Sequoia says your PMF story will fall into one of these 3 very different types. Which one does your startup fit in?

Great points by Jason Lemkin on how it becomes a whole lot different game beyond $5-10m ARR. 

Lots of great metrics here. Must read blog post and report for SaaS founders. Only thing I regret is that the data doesn't segment by target segment as I've seen many differences when selling to SMB, midmarket or enterprise for most of these metrics. 

Can entrepreneurship be taught? That’s an old debate but my friend Pierre Gaubil has a more interesting question: can a startup chances of success be improved? And he makes a convincing case that yes, by applying a robust methodological framework that leverages the collective intelligence we’ve built over decades of tech innovation.

Great example of how being concise, short and to-the-point can lead to quick decisions in VC fundraising. 

More details on their blog here.

There’s a fundamental flaw in many growth hacking approaches: the numbers don’t add up. 10% here, 10% there, etc... but at the end of the day you don’t see the +50% improvement to your top line. 

Andrew Chen not only puts a name on this but outlines how to deal with it by segmenting your users and customers by intent. 

Right on. I've been on TechCrunch and we had decent coverage for Scoop.it. Did it help our user acquisition? Not really. 

For Scoop.it and my previous startup (a b2c mobile platform), I've thought a lot about how to build a brand. And while I respect there might be some exceptions, I very much second what Andrew Chen writes here: building a brand is mostly the consequence - not the cause. 

It doesn't mean there's just nothing to do other than growing to build a brand. The story your company tells, the values your product expresses, how it's design, how you communicate and many other things will shape your brand a certain way. But whether it's big or small - or said more bluntly whether you have a brand or not - remains tightly coupled with how much you grow. 

So, as a startup founder, unless you're an exceptional marketing genius, your best bet is probably to focus on product market fit and finding the right acquisition channels while paying attention to the story you tell. The brand will follow. 

PS: in his post, Andrew focuses on consumer startups but I would say that it's probably also true for most B2B startups. Even though they have more targeted PR / influencer marketing channels they can leverage for brand building purposes, I would consider them from a pure ROI standpoint as customer acquisition channels. And consider any resulting brand awareness impact a bonus. 

A good high level recap by Tom Tunguz of the different ways to price and why. 

SaaStr's Jason Lemkin shares some great lessons in that post. I can definitely echo to some of them but I actually found I've been at times too slow on big decisions (his #1 tip is to slow them down). While I agree that rushing things on impulse can be bad, some of my mistakes came from not reacting soon enough when things were bad. And ignoring - or wanting to ignore - signs that they were.

What are the things you'd be doing better next time? 

Excellent summary by Andrew Chen on the cultural gap when it comes to fund raising in Europe vs Silicon Valley.

On the positive side, I'd add that, coming from Europe where access is often hard and networks often closed ones, I was surprised by how accessible VC's were. It doesn't mean they'll invest easily though, unlike what most foreign founders still think and as Andrew Chen explains in this post. 

As we're developing a new product with the Scoop.it team, I was re-reading this great post by Eric Ries on the fallacy of product launches for startups. This was also the topic for one of the roundtables I participated to at the recent CrossLink capital's Alpha Summit.

As startup people, most of us have dreamed of doing an Apple-like product launch. Get on stage, capture attention, unveil something amazing, etc...

But most startups don't have the opportunity to mobilize attention in a big way like Apple or other big companies do. Unless they have a big personal brand, founders won't get huge attention for their new babies - however beautiful they think it might be. 

Perhaps more importantly, Apple-style launches mean getting products 100% ready and flawless which - for software startups - is terribly wrong. To quote LinkedIn founder Reid Hoffman: "If you are not embarrassed by the first version of your product, you’ve launched too late."

Finally, even a good launch is temporary. Great startups succeed by getting long-term, sustainable growth.

Not 15' of fame. 

I failed at this several times but we got one right with Scoop.it's launch-that-wasn't-a-launch. Instead of trying to get massive PR coverage we couldn't get anyway, we launched a private beta, reached out to influential people who cared about the content curation problem, got them to use our first version which led them to blog about it and iterated the product for close to one year before finally opening the doors.

4 million users later, I think of this of our most successful launch (that wasn't). 

The hype on AI is real says FirstMark Capital's Matt Turck. But he warns that big data is one of the key drivers. If you don't have access to large data sets, you can't train any AI to learn anything. So as he notes, there's a lot of talent recruiting, data gathering and then learning and fine-tuning before you can launch a product. The lean startup approach of "launch, fail fast and learn" therefore doesn't work. 

To which I'll add another point. Will you eventually own the data stream your AI relies on to learn? If you do then great. But if you don't, how defensible is your future business if your data comes from Facebook or Google API's?

When I was studying engineering and thinking about going to grad school, I had my own version of the American dream. I had done a short internship in a Silicon Valley company, I had visited the Stanford campus and I had one obsession: I wanted to come back and be a part of this ecosystem. Because this is where people knew, this is where tech talent and knowledge resided. 

So 20+ years ago (closer to 25 now... damn...), I went to grad school at Stanford and it changed my life. I discovered so many things that weren't even discussed or taught in European universities that I couldn't list them all.

Today, this situation has changed completely as Tom Tunguz explains in this post. Make no mistake though: Silicon Valley is still unrivaled for tech in many ways. Ecosystem, access to funding, tech culture (with its pros and cons as recent coverage unfortunately showed), go-to-market, scaling, market access, etc... But one thing is very different from 25 years ago: great engineering talent doesn't need to be in Silicon Valley to be great. 

I've tested that first-hand being a French founder in San Francisco with an engineering team based in France. Very frequently, I meet peers who will tell me about such and such new technology that's trending in Silicon Valley. And of course, I tell my team about it. But almost systematically, they tell me they've known about it for months or tested it and sometimes implemented it already themselves. The Internet makes the world smaller but it makes the tech world an even tinier little village as engineers are now hyper connected.

Tom adds a new point to this which is the economic equation. All of us foreign founders in Silicon Valley have felt what he explains very personally. We all have rising costs, budgets which are getting harder and harder to balance and - unlike our peers who grew up in the Bay Area - a clear understanding of how much attractive the alternative is economically. 

When I relocated 6 years ago to San Francisco with an engineering team based in France, it felt very exotic. I kept explaining that yes, we have good engineers in France - not just bakers, winemakers and luxury clothes designers. Now, when I say my engineering team is based in France, it looks smart. 

Yes, there's only one really. But where it gets tricky is understanding how to measure it and more importantly what to do about it. 

I've found myself it's easy to crunch a lot of data and feel you're understanding things where actually talking to real people is irreplaceable. There are lots of things data don't capture that people will explain. 

And it's when you start hearing the same story over and over again that the pattern becomes obvious. As well as the data... and the way to improve it. 

Interesting stats from TechCrunch. Even though I'm not sure many entrepreneurs really care about the odds of success. 

Recruiting is hard. And if you had any doubt about this, science has now proven it: by combining random interviews in a series of tests, researchers have found that interviews can not only be irrelevant to detect whether a candidate will perform in his/her job but it can also be counterproductive. 

Are you using any tests in your recruitment process?

OpenView Labs' Kyle Poyar finds that these figures demonstrate a lack of maturity by a lot of SaaS companies in their pricing strategy. 

Their survey is really nice and will make you think about your own approach to pricing if you're in SaaS. 

My own thoughts while I took the quizz is that pricing is indeed a key element of the mix and is often not tested enough. But for some legitimate reasons too: it's quite hard to do solid A/B test on pricing when you're in early-stage phases as you'll probably won't have enough data to produce significant results quickly. And when you're beyond that phase, it can be perceived as harder to change. 

But at the end of the day, this is inspiring data and should inspire you to design some interesting pricing experiments.