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.
High-Level Start-up Metrics
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.
[Bernie Goldbach sourced this information from Git-Hub for use on the creative multimedia curriculum in the Limerick School of Art & Design.]
