Hi there! Thanks for reading this - I originally published this deck with the note below on my blog - you can read the details here: https://andrewchen.co/investor-metrics-deck/ Hope you enjoy :) Andrew San Francisco, CA -- Earlier this year, I joined Andreessen Horowitz as a General Partner, where I focus on a broad spectrum of consumer startups: marketplaces, entertainment/media, and social platforms. This was a big moment for me, and the result of a long relationship that began a decade ago, when Horowitz Andreessen Angel Fund funded a (now defunct) startup I had co-founded. One of the reasons I've been excited about being a professional investor is the ability to apply my skills as an operator. The same skills needed to grow new products can be used both to evaluate new startups to invest in, and once we've invested; to help them grow. The reason for this is that the steps for starting and scaling a new startup share many of the same skills as investing in a new startup: 1) First, we seek to understand the existing state of customer growth including growth loops, the quality of acquisition, engagement, churn, and monetization. 2) Then, to identify potential upside based learnings from within the company as well as across benchmarks from across industry. 3) And finally, to prioritize and make decisions that impact the future. Of course, as an investor you can't run A/B tests or analyze results directly, but you can form hypotheses, ideate, and apply the same type of thinking. As part of my interview process at a16z, I eventually put together an 80 slide deck on how to use growth ideas to evaluate startups. In the spirit that this perspective can help others in the ecosystem, and to share my my thinking, I'm excited to publish the deck below. Disclaimer: This was just one presentation in a 10 year relationship But before I fully share, I have a disclaimer. This is one presentation I made within a series of dozens of meetings and interactions I had with the Andreessen Horowitz team. It was just one ingredient. I've been asked by friends and folks on the best path into venture capital. From my experience, it's a long, windy experience others have written about their processes as well. My journey took a while too: 10 years in the Bay Area (and blogging, building my network, etc) Dozens of angel investments and advisory roles in SaaS, marketplaces, etc Once kicked off, 6 months of interviews (dinners, sitting in pitches, analyzing startups) 100+ hours of interviewing and prep This deck was just one step, but one that I'm proud of, and want to show y'all. Growth Hacking for Startups / Venture Capital Andrew Chen Prepared for Andreessen Horowitz "growth hacking" 0 results "Growth Hacker is the new VP Marketing" Published April 2012 Years later, the Growth function has taken off across the industry... Growth has evolved: Facebook app growth Email address book virality Hacks, tips, and tricks into a much bigger umbrella "growth team" "new user experience" "user engagement metrics" "customer acquisition cost" 660+ essays over 10 years Selection of work How To (Actually) Calculate CAC What does a growth team work on? What's your viral loop? When and why do apps "jump the shark?" Losing 80% of mobile users is normal 60% of users opt-out of push notifications What factors drive DAU/MAU? Audience 100k email/RSS subscribers 110k Twitter followers Front page of YC Hacker News Front page of Google: growth hacking product market fit user growth growth team customer acquisition cost user engagement metrics viral loops viral coefficient push notification click through rate ... Next step: Pulling together the top-end of the professional network Week 1 Growth Models Week 2 Channels and Loops Week 3 Growth Roadmap Week 4 Engagement and Retention Week 5 Viral Growth Week 6 Paid Marketing Week 7 Growth Teams Week 8 Future of Growth Growth professional education: 8-Week Agenda 1500+ mid-career professionals from across tech Personal goals for the future: Stay ahead of the curve Bring people and networks together Put more ideas on paper :) Let's use frameworks to assess startup growth! Growth Frameworks + Startups Questions Is it working? Will it sustain? Can it improve? + New+Reactivated 5,000 - Inactive 1,000 = Net MAU 4,000 + Engaged 10,000 = MAU 14,000 The Growth Accounting Framework New+Reactivated Tends to look linear or S curve Only a few loops scale: Virality, Paid, SEO Most lack a strong reactivation strategy Inactive Also an S-curve, but lags MAUs % of Active Users Sometimes hard churn, usually lack of frequent use case Very hard to move this curve! New + Reactivated - Inactive = 0 Peak MAUs Problem: Growth Accounting gives lagging metrics It's just the current snapshot! You wouldn't invest based on a startup's P&L, Balance Sheet, etc. Same with Growth Accounting - it talks outputs but not inputs Not actionable for product teams +New +Reactivated -Inactive Engaged Acquisition Loop Engagement Loop Leading indicators, not lagging Start with the product's Acquisition and Engagement Loops How defensible/proprietary? How scalable and repeatable? How much upside? We need to understand the quality of growth Use Loops to drive the discussion, not Growth Accounting User Acquisition Loop Examples of Acquisition Loops Acquisition Loop Metrics Acquisition Loop Improvements Applying the Frameworks Examples of Acquisition Loops Acquisition Loop Metrics Acquisition Loop Improvements Applying the Frameworks Acquisition Loop How does a cohort of new users lead to more new users? Durable, scalable sources of new users are built on loops Loops are built on existing platforms and networks (Buy / Build / Partner) Acquisition Loop: User generated content + SEO New users find content % signup and create content Google indexes unique content People search for content Acquisition Loop: Paid marketing New users click an ad % sign up to try product % convert to paid features Budget used to buy ads Acquisition Loop: Viral New users sign up Invites / content sent to friends Some % click on the links Engage with invite/content Measuring loops - and why there's a multiplier New users sign up % import their contacts Invites links sent Some % click on the links 30% 10X 40% 50% Growth Factor = 0.6 1000 signups create +666 more Other loops are the same: Spend $5 CAC Get $10 in profit Write 1 wiki page Get 1.5 pages written Etc Linear channels are helpful, but don't scale New users click an ad % sign up to try product % convert to paid features Budget used to buy ads PR Conferences Content mkting Partnerships App Store features (Aren't repeatable, don't scale!) Examples of Acquisition Loops Acquisition Loop Metrics Acquisition Loop Improvements Applying the Frameworks Paid Marketing Loop - in more detail New users click an ad % sign up on landing screen Budget used to buy ads % who install from App Store page % who mobile verify % who see an item detail page % who add item to cart % who checkout % who add payment % who complete purchase Paid Marketing Loop - in more detail New users click an ad % sign up on landing screen Budget used to buy ads % who install from App Store page % who mobile verify % who see an item detail page % who add item to cart % who checkout % who add payment % who complete purchase Example: App Ratings >50% dropoff at the App Store detail page #deleteuber 1.7 stars :( iOS 10.3 Inline app review modal rolled out 4.7 app rating (4.4 overall) *Public App Store data from Appbot New iOS 10.3 inline review modal Paid Marketing Loop - in more detail New users click an ad % sign up on landing screen Budget used to buy ads % who install from App Store page % who mobile verify % who see an item detail page % who add item to cart % who checkout % who add payment % who complete purchase Example: Mobile Phone # Verification 10-40% dropoff at mobile phone # verification step Optimizations: Voice OTP Android auto-read SMS code Carrier direct verification (Danal) Partner<>Carrier deliverability tracking Paid Marketing Loop - in more detail New users click an ad % sign up on landing screen Budget used to buy ads % who install from App Store page % who mobile verify % who see an item detail page % who add item to cart % who checkout % who add payment % who complete purchase Example: Landing Page Optimization 80%+ dropoff on landing pages A/B testable best practices: Minimal design with strong CTA Remove unnecessary form fields Personalized (to add, inviter, etc.) Rich media, but no links! (carousel, GIF, video) Mobile optimized web Redirect to App Store when mobile Smart app banner META tag ... Examples of Acquisition Loops Acquisition Loop Metrics Acquisition Loop Improvements Applying the Frameworks Loops/Channels Mix Signups broken down by: Channels and Loops Time period Quality indicators Proprietary channels and loops Repeatable/scalable loops Low platform risk 33/33/33 on organic plus two loops Red flags New, unsustainable traffic Spiky acquisition - ads? Brittle dependence on one loop Loops that are actually linear channels Dec Jan Feb Mar Apr brand / Google 1,000 non-brand / Google 10,000 10,000 seo / Google 10,000 10,500 11,025 11,576 12,155 homepage / Organic 2,000 2,100 2,200 2,300 2,400 product hunt / Partner 25,000 2,000 1,000 500 Acquisition Loop Quality Sources broken down: Signups, Cost per Signup Activated, Activation %, CPAU LTV MoM increase % Quality indicators Steady growth on scalable loops $ spent on quality signups Steady growth with high activation Red flags Spikes of $ on low quality signups Signups without activation CPAU > CPSU > LTV Source Signups CPSU Activated CPAU Activation % LTV MoM brand / google non-brand / google display / facebook feed / facebook homepage / organic detail page / organic seo / google Platform Dependency Growth <> Platforms Loops depend on existing platforms Build/Buy/Partner Look at CTR and conversion rates on-platform over time Indicators Stable performance Durable real estate Proprietary Product<>Channel fit $1B versus $10B co - multiple loops Red flags Decreasing conversion rates Easily fast followed Unstable or shrinking platform Examples of Acquisition Loops Acquisition Loop Metrics Acquisition Loop Improvements Applying the Frameworks Applying our acquisition framework Baseline forecast Linear and non-linear Flat on linear channels Nonlinear projection for loops Project out to the next financing MAU and Activated, not just signups! Dec Jan Feb Mar Apr brand / Google 1,000 non-brand / Google 10,000 10,000 seo / Google 10,000 10,500 11,025 11,576 12,155 homepage / Organic 2,000 2,100 2,200 2,300 2,400 product hunt / Partner 25,000 2,000 1,000 500 Bottoms Up Roadmap Factor in improvements Bottoms-up roadmap to next funding round +5%-10% for minor upgrades +25-50% for new loops Share this with the entrepreneurs afterwards! Iterate the forecast Rebuild forecast + risk analysis Build into Net MAU model Need Engagement Loop too... Qualitative risks Platform + competitive risks What if a loop goes to zero? Can this team execute? but Acquisition's only half the picture! Let's talk about the Engagement loop User Engagement Loop Examples of Engagement Loops Engagement Loop Metrics Engagement Loop Improvements Applying the Frameworks Examples of Engagement Loops Engagement Loop Metrics Engagement Loop Improvements Applying the Frameworks Engagement Loop For social apps: How does one engaged user cause another to be more engaged? For utility apps: How does engagement in an app cause future engagement? Prefer organic, natural use cases over notifications, which should just be an accelerant Engagement Loop: Social feedback User creates content Content viewed by other users Social feedback given on content Notification sent to content creator Engagement Loop: Personalized content Users subscribe / add New content comes up Users are alerted by notification or feed Users view and like content Linear channels for engagement User creates content Content viewed by other users Social feedback given on content Notification sent to content creator PR Promotions Holiday New features ... (Aren't repeatable, don't scale!) Examples of Engagement Loops Engagement Loop Metrics Engagement Loop Improvements Applying the Frameworks Social Feedback Loop - in more detail Notif opened and CTR User creates content Notif sent to content creator User views notif User hits publish # of connections Notifs sent with new content Notif opened and clicked through Recipient logs into website Recipient gives feedback on content Social Feedback Loop - in more detail Notif opened and CTR User creates content Notif sent to content creator User views notif User hits publish # of connections Notifs sent with new content Notif opened and clicked through Recipient logs into website Recipient gives feedback on content Kicking off the engagement loop Activation bends the cohort curve early Examples from Pinterest's growth team: "Save" instead of "Pin It" Interstitial from Mobile web to app Pin Education Simplifying Home Feed +100% lift to activation (Weekly repinners / Signups) Social Feedback Loop - in more detail Notif opened and CTR User creates content Notif sent to content creator User views notif User hits publish # of connections Notifs sent with new content Notif opened and clicked through Recipient logs into website Recipient gives feedback on content Building social graphs Social feedback loops are dependent on a network Bootstrap on a pre-existing graph: "Find friends" 30% will sign in via FB/Google 95% connection rate by layering value props Other proven tactics: Reverse addressbook lookup "Your friend X just joined - add them?" People You May Know Post-content creation sharing prompt After a connection, cross-sell to adding more - build a loop Red flags Viral factor > 1 based on spam Select all on contacts, SMS spam, No skip buttons Engagement that's all connecting, no core actions Social Feedback Loop - in more detail Notif opened and CTR User creates content Notif sent to content creator User views notif User hits publish # of connections Notifs sent with new content Notif opened and clicked through Recipient logs into website Recipient gives feedback on content Inactive users trying to engage Reactivating users is a huge non-obvious opportunity for late stage startups ~50-75% of registered users are inactive They face challenges with trying to engage: Need to reinstall app Forgot their username or password Using a new phone # Solutions Treat "Forgot password" as a key funnel "Looks like you're trying to login" email Android Smartlock, iCloud keychain 1-click sign-in for app constellations (EATS/Uber) Active users engaging inactive users Cross-sell login when a user's signing up with an existing email/username Examples of Engagement Loops Engagement Loop Metrics Engagement Loop Improvements Applying the Frameworks Cohort Retention Loops versus Linear Don't want one-time spikes in retention What value is created on each visit? Are the loops improving over time? Actions, not just sessions Not just a session! D1/D7/D30 and month-to-month Segment by acq source, geo, etc What I'm looking for: Active/Reg flattens out to >20% TAM * 2y cohort * ARPU Inactives aren't viral! Key to acquisition too Notifications analysis List of notifications Volume and how they're triggered Company initiated versus UGC CTR decay over time Quick spam check - "[company] spam" on Twitter, Google, Reddit Notifications are key to many engagement loops Notifications as accelerant, not foundational High organic versus notification-triggered visits Stable CTRs >25% Platform dynamics Upside in adding more personalized channels Frequency and Loops Frequency segmentation Channel and Time period DAU/MAU Multiple engagement loops Users often come for one, stay for another High frequency, strong loops are social versus lower-frequency utility "Come for the tool, stay for the network" What I'm looking for: Frequency that matches value prop Actions/Use cases for HFU vs LFU Key attributes (Geo, age, etc.) Upside opportunity to upsell/cross-sell to move metrics Examples of Engagement Loops Engagement Loop Metrics Engagement Loop Improvements Applying the Frameworks Baseline Forecast Prior work on Acquisition loops plugs in! Acquisition <> Engagement <> MAU Acquisition drives cohort Day 0 Activated users, not signups! Use MAU / Activated Forecast MAUs MAU based on retention curves 2y Signups * Activated/Signups * % Retained No credit for notif-driven spikes :) Bottoms Up Roadmap Retention curves are hard to move! Never seen a leaky product fix itself by adding more notifications :) Biggest levers Activating new users to engage Adjusting mix of High Freq vs Med Freq Usage intensity - Actions/MAU Adjust cohort curves to reflect potential improvements +New +Reactivated -Inactive Engaged Acquisition Loop Engagement Loop Now we understand both loops - these are the inputs... which means we understand and trust the outputs We can have a granular, data-driven analysis of risks... Quality? Risks? Upside? + New + Reactivation - Inactivity Engagement and answer the questions that matter! Is it working? Will it sustain? Can it improve?