Analytics of a Failed Start-up
WE CAN OFTEN LEARN more from failure than from success. That is a conclusion I've reinforced after viewing Everpix business intelligence on Github.
The company Everix opened in 2011 and it targeted "the Photo Mess", something I have in my digital lifestyle. I have very valuable photo collections but they span five hard drives and three cloud services. Everpix offered to help me solve my digital clutter and its startup engineering pulled down $2.3M in angel and VC funding. The money didn't stretch far enough.
According to the Everpix team, "After two years of research and product development, and although having a very enthusiastic user base of early adopters combined with strong PR momentum, we didn't succeed in raising our Series A in the highly competitive VC funding market. Unable to continue operating our business, we had to announce our upcoming shutdown on November 5th, 2013".
But there's more--much more--because Everpix released a very powerful set of metrics that all start-ups could leverage.
At the time of its shutdown announcement, the Everpix platform had 50,000 signed up users (including 7,000 subscribers) with 400 millions photos imported, while generating subscription sales of $40,000 / month during the last 3 months (i.e. enough money to cover variable costs, but not the fixed costs of the business).
Everpix shared its high-level metrics from September 2012, with the launch of paid subscriptions, to October 2013, the last month before the Everpix shutdown announcement. This is a significant dataset of hundreds of files covering all aspects of business. This rare and uncensored inside look at the internals of a startup will benefit the startup community.
Here are some example of common startup questions this Github dataset helps answering:
- What are investment terms for consecutive convertible notes and an equity seed round? What does the end cap table look like? (see here)
- How does a Silicon Valley startup spend its raised money during 2 years? (see here)
- What does a VC pitch deck look like? (see here)
- What kinds of reasons do VCs give when they pass? (see here)
- What are the open rate and click rate of transactional and marketing emails? (see here)
- What web traffic do various news websites generate? (see here and here)
- What are the conversion rate from product landing page to sign up for new visitors? (see here)
- How fast do people purchase a subscription after signing up to a freemium service? (see here and here)
- Which countries have higher suscription rates? (see here and here)
- What frustrates people the most abour their photo collection? (see here)
- Do people actually edit their digital photos? (see here)
- What would it take to acquire customers through online ads in such a business? (see here)
- How much price sensitive are consumers for such online services i.e. what's the price elasticity? (seehere)
Start-up Dataset with an Inside View
The dataset is organized as follow:
- Anonymized VC Feedback.md: Unedited feedback from VCs who passed on Everpix
- External Metrics: Raw metrics retrieved from external systems like Google Analytics or AWS billing
- Financials.md: High-level financials with fundraising and final P&L
- Internal Metrics: Raw and computed metrics from our service from photos imported to subscription sales
- Investor Reports: Monthly investor reports detailing the progress, strategy and ups and downs of Everpix from the inside
- Online Paid Customer Acquisition Test Results.pdf: Results from early test ad campaigns for paid customer acquisition in Summer 2013
- Presentation Slides: The slides used to introduce Everpix to press and investors along with the latest version of our more extensive VC pitch deck
- Product Videos: Everpix presentation videos made during the product life
- Public Feedback: Press articles covering Everpix and user reviews on App Stores
- Google Consumer Surveys: Exclusive consumer insight research about people and their photos done with Google Consumer Surveys
- Timeline & Numbers.md: Everpix product timeline and numbers
The metrics in the dataset were "frozen" as of November 6th, 2013 (the day following the announcement of Everpix's shutdown) and represent more than 90% of all available Everpix metrics. Only metrics covered by NDAs with partners or metrics exposing identifiable Everpix users information have been omitted.