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Quora stats: 150% growth in January, 160k monthly actives, 18k daily actives (lower bound estimate via Facebook app data)

I ♥ Quora. Oh yes, I’m a fanboy.
As my many of my friends and family know, I love Quora and get a ton of value out of it. It’s incredible to see what some of the most intelligent and influential folks in Silicon Valley and beyond have to say, and it’s some of the most valuable content I read every morning.

My prediction for Quora is that it’s going to turn into a huge, important Internet property- it’ll break out of the Valley network easily and inevitably. The experience of Facebook going from college-to-college will inform a strategy of going topic-to-topic, profession-to-profession, and network-to-network. I can easily see how the Q&A mechanics would apply to many other things, especially the political blogosphere and Beltway insiders, the entertainment industry in LA, the media and advertising industry, as well as random everyday stuff. And of course, it’s one of the best executed products I’ve seen in a long time- the interaction design in the product is amazing.

Writing down all the things that product designers and entrepreneurs can learn from studying Quora would be many blog posts in itself. Like I said, I’m a fanboy :-)

Facebook app data shows stats for connected sites and products, including Quora
All the fanboys want to know: Quora’s been growing, but how fast? I recently realized that because Facebook sign-in is used so aggressively by Quora, they will end up getting listed as an app just like everyone else. As a result, Facebook (for better or worse) ends up publishing their DAU and MAU stats, which are then stored and graphed on services like AppData, AllFacebook’s stats service, and others. This establishes a lower-bound for all the core users who have authed to Facebook, but obviously doesn’t count users who bounce or who don’t sign up, etc.

I included the public listings for Quora excerpted from both AllFacebook and AppData. (Both are great services, I’d encourage you to try them). As you can see from the graphs, Quora is growing on a very nice clip, over 2X larger in MAU over the last month. Very nice!

Caveats: These numbers ought to be a lower bound since not everyone is going to either 1) register on Quora, nor 2) connect their Facebook accounts. As a result, I’m sure the real uniques number is much larger, but this is probably a good estimate of the active Quora community. I’ve also seen a lot of Quora answers in my search engine result pages, so I’m sure there’s easily a multiple that come purely to look at the answers and then bounce, that ought to be added to the totals. So again, think of it as a lower bound, but I imagine that the relative growth trajectory is right.

Again, this is really impressive growth and I look forward to seeing Quora’s progress continue.

PS. For the data geeks out there: If someone else does a more thorough analysis on their historic growth rate, sticky ratio, benchmarks/comparisons, etc., please write it in a comment and I’ll link you. And please point out if I’m misinterpreting these stats!

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Retention metrics roundup of articles and links

Just returning briefly from blog vacation to share a couple links and slides I had collected on retention metrics. There’s so little public information out there that I wanted to call out the various articles and presentations that actually do contain real data. Given the difficulty of getting exponentially viral on Facebook these days, most companies are focused on great lifetime value and making it work with big ad buys. Obviously good long-term retention is important for that.

After you calculate out some basic cohort retention analysis, where do you go from there? One key thing is comparing it to existing benchmarks to see if things are going well or badly. Below are some of the few public articles and slides with real data in them.

Here’s a collection of public retention data and discussion

First, from Daniel James of Three Rings:

Metrics for a Brave New Whirled

Also, some slides social analytics company Kontagent:
Twitter retention analysis
RJMetrics, another analytics vendor, did this analysis of Twitter a while back: Twitter Data Analysis. Has some nice graphs like so:

Social gaming data for Facebook apps
And finally, Mixpanel based in San Francisco has some aggregated social gaming data:

Here’s a nice graph from them:

So what to make of all of this?

For now, you’ll have to look through this data yourself. I have a few rough notes I’ve written up about all of this retention data, and time allowing, I’ll publish some of them later. In the meantime, enjoy the presentations and links.

OK, back to blogging vacation :-)

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Minimum Desirable Product and Lean Startups (slides included!)

(if you don’t see the slides, go here to Slideshare)

Recent slides for a talk in Steve Blank / Eric Ries’s class on High-Tech Entrepreneurship

Yesterday I had the pleasure of giving a talk at Steve and Eric‘s class at Haas on the topic of Minimum Desirable Product – if you haven’t read the original article, it provides some useful context. I included an set of slides above on the topic, updated from my talk yesterday, which you can peruse at your convenience.

After you’re done, you can read my extended remarks below on some stuff I learned along the way. Frankly, any of these could probably be its own blog post but I’ve been feeling lazy lately so you get a couple sentences apiece instead :-)

“Viable” means different things to different people – my usage is meant to be pretty specific
Eric noted during my talk that I use a very narrow definition of “viable” within Minimum Viable Product, which is true. I believe in his usage of it, the focus on viability is actually a conglomeration of IDEO’s concept of desirability, feasibility, and viability. It’s frankly a coincidence that IDEO and the Lean Startup use a common term, though I believe they mostly overlap. I prefer IDEO’s framework because it allows a bit more precision in describing the class of issues you’re concerned about, but frankly there’s a ton of gray area. (Is a low-priced X a desirability thing or a viability thing? Honestly, both.)

Viability-first strategies do work, and may be the right thing for you
Many companies have come and gone that make products that aren’t that great, don’t generate a lot of consumer value, and yet still pull in a lot of money. It’s a strategy that can work, and I’m not arguing the opposite. However, I’m convinced that if your goal is to make a mainstream web property that has daily engagement, starting with the goal of creating lots of user value is probably the way to go. Similarly, if you have a highly transactional business like ecommerce, designing for daily engagement is probably overkill – in that case, reducing your cost of customer acquisition might be the right way to go. So it’s all very situational, and frankly, very personal based on how you want to run your product.

Minimum Desirable Product is just a starting point – you still need to figure everything else out
I also want to note that my message isn’t just to build for any random group of users and then the rest will take care of itself. That’s far too idealistic. Instead, it’s just a starting point for how you think of the problem. Ultimately, all your product ideas still need to be filtered through the lens of whether you can market them, that the market is big enough, and that the technology issues aren’t insurmountable. There was a recent Times interview with Steve Jobs on the iPad that illustrates this perspective:

… surely Apple stands at the intersection of liberal arts, technology and commerce? “Sure, what we do has to make commercial sense,” Jobs concedes, “but it’s never the starting point. We start with the product and the user experience.”

Metrics can be oriented towards user value
I’ve written before on some of the short-comings of using metrics-driven product strategies, such as here and here. An analytics dashboard is ultimately just one tool out of many that help you optimize whatever goal you want to set. If you are very focused on validating your business model and spend all your time tracking metrics such as viral factor, ARPU and conversation rates, then you will make those go higher. If you use your metrics to define user benefits and optimize those (I’ve begun calling this “Metrics of Love”) then you’ll make your value proposition go higher. So depending on your perspective and where you want to start, you’ll end up in different places.

Highly desirable consumer products also have minimalist featuresets
In consumer products, unlike some enterprise products, there’s a big focus on simplicity and immediate value. In some ways, the idea of a “minimum desirable product” is kind of misleading because highly desirable products may also have minimum featuresets also, perhaps even more minimal than an MDP. The important part is that they are the right features, and in fact, it often takes a longer time to simplify your product and boil it down to the core value. I think that’s an interesting paradox that exists in consumer products, and one that I didn’t grasp for a long time.

Learning about your business and learning about your product desirability are different things
One of the interesting points that came up yesterday was that if you view your company as a learning machine to validate your business before you run out of money, then you may see that worldview clash with wanting to deliver maximum product desirability. In many cases, shipping a 50% done feature may teach you a ton about the market, and very quickly you will learn what you need and want to move on. The problem is, it may turn out that going from 50% to 100% in user experience actually continues to increase value to the user, by making things more refined and more compelling, even if you stop learning about your business. This is a hard thing to trade off, and requires situational judgement. As Steve noted during yesterday’s discussion, deciding when you stop and just consolidate and refine what you have, versus packing in new features – well that’s the place where entrepreneurship is an art and not a science :-)

OK! Back to blogging vacation ;-) See you guys later.

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