards passive content consumers with relevant and valuable content
  3. A feedback loop that rewards (and culls) connections within the network

It’s great when all three feedback loops act in harmony. As users act within each feedback loop, everyone’s happy, and the players in the ecosystem produce and consume valuable content for the network. When this happens on a daily or hourly basis, it creates habitual usage within your product- driving engagement and retention.

On the other hand, when even one feedback loop starts to fail, reverse Metcalfe’s Law goes into effect, leading to stagnation and ultimately, network collapse.

As an industry we’ve often talked about the distribution of content creators, curators, and consumers – it’s often known as the 1/9/90 principle. But that’s about the distribution of these different kinds of users, and not about fundamental motivations behind their actions. The feedback loops for social product aims to think in terms of why these feedback loops are able to create happy emotions and build up habits. Furthermore, by looking at each loop in isolation, it becomes more obvious where one could innovate- by adding a twist in content creation, consumption, or how people are networked. I’d argue that anonymity, constrained media types, algorithmic news, and other innovations all fit into these feedback loops in different ways.

Content posters that crave feedback (or utility)
First and foremost, let’s talk about the folks who post content – these are the 1% and 9% part of the 1/9/90. These users might post content by creating it in a textarea or uploading a photo, or it might be more curation oriented- simply retweeting a funny link or sharing a link. Either way, they take an action that writes new info into your network that impacts the content consumption experience. The feedback loop that’s important here is to reward content posters with social feedback. You publish content to your audience and then social feedback trickles in over time, drawing you back to the product. If content creation is easy enough, and the social feedback is compelling enough, then you do more. And so the loop continues.

It turns out that what type of content people post is important: Social products ultimately have some kind of content in the middle of it (sometimes called the social object), that determines the posting/consumption behavior of the content. This might be a tweet, a photo, a musical playlist, a restaurant review, or even a commerce page. It would be a mistake to assume that it’s as simple as wanting this content to be as simple as possible to create, because you also need to make it a frequent behavior. You also need the resulting content to be compelling as well – it’s these constrains that make this system tricky.

First let’s talk about what it means to make the posting “easy” – it’s not just that the tools are simple, but also:

  • You’re already creating it, so it’s not a new behavior (for example, almost everyone sends links, photos, etc.)
  • You can create it in seconds (sometimes via an artificial constraint)
  • You do it all the time, and over a long period of time
  • You don’t feel self-conscious publishing it
  • You can use new technology that lowers the bar (location sensors, camera, etc.)

A lot of the recent innovations in social products have focused on making this easier. One important tool is the use of constrained media types, where a tweet of 140 characters ensures a level playing ground for content so everyone can write a tweet in a few seconds. The 6-second Snapchat lowers the mental effort in taking the perfect photo. Foursquare uses our smartphones to make it easy to publish our location, whereas years ago, the effort on a feature phone would have been much higher. Similarly, the new trend of anonymity is another way to lower our inhibitions towards content creation. (I’m excited about the trend towards wearable and ubiquitous computing because they’ll be tools for all sorts of easy content creation.)

The tricky part of content creation is that the output has to be compelling to consumers, and over a long period of time. If your content is novelty (for example an avatar creator), then it may thrive for a period of time but ultimately the loop will weaken and stop. That’s fine for an ad campaign but not a product.

On the other hand, sometimes content can be very high cost but still be really compelling, for example long-form writing or high-production video production. You end up with a small % of creators who can actually author the content, but the end result is compelling enough that the whole thing keeps going. Yelp reviews, Stackoverflow, and others operate like this, with a push from SEO which help both creators and consumers find the site again over time.

Ultimately, the balancing act between content creation cost, the frequency/retention of it, and how compelling the output is – well that’s the magic of a new product design.

The health of the feedback loop around content consumption versus social feedback is based on a number of key variables, all of which are interrelated with each other:

  • What % of users create content
  • How much content is created (ease, frequency, retention)
  • Who this content is shown to
  • How compelling the content is
  • What % of consumers give feedback to the content creators
  • How compelling that feedback is
  • Whether the feedback brings back content creators to make more

The tricky part to the above is that many key variables oppose one another. You can increase how often content is shown to people just by blasting out content indiscriminately, but that decreases the relevance of the content. You can make it really easy to give user feedback, but at the cost of making the feedback less compelling. All of these tradeoffs ultimately manifest themselves in the design of a social product, hopefully in the right dosage and combination.

One footnote is that content posters can also be compelled by providing a single user utility, which produces compelling content as a byproduct. The classic example of this is bookmarking- Pinterest and Delicious help you organize content as your single user utility, but once the content is in the network, other folks can interact with it. This ultimately bootstraps the network as positive social feedback flows in, ultimately replacing the “organize stuff” value proposition with a “people tell you how much they love your stuff” benefit.

Content consumers want relevant content, updated frequently
Now lets think about the viewing experience. When it comes to content consumption, I think about the things that people want to look at every day. There’s not too many of them. News about their friends/family, news about the world. News about work. That’s one big chunk. Entertainment, which these days might look like YouTube videos, but even easy-to-create memes. For some demographics, maybe they want to see commerce content – shopping is always fun. And if you have hobbies, maybe you want to see a bunch of vertical content about that kind of thing – whether it’s about the arts, cooking, or programming.

The feedback loop for content consumers is simple: Every time they open your app or website, they see compelling content. That builds a habit for them to check in every morning, every time they’re standing in line, and every time they’re bored at work.

Yet the loop is easily broken – here’s the usual failure states:

  • Feeds that lack content
  • Feeds with stale content
  • Feeds with too much content
  • Feeds with irrelevant content

Lack of content and stale content comes from using a friending/following method of connecting content posters and consumers – but often, the network is underdeveloped or isn’t growing fast enough. Or maybe there’s not enough friend density to drive a full feed. Or even if there is a lot of users using the product, there isn’t the “right” users – for instance, an adult user stumbling into a website mostly filled by teens. These are some of the common reasons why it can be difficult to evaluate new social products – even if the mechanics and loops are well setup, if you don’t have the right users it’s hard to see the magic.

But once there’s a nice balance of new content coming into a feed at about the rate that content consumers want to see it, something great happens. Then the engagement can lead to people giving social feedback to the folks who posted it in the first place – via likes, comments, re-shares – and that stimulates the production of more content.

Connecting content posters and consumers to drive relevance
The way that content consumers participate in the feedback loop is that they give feedback to content creators. But before they do that, they need to have a method of picking what content is relevant to them on their home screens:

  • Picking people (Facebook, Twitter)
  • Picking topics (Quora, Stackexchange)
  • Leaderboards (Reddit, Hacker News)
  • Editorial curation (Medium)
  • Algorithmic curation (Flipboard, Prismatic)
  • Location (Foursquare, Highlight)
  • Anonymously matched (Secret)
  • … and more to be invented!

All of the above work, with different tradeoffs. Allowing people to customize their content consumption based on people and topics is the most scalable, but the hardest to get started. It’s a classic cold-start problem. To get to that, you need a critical mass of content creators who are making the kind of content that might attract a passive audience. Given that content creators are also consumers, that’s why oftentimes it’s the easiest to get started with a group of content creators.

The feedback loop about generating meaningful connections needs to reward the network when authentic connections are made. When you pick a new topic, or a new person, does that expose you to new content that then gives you new opportunities for people to follow? Do you have plenty of opportunities to unfollow or otherwise clean out your feed of irrelevant information? And are new people joining the product all the time, driving notifications, re-engagement, and ultimately new content into the network?

Editorial curation and leaderboards (like Hacker News) are easier to start, but have the drawback that they don’t scale well. Editorial requires you hire lots of people. Leaderboards create a single public space where it’s difficult to create a “one size fits all” experience that makes everyone happy.

It’s also difficult to mix the two. If you combine user generated content with editorial, within the same feed, then inevitably editorial content will “steal” the feedback from the UGC. That’ll weaken the loop. Instead, to really make sure that enough social feedback is being given, the goal is to make a feed with compelling assortment of content, and a lot of easy ways for consumers to interact with the content creators.

Another interesting issue on social feedback is the issue of quality. If you upload a video to YouTube, and then get 1000s of incomprehensible comments from teenagers, is that better than a smaller number of comments from thoughtful people? I suppose it depends on your own tastes, but over time, I’ve personally come to value the feedback of a small group of people I respect rather than trying to maximize the levels of pageviews or comments that I get.

This can be a difficult challenge because startups obviously face the pressure to grow, and one of the easiest ways to do that is to get your users to invite and add lots of meaningless connections. At the same time, if they follow too many users, or topics, then their feed will get busy and the product will lose relevance. So ideally, you have a system in place where users can add (and remove) connections to other users easily, and the system is able to suggest more relevant connections. This should also provide a better and more personalized experience around content consumption.

Building a checklist to ensure your loops are healthy
I leave you with a checklist for those of you who are designing social products, but find that your feedback loops aren’t quite working. The question I’d ask is, zoom into each of the feedback loops, starting with the folks who are posting content. Ask yourself, are they getting feedback on every action they take? Is it high quality feedback that makes them feel good? Are they making enough content to be interesting? If not, the feedback loop is broken and needs to be fixed.

For content consumers, are people getting high value, meaningful feeds? Or is it a random mishmash of popular content in your product? And if the feedback loops aren’t working, consider creating a small network where it all works, and grow that out, rather than forcing bad feeds on everyone that visits.

Or alternatively, consider taking a small part of another products’ feedback loops, and tweaking it a little. There’s many innovative products yet to be invented.

In a decade of social product design, we’ve seen many significant innovations around many components of these feedback loops. Facebook innovated with real names and a privacy model which helped drive closer-knit social feedback. They also invented the feed, a new way for posters and consumers to more efficiently transact on content. Twitter pioneered the follow model, which is yet another way to connect people. Instagram took advantage of much easier content creation methods on your smartphone, combined with plugging into existing networks, to bring something new to the model. And recently, anonymity apps like Secret are connecting people in yet another new way.

When I first arrived in Silicon Valley back in 2007, I remember a very smart B2B investor asked me, “Does the world need another social app?” implying that the category had been fully exploited. I think we see that in fact, given the years of solid innovation since then, there’s many new social products yet to come.

PS. Get new updates/analysis on tech and startups

I write a high-quality, weekly newsletter covering what's happening in Silicon Valley, focused on startups, marketing, and mobile.

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