Uber leaked select city-by-city revenue data last week. The numbers were impressive: $26MM in top line revenue in NYC alone for December 2013.

But just how big is Uber relative to the NYC taxi market? Chris Wong FOILed all of NYC’s taxi trip data for 2013 which is now in Google Big Query for easy SQL querying to compare Uber’s usage relative to NYC taxis.

1. Uber NYC earned 18% as much as NYC taxis on Dec 31, 2013

  • Uber 2013: $1,118,271
  • Uber 2012: $182,819
  • Taxis: $6,272,548

2. Uber NYC earned 12% as much as NYC taxis in December 2013

  • Uber: ~$26MM
  • Taxis: $211,328,661.33

3. Uber NYC had 7% as many rides as NYC taxis on Dec 31, 2013

  • Uber 2013: 32,547
  • Uber 2012: 4,785
  • Taxis: 467,587

4. Uber NYC’s average ride was 256% the average NYC taxi fare on Dec 31, 2013

  • Uber NYE 2013: $34.36
  • Taxis NYE 2013: $13.41

NYC Taxi Data Queries In Google BigQuery

  1. December 2013 Total Revenue

    SELECT SUM(FLOAT(total_amount)) FROM [833682135931:nyctaxi.trip_fare] WHERE INTEGER(YEAR(TIMESTAMP(pickup_datetime))) = 2013 AND INTEGER(MONTH(TIMESTAMP(pickup_datetime))) = 12

  2. December 31, 2013 Total Revenue

    Same as above except add AND INTEGER(DAY(TIMESTAMP(pickup_datetime))) = 31

  3. December 31, 2013 Total Trips

    SELECT COUNT(*) then same as 2 above

Sources

Chris, Sissi and the organizers of the GA Extra Credit Meetup kindly invited me to come and speak a few weeks ago. There was no specific topic outside of venture, so I took the opportunity to walk people through a potential framework they might use if evaluating a new company. There are a lot of business school and consulting frameworks out there, but most of them are for mature companies. I have not found anything that is particularly helpful when considering joining, starting or investing in a new company, so I proposed the 5Ms1.

  1. And a P … because otherwise it would be too clean like every other business framework

This post was originally published on Medium

Tictail is a global community with stores and customers in over 140 countries.

Metrics and data visualization are critical to understanding how the community is developing. This starts with absolute numbers displayed over time e.g., GMV, number of active stores, number of unique customers. Slightly more sophisticated is cohort data which offers insights on how a set of users engage with the product through time e.g., average monthly sales by store grouped by the month the store launched. Cohort data also helps us understand what type of users we are attracting and how product changes are helping them.

All of these numbers and graphs are great for analytics, but they don’t provide our community with an intuitive appreciation of just how global the community is. We thought it would be amazing to see an animated map of every Tictail order over time.

It is inspiring to see how many people, all around the world, find products they love on Tictail stores. This is just the beginning.

We recently launched our first consumer app to help stores reach mobile shoppers around the world, and we can’t wait to see what the next two years looks like!

Technical Background: To make this graphic we exported a uniform subset of recipient addresses, extracted unique city and country pairs, and geocoded those using Mapquest’s free geocoding API built with OpenStreetMap data. If you are interested in geocoding at scale, check out this brief introduction of available resources. We designed a custom basemap using Mapbox and used the CartoDB Torque engine to create the animated temporal map.