Link of the Day: Wisereads Vol. 108 — Alex McCann on the death of the corporate job, Wuthering Heights, and more
The title of this link is a little misleading. The real lede here is buried in the “Twitter Thread of the Week: WHY RETENTION IS SO HARD FOR NEW TECH PRODUCTS" section by Andrew Chen. I am always looking for shortcuts, key principles that can help me cut through the clutter and see things others miss. In this article, Chen lays out what I think most people (including me) miss about tech products in general and networked products in particular: retention is everything.
We understand this concept when we see it physically. The flex seal guy showed us all how important it is that a boat retains water. Yet we struggle to see it in tech, especially viral products. It does not matter how many new users you add if you cannot retain them. What I find interesting about this concept is that it means some of the nefarious tactics marketers use are not only annoying, they are ineffective. Spamming people with emails and texts if your product is not sticky does nothing. You may briefly see a surge of users but if your product isn’t great, it’s game over.
Yet retention is not synonymous with product quality, especially with networked products. Most of us do not use Facebook because it is an incredible app, but because we were required as a result of its ubiquity.
So there’s a couple of core truths here:
- Viral growth is less useful than we imagine it to be, at least in the absence of good retention
- Good retention does not require a good product. Many of us use Jira daily. That does not mean Jira is good.
These core truths remind me of Good Strategy, Bad Strategy. Chen here basically lays out the key strategic pieces of retention. There appear to be certain laws to retention that determine what does and doesn’t work.
They are counterintuitive. Who would guess that it’s easier to acquire a new user than to re-activate an old one? Who would guess that your best users are your first ones and that retention falls as you add new users?
Without knowing these core facts you can either freak out about an inevitability (falling retention) or ignore an emergency (high growth, bad retention). You may also try to chase retention where it will never appear. A dating app will never have great retention, you need to rely on other strategies to achieve profitability.
It feels like knowing these laws at the outset can save you a lot of pain and stress. Without further ado, here is the high level of the laws below. If you want to learn more, dig into the thread or jump into the Cold Start Problem.
The title of this link is a little misleading. The real lede here is buried in the “Twitter Thread of the Week: WHY RETENTION IS SO HARD FOR NEW TECH PRODUCTS" section by Andrew Chen. I am always looking for shortcuts, key principles that can help me cut through the clutter and see things others miss. In this article, Chen lays out what I think most people (including me) miss about tech products in general and networked products in particular: retention is everything.
We understand this concept when we see it physically. The flex seal guy showed us all how important it is that a boat retains water. Yet we struggle to see it in tech, especially viral products. It does not matter how many new users you add if you cannot retain them. What I find interesting about this concept is that it means some of the nefarious tactics marketers use are not only annoying, they are ineffective. Spamming people with emails and texts if your product is not sticky does nothing. You may briefly see a surge of users but if your product isn’t great, it’s game over.
Yet retention is not synonymous with product quality, especially with networked products. Most of us do not use Facebook because it is an incredible app, but because we were required as a result of its ubiquity.
So there’s a couple of core truths here:
- Viral growth is less useful than we imagine it to be, at least in the absence of good retention
- Good retention does not require a good product. Many of us use Jira daily. That does not mean Jira is good.
These core truths remind me of Good Strategy, Bad Strategy. Chen here basically lays out the key strategic pieces of retention. There appear to be certain laws to retention that determine what does and doesn’t work.
They are counterintuitive. Who would guess that it’s easier to acquire a new user than to re-activate an old one? Who would guess that your best users are your first ones and that retention falls as you add new users?
Without knowing these core facts you can either freak out about an inevitability (falling retention) or ignore an emergency (high growth, bad retention). You may also try to chase retention where it will never appear. A dating app will never have great retention, you need to rely on other strategies to achieve profitability.
It feels like knowing these laws at the outset can save you a lot of pain and stress. Without further ado, here is the high level of the laws below. If you want to learn more, dig into the thread or jump into the Cold Start Problem.
- You can’t fix bad retention. No, adding more notifications will not fix your retention curve. You can’t A/B test your way to good retention
- Retention goes down, it doesn’t go up. And weirdly, it decays (oh, does it decay) at a predictable half life. Early retention predicts later retention
- Revenue retention expands, while usage retention shrinks. Good news: You lose people over over time, but the ones that remain sometimes spend more more money!
- Retention is relative to your product category. There’s nature, and there’s nurture. Sorry, you’ll never make a hotel booking app a daily use product
- Retention gets worse as users expand and grow. The best users are early and organic. The worst users come after that
- Churn is asymmetric. It’s far easier to lose a user forever than to re-win them back
- Retention is weirdly hard to measure. Seasonality is a real thing. New tests throw things off. Bugs happen. D365 is a real metric but you can’t wait
- Crazy viral growth with shitty retention fails. We’ve run this experiment many many times already, across multiple platforms and categories