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Why you should make it easy for users to quit your product
Don’t worry, I’m not a hippie
From the title of this blog post, you might think that I’m going to make a touchy-feely argument about why you should respect the right of your users to do all the terrible things that every entrepreneur fears:
- delete their accounts
- unsubscribe from email lists
- cancel their subscriptions
- uninstall their apps
… but you’d be wrong.
In fact, I’m going to argue that for every early product out in the market, making it really easy to quit is completely aligned with self-interested thinking. I’ll make the assumption that all the entrepreneurs reading this post are greedy, self-interested individuals, and target the appeal straight into your dark hearts 
My central argument is that if you believe that every startup is an iterative learning process that converges towards product/market fit, then you need extremely high-fidelity signals to tell you if you’re going in the right direction. That means that along with trying to charge people money from early on, which is the highest form of “I love this!” you should give people valves to tell you “I hate this!” so that you can learn more faster.
Let’s drive into this further…
Product/market fit
There’s a notion of product/market fit that Marc Andreessen references in his blog, and he calls it the “only thing that matters” and says that every startup should do everything they can to get to this point. Let’s see what he writes:
The only thing that matters is getting to product/market fit.
Product/market fit means being in a good market with a product that can satisfy that market.
… and Marc continues:
Lots of startups fail before product/market fit ever happens.
My contention, in fact, is that they fail because they never get to product/market fit.
Carried a step further, I believe that the life of any startup can be divided into two parts: before product/market fit (call this “BPMF”) and after product/market fit(“APMF”).
When you are BPMF, focus obsessively on getting to product/market fit.
Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital — whatever is required.
When you get right down to it, you can ignore almost everything else.
If you believe what he says, that gives you a pretty firm set of marching orders. And for early products on the market, getting to to this point in which your product is good enough and the market is compelling enough is a tough slog. So the question is, how do you navigate your way to product/market fit?
At the heart of every startup is a learning loop
For the idea that every startup is inherently a learning machine, we can turn to two of my favorite startup people, Steve Blank and Eric Ries. Eric has blogged in a lot of detail about how he believes that inside of every startup is an OODA loop that involves trying stuff out, learning, and trying more stuff again. And of course a lot of these ideas are built off of Steve Blank’s Customer Development framework that I’d encourage my readers to look into as well.
In this light, to combine the two ideas: Every startup is a series of iterative experiments that gets you from zero to product/market fit, and if you can do it before running out of money, then you might get rich
And the decision-making process in this approach is totally different. In most product strategy conversations I’ve been involved in, the most heated debates center around whether a particular product will work, and all the pros and cons of the situation. Contrast this to a learning-centric approach, which emphasizes whether or not experimenting with an idea will yield insights, and how much it’ll cost to learn these insights.
In other words, you’re much more likely to try things that will fail, if those failures teach you something important about the market.
Of course, all of the decisions that power these iterations rely data – and the better the data, the better your decisions will be, naturally. So where do you get the data to tell you if customers are happy or not about your product?
Explicit signals beat implicit signals almost every time
One of the key lessons I took away from my time from the behavioral targeting ad industry is that explicit data is much, much better than implicit data, when it comes to predicting user behavior.
That is, you’d prefer explicit “intent” data like:
- made a purchase
- used a student loan calculator
- searched for “palo alto bmw dealership”
- filled out a form
versus the less valuable implicit “interest” data like:
- have similar demographics to other people who buy
- visit the same publications as similar customers
- having a pattern of reading finance articles
So if you are looking to collect data to drive decisions, then the best kind comes from the explicit data of having users specifically take action, whether it’s positive or negative. Purchase intent data, as illustrated above, is positive – and quitting intent gives you the negative half. In fact, if you only look at the positive feedback, you might be ignoring 50% of your data.
As a result, you want lots of explicit data points in the axis of “I love it!” to “I hate it!” which includes people giving you money (maybe donations being the ultimate form of love) to allowing them to easily quit. Make it easy for your users to quit, unsubscribe, or otherwise cancel – it gives you the strong signal when you’re doing wrong! And make sure to track it and include it in all of your quantitative experiments as well.
Better data = better learnings = Better product
So to summarize my key arguments here:
- Give users lots of explicit ways to show appreciation and hatred
- These datapoints will help you iterate your product
- Better product iterations will let you reach product/market fit faster
- Reaching product/market fit will lead to more money faster
You can only learn so much from reacting to positive data, and trapping your users in unwanted subscriptions won’t get you to product/market fit any faster.
And finally, don’t do it because it’s annoying
‘nuf said.
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Spot on Andrew as usual. When thinking about your (and Marc's) comments re: product/market fit, it reminded me to relook at Christensen's “market to circumstances” discussion. On p. 75 he explains that “companies that target their products at the circumstances in which customers find themselves, rather than at the customers themselves, are those that can launch predictably successful products.” (The Innovator's Solution).
Quitters indicate that the product didn't fit the circumstance well, so the question remains, what was the circumstance and is there a common thread among quitters?
Thanks again for your posts, always a pleasure.
John Haggard
15 Jun 09 at 10:32 am
Epic. Very well said.
Eric Ries
15 Jun 09 at 10:39 am
I love your stuff Andrew. Very logical arguments all around.
I'd add another point, which is that we all want to earn money from people who love our products. Earning money from people who hate them is often possible and almost always a short lived and unsustainable phenomena. I've found that it's often hard to tell the difference online. If you can tie a satisfaction metric to profitability, it's a good way to know if you have a sustainable businesses or a short-term exploit.
Here's a good discussion of that topic. http://www.netpromoter.com/np/profits.jsp
Rob Goldman
15 Jun 09 at 11:29 am
Great link! Very interesting, and makes a ton of sense to distinguish the two.
Just to excerpt from the article that Rob linked, for readers of the comments:
The right goal for a company is to deliver customer experiences of such high quality that customers recognize the value in the relationship and become Promoters. These Promoters generate good profits and fuel true growth. They become, in effect, part of a company's marketing department, not only increasing their own purchases but also providing enthusiastic referrals.
By contrast, companies can boost short-term profits by exploiting customer relationships, raising prices when they can get away with it, or cutting back on services to save costs and boost margins. Those practices boost bad profits by extracting value from customers at the expense of loyalty, creating Detractors. Companies can not achieve long-term sustained growth on the basis of bad profits.
Conventional accounting can't distinguish a dollar of good profits — the kind that lead to growth — from a dollar of bad profits, which undermine it. The Net Promoter Score fills this gap. Just as managers use financial reporting to make sure they are meeting profit goals, they can use NPS to make sure they are meeting customer-relationship goals. Therein lies the path to true growth.
Andrew Chen
15 Jun 09 at 11:35 am
Exactly – there's a lot to be learned by studying how positive experiences deviate from the mean, but also the reverse. Studying quitters and extreme users in general is a very useful thing.
I know that IDEO often has what they call “unfocus groups” in which they invite very extreme users, rather than trying to find representative normal people. And the reason, of course, is because they want as many strong signals as possible from people throughout the spectrum of love vs hate, not just the middle part.
Thanks for the comment!
Andrew Chen
15 Jun 09 at 11:37 am
Exit barriers are a sin in web-based applications. Exit barriers are valuable with installed applications. Eliminating exit barriers are part of what must be done to move an installed application online.
Learning creates exit barriers beyond those of data movement. Learning-based exit barriers can be and should be reduced when moving an installed application online.
Even open software has implicit costs around the learning exit barrier. No software is ever free. Sure you may not see the dollars flowing out of your wallet, but your time is money as well.
David Locke
15 Jun 09 at 11:59 am
Great article, Andrew. Before reading this, I would have said that you should make it easy to quit for the same reason that business offer money-back guarantees: by eliminating all risk of trying your product, you make it more likely that people will try it (and hopefully love it). But the learning argument is even stronger, especially for startups.
Regarding startup-as-learning-machine, has anyone looked at the process as essentially *risk*-based? The core idea seems essentially the same as in Barry Boehm's spiral model of software development. (The Wikipedia page for OODA doesn't mention the spiral model, though, or vice versa.)
jasoncrawford
15 Jun 09 at 12:11 pm
The OODA is always followed by an AAR. The AAR is QA'ed, then training programs that teach the skills that serve as the infrastructure for the OODA cycle are modified. In the sense of the spiral model, the OODA loop itself is a sensor in the decision support network. The AAR and QA processes are the fusion in the decision support network. The decision in this DSS is to commit the resources to revise the training, and to train the revision.
The faster battlefield OODA is backed up with a slower training OODA. Layers and layers. The spiral is only visible when you step back and see the whole thing.
David Locke
15 Jun 09 at 2:29 pm
I've got old Photobucket accounts I haven't used in years. This usage information is ambiguous to Photobucket because I may be actively using the service via different credentials (which I am).
Would an quit signal add any more value in my case? I don't think so.
David Semeria
15 Jun 09 at 2:52 pm
By far the greatest learning always comes when talking to people who had made an active choice not to use (after having engaged in a buying process), or who had decided to become non-users (after a period of use).
Newly non-users are particularly useful – they've chosen to abandon their ££££'s – their disappointment is sharply felt and they tend to articulate it well.
NickS
29 Jul 09 at 11:34 pm
It's for exactly this reason that I started using gmail…
…and for exactly this reason that I won't be a Tivo customer ever again. I switched to the crappy cable company DVR because Tivo and the set-top wouldn't play nicely together, having otherwise thoroughly enjoyed my Tivo experience. So I call up to cancel Tivo, and they do this high-pressure thing where they insult your intelligence, tell you how bad life is about to get, and in the face of me saying “no no no cancel my service” this goes on and on for 20 minutes. Totally ridiculous, and I was taken aback (given how good Tivo is otherwise). Screw Tivo.
Steve C
10 Aug 09 at 7:03 pm
What do you think based on your experience, how many startups (%) achieve product/market fit and how many die trying.
Thanks,
Saku
Saku
30 Aug 09 at 6:33 am
What do you think based on your experience, how many startups (%) achieve product/market fit and how many die trying.
Thanks,
Saku
Saku
30 Aug 09 at 1:33 pm