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  • where DAU/MAU fails

    How DAU/MAU got popular DAU/MAU is a popular metric for user engagement – it’s the ratio of your daily active users over your monthly active users, expressed as a percentage. Usually apps over 20% are said to be good, and 50%+ is world class. How did this metric come into use? DAU/MAU has been a popular metric because of Facebook, which popularized the metric. As a result, as they began to talk about it, other consumer apps came to often be judged by the same KPIs. I first encountered DAU/MAU as a ratio during the Facebook Platform days, when it was used to evaluate apps on their platform. Assessing product/market fit with DAU/MAU It’s an important metric, to be sure, but it’s often misused to say that “XYZ isn’t working” when in fact, there’s a slightly less frequent usage pattern that’s still equally valuable. For consumer and bottoms up SaaS products, this metric is super useful, but seems to mostly exclude everything besides messaging/social products that are daily use. These are valuable products, but not the only ones. Products that aren’t daily, but still hugely valuable Not everything has to be daily use to be valuable. On the other side of the spectrum are products where the usage is episodic but each interaction is high value. DAU/MAU isn’t the right metric there. At Uber, our most profitable rides are to airports, via Black Car for a special night out, business travel, etc. These don’t happen every day, and although there are folks using us to commute, that’s not the average use case. So our DAU/MAU wasn’t >50%. The driver side has clusters of “power drivers” who are active >30hrs/week, but as it’s been widely published, our average driver is actually part-time. (Pareto Principle!) LinkedIn is another interesting example which is low frequency – only recruiters and people looking for jobs use it in daily spurts – but it throws off so much unique data that you can build a bunch of vertical SaaS companies on top of this virally growing database. Products in travel, like Airbnb and Booking, are only used a few times per year by consumers. The average consumer only travels ~2x/year. Yet there are multi deca-billion dollar companies built in this space. In fact, for SaaS, it seems to be the exception not the rule. While email and business chat can be nearly daily use, a lot of super important tools like Workday, Google Analytics, Dropbox, Salesforce, etc. might only be used 1-2x/week at most. Much of e-commerce looks like this too, of course. You buy mattresses, new sunglasses, watches, etc fairly infrequently. Yet there are $1B+ wins in the category. You may notice a pattern here. If you’re low-frequency/episodic, then you have to generate enough dollars or data that it’s valuable. If you’re high-frequency, you have a higher chance of growing virally and building an audience business that monetizes using ads. Nature versus nurture To extend this idea further, you can argue that messaging/social products with high DAU/MAU is actually the extreme case, and in fact most product categories don’t index highly. I found this interesting diagram which compared different app categories and their retention versus frequency of use: In this chart, a couple categories jump out: Social games have high frequency (“I’m getting addicted!”) but once you burn through the content, you tend to churn Weather is interesting too – you don’t often check, maybe only on cloudy days, but you will have a need to check throughout your entire life- so it maxes out on highest retention rate over 90 days Communication, for all the reasons discussed before, is both high frequency and high retention. That’s awesome! What I’d love to see on this chart would be another overlay, monetization. There, I bet Travel, Dating, and Gaming would tend to stand out for different reasons. Travel because each transaction is big, and Dating/Gaming because it’s frequency combined with a focus on monetization because you won’t have the user for long. So you want to increase DAU/MAU? It’s hard So let’s say that you want your DAU/MAU to increase – so what do you do? Funny enough, a lot of people seem to implement emails and push notifications thinking it’ll help. My experience is that it tends to increase casual numbers (the MAU) but not the daily users. In other words, it’ll actually lower your DAU/MAU to focus on notifications because you’ll grow your MAUs more highly than your DAUs. I’ve also not seen a 10% DAU/MAU product, through sheer effort, become 40% DAU/MAU. There seems to be a natural cadence to the usage of these product categories that doesn’t change much over time. Increase, measure your hardcore users, network effects, monetization If your DAU/MAU isn’t super high, this is what I like to see instead: Show me your hardcore userbase. What % of your users are active every day last week? What are they doing? How are you going to produce more of them? Showing this group exists goes a long way. Similarly, show how the freqency of use increases in correlation to something. Perhaps size of their network – showing network effects – or how much content they’ve produced or saved. Then make the argument that by increasing that variable, DAU/MAU will rise in cohorts over time. Finally, maybe DAU/MAU is just not for you. Sometimes you don’t have to be a foreground app to be successful. Maybe you just need to build something awesome that does something valuable for people, makes enough money, and they use it twice a year! Also great. DAU/MAU is useful, but has its limits In conclusion, if your product is a high-frequency, high-retention product that’s ultimately going to be ads supported, DAU/MAU should be your guiding light. But if you can monetize well, develop network effects, or quite frankly, your natural cadence isn’t going to be high – then just measure something else! It’s impossible to battle nature… just find the right metric for you that’s telling you that your product is providing value to your users.

  • The Power User Curve

    The importance of power users Power users drive some of the most successful companies — people who love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers. All companies want more power users, but you need to measure them before you can find (and retain) them. While DAU/MAU — dividing daily active users (DAUs) by monthly active users (MAUs or monthly actives) — is a common metric for measuring engagement, it has its shortcomings. Since companies need a richer and more nuanced way to understand user engagement, we’re going to introduce what we’ll call the “Power User Curve” — also commonly called the activity histogram or the “L30” (coined by the Facebook growth team). It’s a histogram of users’ engagement by the total number of days they were active in a month, from 1 day out of the month to all 30 (or 28, or 31) days. While typically reflecting top-level activity like app opens or logins, it can be customized for whatever action you decide is important to measure for your product. The Power User Curve has a number of advantages over DAU/MAU: It shows if you have a hardcore, engaged segment that’s coming back every day. It shows the variability among your users: some are slightly engaged, whereas others are power users. Contrast this with DAU/MAU: it’s a single number and so blurs this variance. When mapped to cohorts, Power User Curves let you see if your engagement is getting better over time — which in turn helps assess product launches and performance of other feature changes. Power User Curves can be shown for different user actions, not just app opens. This matters if the core activity that matters for your product is deeper in the funnel. In other words, while the DAU/MAU gives you a single number, the Power User Curve gives entrepreneurs several avenues of analysis to assess their product’s engagement to the most addicted users — in a single snapshot, over time, and also in relation to monetization. This is useful. So how does it work? The Power User Curve will “smile” when things are good The shape of the Power User Curve can be left-leaning or smile-like, all of which means different things. Here’s a smile: The Power User Curve above is for a social product, and shows the characteristic smile shape that indicates there’s a group of highly engaged users using the app daily or nearly daily. Social products with frequent user engagement like this lend themselves well to monetization via ads—there’s enough users returning frequently that the impressions can support an ad business. Remember that Facebook would have a very right-leaning smile, with 60%+ of its MAUs coming back daily. What matters is that, over time, the platform is able to retain and grow its power users: successive Power User Curves should ideally show users shifting over more to the right side of the smile. As the density of the network grow, and with stronger network effects, it’s expected that there’s more reason for users to return on a daily basis. The Power User Curve can show when strong monetization is needed Let’s look a different example, which doesn’t smile: This Power User Curve of a professional networking product looks quite different than that of a social product. It’s left-weighted with a mode of just 1 day of activity per month, and decays rapidly after those few days. There’s no power users. But this light engagement can be okay — not every company needs to have a smile-shaped Power User Curve, just as not every product category necessarily lends itself to an ultra-high DAU/MAU. When there’s low engagement, what matters is that the company has a way to extract enough value from users when they are engaged. Think about an investing product like Wealthfront or networks like LinkedIn — few users are likely to actively check it on a daily basis, but that’s ok, since they have business models that aren’t tied to daily usage. CEOs of such companies should therefore,think about: Is there a way to create revenue streams where the business can still monetize effectively despite users’ infrequent engagement? Or, who are the users using this product more frequently, and how can I get more of them? Is there something about the product — e.g. onboarding, the core experience, etc. — where a significant chunk of the user base isn’t experiencing the ‘aha moment’ that makes them “get” the product, and therefore not getting value from it right now (and if so how to get there)? Some products should be analyzed in a 7 day timeframe – like SaaS/productivity – and others on 30 days Another flavor of the Power User Curve is a histogram of users’ engagement for a 7-day period, also commonly called L7. The 7 day Power User Curve shows weekly actives, not monthly actives. Plotting this version can make sense if your product naturally follows a weekly cycle, for instance, if it’s a productivity/work-related product that users engage with Monday through Friday. B2B SaaS products will often find it useful to show this version, as they want to drive usage during the work week. Note that using DAU/MAU wouldn’t be the appropriate metric for this product as it’s not designed to be a daily use product. You can also see there’s actually a smile curve through 5 days, but fewer users are using it 6-7 days, which makes sense for the power users of a workweek product like this. CEOs of such product companies should therefore want to understand: Who are the users engaging just 1 or 2 days each week? Are there certain teams or functions within an organization that are getting more value, and how can I build out features to capture the teams with less engagement? Or, if the product is really driving a lot of value for specific departments — how can I understand their needs better and make sure we continue building in a direction that supports their daily workflow (and that we can upsell new features)? The trend of over time can show if the product is getting more engaging over time Plotting the Power User Curve for different WAU or MAU cohorts can also be very insightful. Over time, you can see if more of your user base are becoming power users, by seeing the shift towards higher-frequency engagement. Here’s an example: The Power User Curve for MAU cohorts from August through November shows a positive shift in user engagement, where a larger segment of the population is becoming active on a daily basis, and there’s more of a smile curve. You can see when the line starts to inflect in order to see when a critical product release or marketing effort might have started to bend the curve.  This might be a place to double down, to increase engagement. For a network effects product, you might expect to see newer cohorts gradually improve as you achieve network density/liquidity. On an ongoing basis, you can measure the success of product changes or new releases by looking at different cohorts’ Power User Curves. If a product unblocks a bunch of features for power users, you might see a gradual increase in power users. The Power User Curve can be based on core activity, not just app opens or logins The frequency histogram can be keyed on actions beyond the visit — did someone show up or not — you can also go with deeper user actions. For instance, you may want to plot the core activity that maps closely to how your business is monetized… or that better represents whether users are getting value from your product. This is important because it forces you to think about what really matters to measure. The above chart for a content publishing platform shows the total number of days in the month users posted content. A lot of products have smile-shaped core activity Power User Curves, because while most people tend to contribute lightly, there is a small contingent of users who are power users. Think of the distribution of Youtube creators, or Ebay sellers, or even how often you post on Facebook. As the CEO or product owner of a platform like this, it’s important to design the platform such that the everyone has a chance to succeed. On Facebook, the news feed algorithm makes sure that if you feel strong affinity to a person or organization, you’ll still see their posts even if the sheer volume of other content (for instance, from more prolific media companies) would otherwise drown it out. On OfferUp, even if I seldom sell items, when I do list something, their algorithm makes sure that it’s surfaced to the relevant potential buyers. Why does this all matter? Not everything is a daily use product, and that’s okay. Power user analysis allows you to get a better understanding of how users are engaging with your product, and make more informed decisions using that data. That might mean choosing an appropriate business model that works for your pattern of engagement, or designing better re-engagement loops for lower-engaged user segments, or doubling down on use cases that your high-engagement user base is already getting value out of. The beauty of the Power User Curve over DAU/MAU is that it shows heterogeneity among your user base, reflecting the nuances of different user segments (and therefore what drives each of those segments). Creating versions of Power User Curve by various user segments can also be particularly insightful. For instance, for a business with local network effects (like Uber or Thumbtack), showing Power User Curves by market can reveal which geographies are developing density and strong network effects. Power User Curves show if your product is hitting a nerve among a super engaged core group of users, even if perhaps the overall blended DAU/MAU is low. It also doesn’t have to just reflect app opens or logins — you can hone in on an action that maps closely to users getting specific value out of your specific product and plot the Power User Curve for that action. The key for founders is to know that there isn’t a single silver bullet to measure perfect engagement — rather, the goal is to find the set of metrics that are appropriate for their businesses. Comparing the Power User Curve of a social app vs. a work collaboration app doesn’t make sense, but looking at your own Power User Curve over time, or finding benchmarks for your product category, can tell you what’s working… and what’s not.

  • Death from paid marketing

    Many of the biggest implosions in recent history – especially eCommerce – have been due to startups getting addicted to paid marketing while fooling themselves on Customer Acquisition Costs. As spend scales, it always gets more expensive and harder to track – never less. A familiar story: New product launches. Nice spike, but it dies down. The product is low freq – gotta spend to grow. Marketing spend increases, it’s profitable! More is spent, more money is raised via VCs. OMG this is working! Party! Suddenly top line hits a ceiling. Payback period goes from 9 months to 12, then more. Unit economic profitable, but not with staff + HQ. Without top line growth, more investment dollars can’t be raised. Budgets get slashed, then layoffs. Even slower growth means a pivot is in order. Try something else, also powered by paid marketing. Maybe subscription? Premium? Try another thing. Then another. Irrelevance – or maybe bankruptcy. This happens enough that y’all should be nodding your heads now – it’s tough, but there’s a pattern. This is the Paid Marketing Local Max. The key insight here is that Paid Marketing is tricky to grow, at scale, as the primary channel. It’s highly dependent on both against external forces – competition and platform – as well as the leadership team’s psychology when things get unsustainable. The first mistake is to start by thinking of everything as Blended CAC – dividing all your acquisition against dollars – as opposed to understanding CAC of each channel (Facebook, Google display, Google AdWords, etc.). The former is misleading. Because your initial organic users are your biggest fans, your Blended CAC and per-channel CAC can often by off by 2-5X. As you scale your paid, your organic won’t follow 1:1. So as you grow, your Blended will approach your dominant channel’s CAC. Scale effects mostly work against you in paid marketing. The longer your campaigns run, the less effective they become – people start seeing your ads too often. The messaging becomes stale, and novelty effects are real. Market performance has a reversion to the mean. Saturation is also a thing. As you buy up your core demographic, the extra volume comes from non-core, who are less responsive. The first US-based ad impression on a property is the most responsive, but you eventually run out of those. Competitive dynamics are real. They’ll come in to copy not just your product, but also ad messaging and creative. It’s not hard to fast follow, especially if you can start the test just with a experiments on millennial-friendly ad copy and landing pages. Contrast that to viral channels, folder sharing in Dropbox or team channel creation for Slack – these are highly situational and only a few folks can copy. Whereas in ads you’re competing with everyone going after your same demographic. Addiction to paid marketing can get you into a local maximum. It’s much harder to fix the underlying issues – creating real moats, product differentiation, doing deeper adtech integrations. Easier to just spend more and push the LTV window from 9 months to 12 to 18. There’s a few scenarios where paid marketing is justified, but it’s situational. If your product has network effects that kick in after an activation point and really scale, you can use paid to help bootstrap that. Facebook uses paid to build out new regions, for example. If you are really going to invest a ton of time from engineering/growth to integrate with all the APIs, try out a ton of things algorithmically, then you can develop a lasting edge. I’ve heard Wish does this well, but it’s not common. The new generation of ad platforms makes it possible to scale revenue to new heights, but without profitability. Make sure you don’t get addicted. Build out new channels. Fix churn and frequency. Don’t congratulate yourself too early. And calculate LTV/CAC correctly :) So what do you do about it? One of the best case studies of this is from @drewhouston’s Dropbox presentation from the early days. Lots of great stuff in this deck and it’s worth paging through, now nearly 10 years later. Here it is. On slide 18, Drew talks about early experiments they did on paid search. They executed the industry best practices at the time – go to trial-based pricing, hide the free option, optimize landing pages. Slide: What they learned was that, in the mature market for cloud storage, there was already a lot of competition. All the paid marketing channels were unprofitable. Hiding the free option wasn’t user aligned. Etc etc. The obvious move would have been to continue to grind on the problem! Tweak pricing, optimize more ads/funnel/landing pages, etc. And many would have been tempted to do that, because it’s worked for others The interesting thing, and you can see in the deck, is that grew virally instead – via folder sharing, the give/get disk space program, etc. It seems obvious now, remember that back in the day, “cloud storage” was the space, and it’s not clear that you can go viral there. Dropbox has done well since then, of course! As an aside, isn’t it interesting that exponential growth curves always look linear instead? Here’s Slack’s as well: In some ways, you could argue that Dropbox is lucky that their initial forays into paid marketing didn’t work. That made it easier for them to stop their efforts there, and to focus on the viral channels that are now their bread and butter. On the other hand, it takes a lot of insight and reflection to go away from the current industry “best practices” – even if they erode profitability, cause shark fins, etc. So for those of you who are thinking about going all-in on paid marketing, I challenge you to go deeper on that strategy. Perhaps cap your paid acquisition at 30-40% of TOF. Instead, where can you innovate? In addition to Dropbox, I sometimes use the story of @Barkbox, which created a whole media property, Barkpost (http://barkpost.com ) as a viral content sharing engine that can cross-sell the subscription product. Or at Uber, although they never became significant channels, we were keen to work on sharing viral sharing features like Share ETA, Fare Split, and Location Sharing to potentially drive acquisition. The point is, knowing that Paid Marketing is highly addictive and hard to scale down, all of us in the industry should always be thinking about the 2nd or 3rd channel, in addition to organic/WOM, to give us a way to wean off an ever-increasing ad budget. To do that, you’ll need empower your creative team to attack the problem from all angles- new viral product features, really investing in your referral program, building out your content/SEO strategy even though it’ll take years. It’s worth the investment!

  • Conservation of Intent

    When a +10% isn’t really a +10% OK, this is an infuriating startup experience: You ship an experiment that’s +10% in your conversion funnel. Then your revenue/installs/whatever goes up by +10% right? Wrong :( Turns out usually it goes up a little bit, or maybe not at all. Why is that? Let’s call this the “Conservation of Intent” (Inspired by the Law of the Conservation of Momentum ) The difference between high- and low-intent users For all your users coming in, only some of them are high-intent. It’s hard to increase that intent just by making a couple steps easier – that’ll just grow your low-intent users. Doing tactical things like moving buttons above the fold, optimizing headlines, removing form fields – those are great, but the increases won’t directly drop to your bottom line. In other words, the total amount of intent in your system is fixed. Thus the law of the conservation of intent! This is why you can’t add up your A/B test results If you’re at a company that A/B tests everything and then announces the great results – that’s wonderful, of course, but just run the thought experiment of summing together all of those A/B tests. And then look at your top-line results. Rarely does it match. The most obvious way to see this is to test something high up on a funnel, for example maybe the landing page where a new user hits, or an email that a re-engaged users opens – you can see that a big lift on the top of the funnel flows down unevenly. Each step of friction burns off the low-intent users that are flowing step-by-step. Be skeptical of internal results, but more importantly, external case studies too If you’re at a big company and another team publishes a test result, make sure you agree on the actual final metric you’re trying to impact – whether that’s revenue, highly engaged users, or something else. Make sure you always review that. Similarly, this is a reason to be skeptical of vendors and 3rd parties who have case studies that’ll increase your revenue by X just because they increase their ad conversion rate (or whatever) by X. In these kinds of misleading case studies – often presented at conferences – not only do vendors have the ability to only cherry pick the best examples that reinforce their case, but also the metric that’s highest impacted! Be skeptical and don’t be fooled. Unlock increases to the bottom line First, understand what’s really blocking your high-intent users. Those are the ones who’d like to flow all the way through the funnel, but can’t, for whatever reason. For Uber, that was things like payment methods, app quality (for Android especially!), the forgot password flow, etc. If you can’t pay or can’t get back into your account, then even if you use the app every day, you might switch to a different app that’s less of a pain in the ass. Also, you can focus your experiments. You obviously get real net incremental increases on conversion the further down the funnel you go. By that point, the low-intent folks have burned off. You’re closer to the bottom line. Look the steps right around your transaction flow – for ecommerce sites that might be the process to review your cart and add your shipping info, or the request invoice flow for SaaS products, etc. Think about high-intent scenarios, for example when you hit a paywall or run out of credits/disk space/resources/etc. All of these can be optimized and it’ll hit the bottom line quickly. Make sure your roadmap reflects reality When it comes to your product roadmapping, yes you can definitely brainstorm and ship a bunch of +10% increases, but you need to add a discount factor to your spreadsheets to reflect reality. Can’t just add up all your results. When you focus on low-intent folks, you’ll have to get creative to build their intent quickly. Things like being able to try out the product, having their friends into the product – these are the “activation” steps that generate intent. Conservation of Intent Many of you have directly experienced the “Conservation of Intent” but now you have a name for it! It’s tricky. This is really a reflection of how working on product growth is really a combo of psychology and data-driven product. You can’t just look at this stuff in a spreadsheet and assume that a lift in one place automatically cascades into the rest of the model.

  • The Startup Brand Fallacy: Why brand marketing is mostly useless for consumer startups

    Brand marketing is mostly useless for consumer startups. Startups build a great brand by being successful, finding product market fit and scaling traction, etc. But it’s not a real lever. Let’s not mix up correlation with causation! If this seems contrarian to you, it’s because there’s a vast ecosystem of consultants, agencies, and other middlemen who are highly incentivised to have you spend $ and effort on non-ROI/non-performant activities. Early startups should opt out of all of this It’s easy to confuse correlation and causation: If you’re starting a consumer startup, you see successful late stage cos with fawning media coverage, amazing conference speaking slots, celebrities on the cap table, etc., and think that’s what caused their success: Great brand. But great brand is the lagging indicator of success. The buzz is created by the hard work that the entrepreneurs put in: Finding product/market fit, hiring a great core team, finding acquisition channels that scale. Brand marketing is great, but it should be layered on later. The greatest consumer products in recent years slogged through years of obscurity. The overnight success of Uber, Airbnb, Instagram, etc were actually multi-year successes driven by hard work and multiple pivots. Working on press mentions, conferences, etc can be a good way to get an initial hit of traffic. It’s great! But it’s not enough. Anyone who’s been on the homepage of TechCrunch, AngelList, Hacker News, or even in the NYTimes knows that it’s an increase to your dopamine but not so much your customer acquisition :) It’s great for the early days, but you need a lot more to scale. Furthermore, the metrics-driven argument is obvious. Ultimately, the engagement in every product can be deconstructed into a series of user cohorts that join and decay over time. How does brand help these cohorts? My observation: They don’t help much. One argument is that brand marketing can create buzz and word of mouth. OK if that’s the case, why does every brand-driven commerce company have >60% of their customer acquisition happen through paid marketing? Why do they have to buy all their customers? If brand marketing helps make acquisition ultimately cheaper, then why does every startup’s paid acquisition become less efficient over time, even as the company becomes more well known? The same arguments apply to startups’ re-engagement efforts. It’s true that a strong brand can confer defensibility in a noisy space – but it’s brittle, hard to create, and hard to sustain. Hard to bet on that in the early days of a startup. Where brand marketing does matter, especially outside of consumer: Recruiting a great team. Raising money. Partnerships. These are all small targeted audiences where you can reach them with more touchy feely efforts, and it can work! So put your emphasis there. For early consumer startup efforts, it’s better to focus on the basics. Understand your users, deliver a great product to the market that grows by itself, built moats, monetize in a user-aligned way. Grow your team, work with the best advisors/investors/etc. The basics. Do all that, and your product’s brand will take care of itself – and then you can layer on more brand marketing efforts to 10x the effect. Just don’t do the steps out of order!

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

    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: A feedback loop that rewards content posters when they push new content into the network A feedback loop that rewards passive content consumers with relevant and valuable content 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 let's 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. 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.

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