111–120 of 203 posts
(co-founder of Optimizely here) HUGE thank you to YC and the entire HN community for all their support over the years. It was almost ten years ago that we launched here on HN. [1] [1] https://news.ycombinator.com/item?id=1788634
I remember meeting Dan, the founding CEO of Optimizely, at winter 2010 YC demo day. I had just grabbed my name tag and was walking toward the building when he stopped me outside to give me the pitch. He immediately struck me as a smart, capable guy, but as a former software engineer I couldn't understand why companies wouldn't just build A/B testing themselves. What did Optimizely add? How was it defensible? Dan didn…
Earlier quoted context omitted.
It's worse. They essentially kicked-out their existing self-serve customers in the process. We were on the (at the time) silver plan, but when we were ready to upgrade to gold, there was no gold, no silver, nothing... Just some super-expensive and vague enterprise plan. But there's a silver lining: we created and open-sourced Alephbet[0] - a simple A/B testing platform together with a couple of backend options with A…
Thanks for this, also I love the name :p
I worked at Optimizely from before its series A in 2012 until the end of 2016, so I have a unique perspective on this. For most of the time when I worked at Optimizely, the company was all the rage. It appeared at the top of most "hot startup" lists, the Glassdoor reviews were 5/5, revenue was skyrocketing, and for a period in 2014 it became the fourth most valuable YCombinator company (after Stripe, AirBnB, and Drop…
I have a sort of general question about this narrative, which seems to apply to lots of startups that begin as self-service, developer-focused projects and end in enterprise hell. Is it not the case that these startups begin developer-facing, get market traction, are lavishly funded, and then discover that the self-service offering they've built simply can't satisfy the projections they've made to justify their valua…
One of the important distinctions between Optimizely and most developer-first platforms is that experimentation is a hard practice to pick up. Most companies have difficulties getting their programs off the ground and keeping them funded, let alone grow or scale them. Small digital businesses struggle more for several reasons: 1) they have few resources, so teams are understaffed and resources are pulled easily, 2) they have little money, so the percentage uplifts are rarely motivating, 3) they have little traffic, so it is harder to get a statistically significant measure in their experiments
Because of these issues, Optimizely always had really poor retention in the SMB space. Nonetheless, the SMB customers helped Optimizely build up a brand name, build up legions of practitioners, and get the skills and experience to go after the Enterprise market. When Optimizely started acquiring enterprise customers, retention improved substantially.
This isn't to say that there aren't lots of problems with Enterprise sales and that Optimizely didn't make tons of cultural mistakes in that pivot. But on the core financials, Enterprise kept Optimizely afloat. The problem wasn't the pivot to enterprise, but the trade-offs that were mismanaged along the way. The path to enterprise was inevitable and correct.
I worked at Optimizely from before its series A in 2012 until the end of 2016, so I have a unique perspective on this. For most of the time when I worked at Optimizely, the company was all the rage. It appeared at the top of most "hot startup" lists, the Glassdoor reviews were 5/5, revenue was skyrocketing, and for a period in 2014 it became the fourth most valuable YCombinator company (after Stripe, AirBnB, and Drop…
I'm kinda curious about your >$100k loss on your stock. Your strike price from pre series A options was more than the gains on a near $600 million sale price? Maybe I am misunderstanding, but how is that possible?
Any alternatives to Optimizely?
This sounds like a bad exit. From what I can tell the original Optimizely space has been slowly becoming a more discrete area of progressive delivery, rather than an industry on its own. Many players have jumped into this space with their own A/B testing and Feature Flags solutions as part of their total offering, many of those offerings being free, open source or cheaper. Also potentially better in the concrete task…
In 2014 I wrote an article on why Optimizely's approach to AB testing was statistically flawed [1]. I was working at a competitor so I needed to be a bit circumspect. It was discussed here: https://news.ycombinator.com/item?id=7287665 I always wondered how they got away with it for so long. [1] http://www.datascienceassn.org/sites/default/files/Most%20Wi...
This sounds like a bad exit. From what I can tell the original Optimizely space has been slowly becoming a more discrete area of progressive delivery, rather than an industry on its own. Many players have jumped into this space with their own A/B testing and Feature Flags solutions as part of their total offering, many of those offerings being free, open source or cheaper. Also potentially better in the concrete task…
Optimize is not an incumbent but a challenger. Google Optimize came out years after Optimizely and is less featured and built by a smaller team. The main benefit to Optimize is that it's cheap.