Jan 2, 2019 · 32m · a16z
a16z Podcast | The Basics of Growth 2 -- Engagement & Retention
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode of the a16z Podcast, host Sonal Chokshi and general partners Andrew Chen and Jeff Jordan examine key engagement and retention frameworks, including cohort analysis, power user curves, onboarding magic moments, and network effects. The discussion offers founders actionable insights on transitioning from user acquisition to long-term product stickiness.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The host holds 15.5% of the talking time here. How this is scored →
The host as informed peer 2.2 Guest teaching 3.2 Guest disagreement 0.3 The host pushing back 0.4
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 23:14 Jeff gently corrects Andrew's model premise
Jeff interjects to refine Andrew's mental model on restaurant conversion rates, pointing out that raw headcount fails without benchmarking against the local market's restaurant universe.
Hardest push from the host ▶ 8:25 Sonal questions the 'aha moment' premiseSonal pushes back on the concept of a discrete magic onboarding moment, arguing that engagement for mature products is often cumulative rather than instantaneous.
Biggest teaching moment ▶ 20:25 Andrew explains counterintuitive DAU/MAU mathAndrew educates the host on growth mechanics by showing how sending re-engagement emails often brings back casual users, raising MAU faster than DAU and lowering the overall ratio.
The host holds their own ▶ 27:37 Sonal frames attention competition dynamicsSonal asserts strong sector expertise by expanding the guest's discussion on content competition to include dating apps like Tinder and micro-waiting periods.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Cohort Evolution and the Shift from Acquisition to Engagement | 2 | 4 | 0 | 0 | Sonal prompts the guests on cohort dynamics and interjects with brief personal anecdotes. Andrew and Jeff explain how user acquisition naturally degrades over time, forcing companies to shift focus toward engagement and retention. | |
| Cohort Curves in Marketplace and Network Effects Businesses | 1 | 3 | 0 | 0 | Jeff explains how cohort curves reveal network effects in marketplace businesses like OpenTable. Andrew adds details on geography and team size segmentation while Sonal asks simple clarifying questions. | |
| Identifying Product 'Aha Moments' and 'Magic Numbers' | 3 | 4 | 1 | 2 | Sonal challenges the assumption of a single 'aha moment', questioning if engagement is instead cumulative over time. Andrew reframes her premise by explaining how existing network density makes initial onboarding moments more potent. | |
| Transitions in Company Focus and the Engagement Ladder | 3 | 3 | 0 | 1 | Sonal draws a parallel to SaaS upsell dynamics, prompting Jeff and Andrew to discuss how mature consumer companies transition from acquisition to activation. Andrew introduces the concept of the engagement ladder using Dropbox as an example. | |
| Tactics for Escalating User Engagement and Understanding Journeys | 1 | 3 | 0 | 0 | Andrew details tactics for escalating user engagement including notifications and incentives. Jeff contrasts high-frequency platforms like eBay with episodic services like Airbnb and OpenTable. | |
| Engagement Metrics: DAU/MAU Ratios and the Power User 'Smile' Curve | 3 | 3 | 0 | 0 | Sonal highlights Andrew's prior published writing on the DAU/MAU metric to guide the discussion. Andrew and Jeff explain DAU/MAU limitations, L28 histograms, and the power user 'smile' curve. | |
| The Difficulty of Gaming Engagement Versus Acquisition | 1 | 4 | 1 | 0 | Andrew explains why engagement metrics like DAU/MAU are resilient against growth hacking compared to raw acquisition metrics. He surprises the host by demonstrating how push notification spams can counterintuitively reduce DAU/MAU ratios. | |
| Evaluating Network Effects as Continuous Curves with Diminishing Returns | 2 | 3 | 1 | 0 | Andrew and Jeff explain that network effects operate on a continuous curve rather than a binary switch. Jeff politely refines Andrew's hypothetical model by clarifying that restaurant density must be measured as a percentage of the market universe. | |
| Distinguishing Between User Engagement and Long-Term Retention | 4 | 2 | 0 | 1 | Sonal actively contributes to the analysis of user attention, pointing out that media products compete broadly with non-traditional attention sinks like Tinder and micro-waiting moments. Andrew and Jeff elaborate on engagement versus retention differences. | |
| Retention Frameworks for Episodic Products and Upstream Signals | 2 | 3 | 0 | 0 | Andrew explains upstream signals for episodic products like Zillow and OpenTable where transactions are rare. Jeff concludes by emphasizing data-driven iteration and the primacy of engagement over raw acquisition growth. |
Statements from this episode (28)
Jordan: Most American women have downloaded the Pinterest mobile app
“Most women in America have downloaded the Pinterest app.”
Jordan: Startups can hack user acquisition but not true product engagement
“You can often hack your way into new users. It's really hard to hack your way into true engagement.”
Chen: Elite product retention curves eventually swing back upward
“For the good ones, They start to flatten out and they plateau, and then for the really good ones, they'll actually swing back up and, you know, people will come back to the service.”
Chen: Scaling startups shift focus from acquisition to retention, then resurrection
“The evolution of most of these companies, as they're getting bigger, tends to start with acquisition, Then focus much more on, you know, churn and retention, and then ultimately also to layer in resurrection as well.”
Jordan: The best marketplace business models rely on cohort curve analysis
“Usually in marketplace businesses, the best models are built off of the cohort curves.”
Jordan: Newer cohorts perform better than earlier ones in network-effect businesses
“If it gets more valuable, your newer cohorts should behave better than your early cohorts.”
Chen: Geographic cohort segmentation acts as a natural A/B test
“For a bunch of hyperlocal type businesses, the reason why segmenting it based on market geography, why that's so valuable, is because then you can compare markets against each other. You can say, well, you know, this market, which has much more density in term…”
Jordan: Enterprise software usage curves shift when adopted organization-wide
“You know, just if you have five people in the organization using Slack, you get one use curve. If you have, you know, if the organization, it's the operating system for the organization, you have a very different curve.”
Jordan: US content is insufficient for Pinterest's international expansion
“Pinterest, when it goes to a new market, first of all, they figured out they need a lot of local Capitalized content to make it compelling to local users. The U.S. Corpus of images doesn't necessarily, is helpful in international markets, but isn't sufficient.”
Jordan: Facebook has completely saturated its US user growth
“Facebook Hasn't had any users in the U.S. Forever because they have them all.”
Chen: Products must move users up an engagement ladder to daily habits
“One of the first things that you figure out is that each one of these products actually has this, like, ladder of engagement where oftentimes new users show up to do something that's valuable but, you know, potentially infrequent, and you need to actually leve…”
Chen: Dropbox maximizes user value through workplace folder sharing
“For example, when you first install Dropbox, the easiest thing that you can do is you can use it to just sync your Home and your work computers, right? And that's great, but really the way to get those users to become really valuable is for them to start shari…”
Jordan: Early eBay users spent hours browsing passion-driven collectibles
“EBay had enormous levels of engagement early on. For a commerce app in particular, people would spend hours just browsing, because early on it was about collectibles, and it was about people's passions”
Jordan: OpenTable's median user dined only twice per year
“OpenTable and Airbnb are both typically much more episodic. Most people don't dine at fine dining restaurants with high frequency. Our median user dine twice a year on OpenTable, and so that has completely different marketing implications and user implications…”
Chen: Facebook historically maintained a 60% DAU/MAU engagement ratio
“Their products have historically been 60% plus daily actives over monthly actives, and that's very high.”
Chen: Daily-use consumer products with DAU/MAU under 15% will likely fail
“If you think that your product is a daily use product, and you're going to monetize using, you know, a little bit of money that you're making over a long period of time, but your DaoMao is low, is like sub 15%, then like, it's probably not going to work.”
Jordan: OfferUp user engagement rivals social platforms like Instagram and Snapchat
“OfferUp has engagement that's similar to social sites like Instagram and Snap.”
Chen: Extra push notifications can inadvertently lower DAU/MAU ratios
“The challenging thing is actually usually sending out more notifications will actually cause more of your casual users to show up, because your hardcore users were already kind of showing up, you know, already. And what that does is that'll increase your month…”
Jordan: Early Facebook platform apps grew fast but had zero retention
“Early on in the Facebook platform, companies literally got to a million users in, you know, it felt like minutes. Just because there were so many people on Facebook and the ones who were early just got exploding user bases. There were a number of concepts whos…”
Jordan: OpenTable's San Francisco diligence proved network effects improved key metrics
“My diligence at OpenTable was I looked at San Francisco, which was their first market, and sales rep productivity grew over time. Restaurant churn decreased over time. The number of diners per restaurant increased over time. The percentage that booked through …”
Chen: Network effects exist on a continuous curve, not a binary state
“When you look at the data, what you really figure out is network effect is actually like a curve. And it's not like a binary yes, no kind of thing.”
Chen: Cutting ride-share wait times below five minutes does not boost conversion
“So for example, in Rideshare, if you are going to get a car called 15 minutes versus 10 minutes, that's very meaningful. But if it's, you know, five minutes versus two minutes, your conversion rate doesn't actually go up.”
Chen: Weather apps have low usage frequency but high retention
“So weather is low frequency, but high retention. Because, like, you're actually gonna need to know what the weather is.”
Chen: E-books and games drive high engagement but poor retention
“Versus if you look at something like games or ebooks or, you know, those kinds of products, like really high engagement because you're like, all right, I'm going to get to, I'm going to finish this like trashy science fiction novel that I've been reading. I'm …”
Chen: Products must excel in retention, frequency, or monetization
“That's retention versus frequency versus monetization. And I think you gotta to be like really good at least on one of those axes.”
Chen: Modern consumer apps must steal time from existing products
“Well, because it used to be that you were, you know, what kind of time were you competing for in the first couple of years of the smartphone? You were competing against literally, I'm going to stare at the back of this person's head Or I can, like, use some co…”
Chen: High time spent on Google Search indicates poor performance
“And Google is the best example of this, right? In fact, if you spend a lot of time on google.com, you know, refining your searches and clicking around, that means actually the service is doing poorly.”
Chen: Low-frequency platforms should track upstream engagement signals, not transactions
“One of the things that companies can often do is to measure upstream signal. So for example, Zillow, you're probably not going to buy a house very often, right? Maybe, you know, a couple times in your life. However, what's really interesting is they can say, w…”