Open Data User Journey Analysis. Samuel Engelberg - Mayor s Office of Data Analytics April 2018
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1 Open Data User Journey Analysis Samuel Engelberg - Mayor s Office of Data Analytics April 2018
2 Overview Key Findings User Breakdown 1) Explorers 2) Drivers 3) Test Takers 4) Fact Checkers Key Takeaways Next Steps
3 We re looking to answer the following questions: Who are Open Data users? What sources are referring them to our site? What is the first page they look at? And where do they go next?
4 Previous Research and Findings The Open Data Team engaged Reboot in Spring 2017 to research the users and opportunities for engagement with them. Captured information through 20 different interviews across various different boroughs and industries categorizing them into different user profiles Great qualitative data, we were curious to know what the quantitative data is as well You can find the research done by Reboot on the Open Data website under About and Laws and Reports in the Open Data User Research section This analysis builds off of Reboot s work by exploring insights around user behavior found in website traffic to the Open Data website.
5 Terminology Data Catalog Google Analytics is a web analytics service that tracks and reports (anonymized) website traffic Session is a group of user interactions with a website that take place in a given timeframe
6 Key Findings 1 2 million Sessions (March 17- March 18) 2 35 % Mobile 62% Desktop 3% Tablet 3 Top referral sources 1) Google 2) Nyc.gov 3) Direct 4 75% drop-off/ exit after viewing 1 page
7 Understanding our Users
8 Breakdown User Groups After digging into the user data we were able to identify and group users into the following groups, based on the Assets they viewed. The other user group consists of many smaller groups. 29% 37% 6% 13% 16% Test Takers Explorers Fact Checkers Drivers Other Based on 2 million Sessions from March 17- March 18 *Google Analytics Behaviour Flow Report
9 Fact Checkers: 16% of sessions 01 NYC residents visiting the site, directly digging into the data Coming directly to a page finding what they need and then leaving it. Fact Checkers are: conducting research, identifying trends in their area, or working on a specific case/project Looking at highly unique data sets with no particular use cases
10 Fact Checkers: 16% of sessions 01 Some of the most popular Assets that Fact Checkers look into are the following: NYPD Motor Vehicle Collisions 311 Service Requests from 2010 to present Film Permits Subway Entrances OATH Hearings Division Case Status Citywide Payroll Data Fiscal Year Average Salaries In Department of Correction Sessions (in thousands)
11 Drivers: 37% of sessions 02 Checking assets that are provided by the Taxi and Limousine Commission (TLC) The TLC is responsible for licensing and regulating NYC s medallion (yellow) cabs, for-hire vehicles, commuter vans and paratransit vehicles The TLC has been publishing on NYC Open Data before Local Law 11 of 2012 was in place 61 Datasets in the Catalog are provided by the TLC
12 Use Cases Drivers refer to open data to check up on: Licenses, Vehicles, and Applications Many drivers have shortcuts to datasets e.g. bookmarks External stakeholders reference data for Driver authentication
13 Test Takers 03 Leverage NYC Open Data to check civil service exam results Unions & Supervisors at other Agencies specifically check: civil service test results This data can be found in assets such as: Civil List, Civil Service Titles, City Record Online, and Civil Service List Active Department of Citywide Administrative Services (DCAS) Responsible for publishing this data and managing exams Advertises datasets on City Record Online and Green Book Online
14 Explorers 04 Exploring the site content and data catalog Look into Home/ Info pages such as: home, login, how to, management report and dashboard Browse and search the Data Catalog Delve into a diverse set of filters, searches and over 60% of users continue to other pages Most popular browsing queries *see next slide
15 Sessions Data Catalog Browsing Explorers most commonly used the following search queries & filter options while browsing the Data Catalog 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 - Filter
16 45% 733k 28% 314k 36% 256k 17% 119k Drivers Lorem ipsum porta dolor sit amet nec Referred from: 1) Google Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta 2) nyc.gov venenatis neque pharetra luctus felis vel tellus nec felis. 3) direct Fact Checkers Lorem ipsum porta dolor sit amet nec Referred from: Lorem ipsum dolor sit amet adipiscing. 1) Google Donec risus dolor, porta venenatis neque pharetra 2) nyc.gov luctus felis vel tellus nec felis. 3) direct Explorers Lorem ipsum porta dolor sit amet nec Referred from: Lorem ipsum dolor sit amet adipiscing. 1) direct Donec risus dolor, porta venenatis neque pharetra 2) Google luctus felis vel tellus nec felis. 3) nyc.gov Test Takers Lorem ipsum porta dolor sit amet nec Hypothesis: Lorem ipsum dolor sit amet adipiscing. Word of Donec Mouth risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 7-35% Donec risus dolor porta Pharetra luctus felis Proin vel tellus in felis Molestie nec amet cum continue navigating the site 15% Donec risus dolor porta Pharetra luctus felis Proin vel tellus in felis Molestie nec amet cum continue navigating the site 28% Donec risus dolor porta Pharetra luctus felis Proin vel tellus in felis Molestie nec amet cum continue navigating the site 42% Donec risus dolor porta Pharetra luctus felis Proin vel tellus in felis Molestie nec amet cum continue navigating the site The most common referrals for all users are: direct, Google and nyc.gov. Drivers and Fact Checkers have very few Users that navigate the site after viewing one page, while Explorers and Test takers are more likely to look at other assets and pages
17 Key Takeaways With Drivers, Fact Checkers, Explorers and Test Takers making up the majority of users the following considerations need to be made to enhance strategy and engagement. 01 Offer additional resources for Drivers (e.g. maintenance status) 02 Recommend similar datasets to Fact Checkers 03 Ensure usability of exploring by emphasizing most common queries 04 Determine what other assets Test Takers are viewing
18 Future opportunities for user research 0 1 User Interviews & Surveys Identify individual users and their use cases of Open Data and their unique journey on the platform. Through on Website surveys or invitations to interviews. Google Analytics Automate research to allow up to date findings and changes in behaviour. And drill down on pages with high traffic as well as long time spend Agency Feedback Collaborate with agencies to better understand their users and potential use cases. Specifically referral sources and Agency commitment to Open Data,
19 Thank you For any questions related to this report contact the Open Data Team via the Contact Us page on
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