1.
Weebly
Looker Reports Powered by Snowplow
Audrey Carstensen - Product Analytics Lead
Bo Han - Data Engineer
(With help from the entire Weebly Data team!)

2.
Who is Weebly?
● Drag n’ Drop Website Builder and Tools to Support Entrepreneurs Online
● ~30 million users, 16MM websites, ~10B Pageviews per year
● 3 data engineers & 7 data analysts supporting 200+ employee company
● Use Snowplow’s data-pipeline for events on Weebly.com and Weebly built sites

3.
What are we going to show you?
● Journey to Snowplow + Looker
○ Why Event Tracking?
○ Snowplow Implementation
● What we implemented in Looker (Usage / Visuals / LookML)
○ Event and User Counts
○ Funnels
○ Retention
○ Onboarding timing
● What we learned

4.
Why Event Tracking?
● Product needs to track user behavior...
○ Product dashboards showing cohorts, retention, funnel
● Marketing needs visitor/pre-signup data...
○ Visitor / campaign level breakdown
● Analysts need granular/raw data...
○ AB testing, ad-hoc SQL queries into raw event stream
● Data scientists need features and need it real-time
○ Story for another meetup...
Our needs are beyond what Google Analytics offers ...

5.
Journey to Snowplow (Engineering Perspective)
● Product hosted on on-premise data center;
analytics on AWS
● Small data engineering team with very
limited DevOps support
○ Better not reinvent the wheel
● Need for a large scale event tracking
solution capable of handling 100MMs
events per day
○ $$$ Too expensive for Mixpanel
○ And need it fast.. Think 12 weeks of
development time
● Long term goals
○ Need to store the underlying raw data
○ Real-time consumers
Analysts
Data
Engineers
Snowplow

6.
Example Summary Tables
● Events rollup
○ General event volumes by date (no user data)
● Last 60 days
○ Base many dashboards / looks off this
○ Faster than all time table
● Users
○ User facts based on Snowplow events
● User action times
○ Keep track of onboarding timestamps
○ Quickly pull “time to X” metrics
Pro-tip: Store frequently used views as summary tables for better performance

7.
Rollup Explore: Usage and Visualization
● Date / Category / Action / Label / Page Type (Weebly-specific based on URL)
● Usage: troubleshooting events as an engineer or data analyst, quick
estimates as a data analyst

8.
Rollup Explore: LookML

9.
Events Explore: Usage
● Growth - interaction volume
○ Weekly users performing specific steps in onboarding
○ Monthly active users
● Core Editor - editor actions and upgrade views
○ Weekly users adding pages to their website
○ Most common elements added to sites
○ Upgrade modal views
● Product - use of specific features
○ # of users enabling / disabling a given feature (especially new ones!)
○ Differing rates of feature usage by AB test

10.
Events Explore: Visualization (Example Data)
● Count by events, sessions, users
● Many dimensions and ways to segment (below just snowplow segments)

11.
Events Explore: LookML

12.
Funnel Explore: Usage
● Growth - onboarding and conversion
○ Landing Page → Signup → Themes Choice → Editor View
○ Domain Modal → Domain Selection
○ Upgrade Modal → Transaction
● Core Editor - initial editor interaction
○ Signup → Editor Interaction
○ Signup → Viewing / Adding Pages
● App Center - app installation
○ App Center Page View → View app → Install app

13.
Funnel Explore: Filters

14.
Funnel Explore: Visualization (Example Data)

15.
Funnel Explore: LookML (truncated)
See Appendix for more detailed code samples, this slide for the special filter for exclusions
Based off: https://looker.com/blog/modeling-conversion-funnels-in-looker-a-mysql-subselect-approach
view: user_funnel {
derived_table: {
sql: select s0.blended_user_id, s0.s0_tstamp, min(s1.derived_tstamp) as s1_tstamp
from (
select blended_user_id, min(derived_tstamp) as s0_tstamp
from snowplow_pivots.events_last_60
where {% condition step_1_event_type %} event {% endcondition %}
… and {more conditions here}
and blended_user_id NOT IN {special filter for exclusions here}
group by 1
) s0
left join snowplow_pivots.events_last_60 s1 on s0.blended_user_id = s1.blended_user_id
and s1.derived_tstamp >= s0.s0_tstamp
and {% condition conversion_window %}
and {% condition step_2_event_type %} event {% endcondition %}
and …. {more conditions here}
group by 1,2;;
}

16.
Retention Explore: Usage
● Growth - track early retention
○ % of signups visiting on day 2
○ % of signups visiting in the first week
● Analytics - deep dives
○ Impact of product launches on retention
○ Differential retention between SEM/SEO/etc
○ Differential retention between AB tests

17.
Retention Explore: Filters

18.
Retention Explore: Visualization (Example Data)
Looker automatically does the unique calculation
% of signups that visited in days 2-7 is the blue box divided by the red box
Calculated fields allow a “single value” to populate a dashboard

19.
Retention Explore: LookML (truncated)
See Appendix for full code
derived_table: {
sql: with xxx as (
select blended_user_id, min(derived_tstamp) as first_tstamp
from snowplow_pivots.events_last_60
where {% condition initial_event_type %} event {% endcondition %}
and {more conditions}
group by 1)
select e.blended_user_id, coalesce(e.derived_tstamp, xxx.first_tstamp) as derived_tstamp, xxx.first_tstamp,
event, page_type, category, action, label
from snowplow_pivots.events_last_60 e
left join xxx on e.blended_user_id = xxx.blended_user_id
where
(
{% condition recurring_event_type %} event {% endcondition %}
and {more conditions} and xxx.first_tstamp - '0 second'::interval < e.derived_tstamp
) or (
{% condition initial_event_type %} event {% endcondition %}
and {more conditions} and xxx.first_tstamp = e.derived_tstamp)
;;
}

20.
First Actions Explore: Usage
● Growth - onboarding timing
○ Signup → Template Selection
○ Signup → Publish
● Core Editor - editor timing
○ First Editor View → Publish
● Analytics - differential timing by AB test
○ Signup → First Editor View for different onboarding flows

21.
First Actions Explore: Visualization (Example Data)

22.
First Actions Explore: LookML

23.
Learnings - the Good
● Users huge on refreshable dashboards
● See events data and other data side by side in dashboards
● Join to AB segmentation data
○ Funnel drop off by AB segment
○ Event rate by AB segment
● Join to user data
○ Exclude banned users
○ Has best guess to country / location data

24.
Learnings - the Challenges
● Timing has been tricky for us since Snowplow starts at 5 PM PST. Often set
up dashboards to hide the partial day of data / tell users data is 2 days behind.
● Events can be slightly out of order - added some “slop” to our funnels to
account for this
● Looks can take minutes each to run (depends on Redshift
load). Opening a large dashboard causes a sudden large load
on the servers.
○ We persist queries for 8 hours to help with this
○ Still have outstanding user requests to reduce latency
● Explores are complicated - working on getting all PMs
creating their own looks

25.
We’re hiring!
https://careers.weebly.com/
Questions?
audrey@weebly.com
bo@weebly.com

26.
Appendix: LookML for User Funnels & Retention Funnels
● Copied directly from Looker
● Tables / fields to know
○ snowplow_pivots.events_last_60 - enriched table with data for past 60 days
○ blended_user_id - internal user id to the best of our knowledge, otherwise
cookie-based user id
○ derived_tstamp - snowplow “best guess” timestamp, see
http://snowplowanalytics.com/blog/2015/09/15/improving-snowplows-understan
ding-of-time/

27.
User Funnel LookML: Main derived table

28.
+ 2 additional joins (s3, which represents 4 steps)
User Funnel LookML: Example join to following step

29.
User Funnel LookML: Excluding users based on events
This is the “

30.
User Retention LookML