a journey to enable self-service using Google tools

1. Analytics at Motorola,
a journey to enable
self-service using Google tools
Patrick Deglon, June 2015

2. After a PhD in Particle Physics and 10 years at the University of Geneva studying
the creation of the Universe, Patrick spent the next decade driving business
insights at eBay and Motorola Mobility.
At eBay, he led significant improvements in marketing effectiveness by developing
methods to measure incremental sales, and by running large scale experiments on
Internet marketing channels.
At Motorola Mobility, he raised the bar in Analytics and on-board open Google
tools and technologies including Google Docs, Big Query, App Engine and
Compute Engine.
In June 2015, he founded Deglon Consulting to help companies adopt the latest
technologies in Cloud computing as well as integrate sound analytical
methodologies to measure business impact and marketing incrementality.
He is married with two kids and recently moved to Sarasota, Florida
Patrick Deglon Bio
2

3. Agenda
● Industry overview (mobile)
● Motorola Example
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + gChart + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
3

4. Imagine a world...

5. … where
information is
ubiquitous (anytime & anywhere)

6. … where buildings can
recognize your presence

7. … where even
streetlights are
connected to
Internet

8. Welcome to a
digital
world

9. Mobile was a revolution,
but Mobile is an outdated concept.
The Digital World (Cloud, Internet,
World’s Information, Digital Personal
Assistant…) will be available
everywhere: phones, watches, glasses,
cars, appliance, microchip implant, ...
9

10. Evolution of mankind
1973: First hand-held portable telephone
1989: Web proposal
2009: First microchip implant
...
Homo
Sapiens
Homo
Technicus
10

11. ● Industry overview (mobile)
● Motorola Example
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
Agenda
11

12. 1928 1936 1943 1955 1969
World’s first
portable
FM two-way
radio
Motorola
introduced
Police Cruiser
Radio Receiver
World’s first
high-power
transistor
in commercial
production
First words
from moon
relayed via
Motorola
radio
Motorola: 80+ YEARS OF INNOVATION
1947 1963
World’s first
truly
rectangular
color TV
tube
1983 19961991
World’s first
commercial
handheld
cellular phone
DynaTAC 8000X
weighed 28-ounces
(794 grams)
World’s first
GSM cellular
system
World’s first
dual-mode
cellular phone
The 3.1 ounce
(88 grams)
StarTac©
wearable cellular
phone is the
world’s smallest
and lightest
1990
World’s first
HDTV technical
standard
1973
Demonstrated
prototype of
the DynaTAC
portable
cellular
system
World's first handset
, iDEN i1000plus, to
combine a digital
phone, two-way
radio, Internet
microbrowser,
e-mail, fax and two-
way messaging
1999 2000
World’s first
general packet
radio service
(GPRS)
wireless phone
for always on
Internet access
2004 2006 2009
Iconic RAZR
V3 wireless
phone
introduced
MING smart
phone recognizes
10,000+
handwritten
characters
from Chinese
alphabet
20122002
World’s first
wireless cable
modem gateway
introduced
Motorola
DROID #1 on
Time’s Top
Ten of 2009
2013 2015
Launch
Moto X,
Moto G
Fast
upgrades
Galvin
Manufacturing Corp
Moto E
Moto 360
12

13. Motorola Cloud Customers Ecosystem
WebProduct
Sales
Business
Operation
Customer
Support
Partners & Carriers
Moto
Maker
Consumers: Phones,
Wearables &
Companion Products
Internal Business
Teams
Marketing
FinanceEngineering
Motorola Cloud
13

14. Motorola Cloud Applications & Services
Infrastructure as a Service
On-Device Applications &
Services
Web Applications &
Software as a Service
Platform as a Service
Cloud Applications &
Services
14

15. Google Cloud Platform (GCP) 101
GAE
Google App Engine
GCE
Google Compute Engine
GCS
Google Cloud Storage
BQ
Google Big Query
GA
Google Analytics
Virtual Linux/Windows Server
Web Server with automatic scaling
“FTP”
Big Data warehouse, public version of Dremel that
is powering Google Search
Website, Mobile and IoT tracking & analysis
15

16. Confluence’s
Data Wiki
OSQA’s FAQ
(“Stackoverflow”)
Data & Analytics
Summits
Solution
Engineering
Analytics Ecosystem
Device Instrumentation
Check it out:
→ Android Settings
→ Motorola Privacy
⌧ Help Improve Motorola Products (On/Off)
⌧ Moto Care (On/Off)
Motorola Big Data Environment
Motorola Cloud
(GAE/GCE)
Big Data (BQ)
Moto Insights
(GAE)
BigFeed (GAE)
16

17. ● Industry overview (mobile)
● Motorola Example
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
Agenda
17

18. How to provide a global
source of truth,
available on any form
factor with an outreach
mentality?
Existing Situation
- Numerous (conflicting) sources of truth
- Too many variations of same data cube
- “Table in your face” approach
- No global business definition
- No curation of manually entered data
points
- Report accessible on an internal portal
only (through VPN)
- No mobile form factor
Daily Activations Report
18

19. Motorola Factory
# Shipments
Distribution Channels
# Sales
First Usage
# Activations
Simplified Business Flow
Key Performance Indicators
19

20. Motorola Factory
# Shipments
Distribution Channels
# Sales
First Usage
# Activations
Google BigQuery
Motorola
Cloud
Insights
...
Data Flow
Motorola
IT
20

21. Demo Daily Report: Final Email
21

22. Features of Daily Report
● Get data (pivot) from BigQuery
● Spreadsheet magic
● Insights: WoW trends with statistics test, Key driver for growth, Key
milestone, internal QA tests
● Email
● Embedded Chart
● Scheduler
Demo:
https://docs.google.com/spreadsheet/ccc?key=0AjgpL8JvOwsvdDJjV0s3NF
phS3RnRzBXakNpZUR1ZGc#gid=21
22

23. • Assume sales follow a diffusion S-shape, i.e.
Description of the illustrative simulation
Marketing Word of mouth
ΔN
Nmax
N
• Add random noise to theoretical daily activations (Poisson)
• Simulated daily activations (sales) for United States, Canada,
Brazil, India, Russia, China, Germany and United Kingdom
with various launch date per region
ΔN = a (Nmax - N) + b N (Nmax - N)
23

24. Step 1: Create a backbone table
SELECT
CAL_DT,
Country
FROM
ON A.Dummy=B.Dummy
WHERE
B.CAL_DT>=A.Launch_Date
motorola.com:sandbox:demo.backbone:
INNER JOIN
(
SELECT
Country,
CASE
WHEN Country IN ('United States','Canada') THEN '2013-08-01'
WHEN Country IN ('Brazil','Russia','India','China') THEN '2013-10-01'
ELSE '2013-12-01'
END AS Launch_Date,
GDP_USD/1e7 AS Scale,
1 AS Dummy
FROM
[motorola.com:sandbox:pdeglon.countries]
WHERE
Country IN ('United States','Canada','Brazil','Russia','India',
'China','Germany','United Kingdom')
) AS B
(
SELECT
CAL_DT,
1 AS Dummy
FROM
[motorola.com:sandbox:pdeglon.calendar]
) AS A
X
BACKUP
24

25. Step 2: Calculate KPI value over time
SELECT
CAL_DT,
Country,
‘Phone 123’ AS Model,
INTEGER(Scale*
EXP(-POW( -150,2)/2/POW(75,2))
/(75*SQRT(2*PI()))
) AS Daily_Activations
FROM
[motorola.com:sandbox:demo.backbone]
motorola.com:sandbox:demo.baseline:
DATEDIFF(TIMESTAMP(CAL_DT),TIMESTAMP(Launch_Date))
...
Normal Distribution:
BACKUP
25

26. Step 3: Add Random Noise
SELECT
CAL_DT,
Model,
Country,
INTEGER(
Daily_Activations + SQRT(Daily_Activations) *
SQRT(-2*LN(RAND()))*COS(2*PI()*RAND())
) AS Daily_Activations
FROM
[motorola.com:sandbox:demo.baseline]
motorola.com:sandbox:demo.simulation:
Normal (Gaussian) Random Number
(mu=0, sigma=1)
(pseudo) Poisson distribution for
N=Daily_activation
BACKUP
26

27. Step 4: Final Pivot for report
SELECT
CAL_DT,
SUM(Daily_Activations) AS Total,
SUM(CASE WHEN Country IN ('United States','Canada') THEN Daily_Activations ELSE 0 END) AS NA,
SUM(CASE WHEN Country IN ('Brazil','Russia','India','China') THEN Daily_Activations ELSE 0 END) AS BRIC,
SUM(CASE WHEN Country IN ('Germany','United Kingdom') THEN Daily_Activations ELSE 0 END) AS EU,
SUM(CASE WHEN Country='United States' THEN Daily_Activations ELSE 0 END) AS UnitedStates,
SUM(CASE WHEN Country='Canada' THEN Daily_Activations ELSE 0 END) AS Canada,
SUM(CASE WHEN Country='Brazil' THEN Daily_Activations ELSE 0 END) AS Brazil,
SUM(CASE WHEN Country='Russia' THEN Daily_Activations ELSE 0 END) AS Russia,
SUM(CASE WHEN Country='India' THEN Daily_Activations ELSE 0 END) AS India,
SUM(CASE WHEN Country='China' THEN Daily_Activations ELSE 0 END) AS China,
SUM(CASE WHEN Country='Germany' THEN Daily_Activations ELSE 0 END) AS Germany,
SUM(CASE WHEN Country='United Kingdom' THEN Daily_Activations ELSE 0 END) AS UnitedKingdom
FROM
[motorola.com:sandbox:demo.simulation]
WHERE
CAL_DT<CURRENT_DATE()
GROUP BY 1
ORDER BY 1 DESC
BACKUP
27

28. Demo Daily Report: New Menu Item
28

29. Demo Daily Report: Edit Code (App Script)
29

30. Demo Daily Report:
Example for adding a new menu item
30

31. Demo Daily Report: Key Query (pivot)
31

32. Demo Daily Report: Running the query
32

33. Demo Daily Report: Parsing the results
33

34. Demo Daily Report: Data Sheet
34

35. Demo Daily Report: Summary sheet
35

36. Demo Daily Report: Preparing email
36

37. Demo Daily Report: Email template sheet
37

38. Demo Daily Report: Preparing email
38

39. Demo Daily Report: Sending email
39

40. Demo Daily Report: Signal vs Noise and Main Drivers
40

41. Demo Daily Report: Key Milestones
41

42. Demo Daily Report: Final Email
42

43. Demo Daily Report: Scheduling
43

44. ● Industry overview (mobile)
● Demos
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Wish List
● Q & A
Agenda
Share of time
44

45. Big Query to Big Query ETL
BigFeed
Check-in
Data
(PB)
Stagging
Data
(TB) Reporting
Data
(GB)
BigFeed
45

46. ● Industry overview (mobile)
● Demos
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
Agenda
46

47. Moto Insights: Democratizing Business Intelligence
Old Analytics Portal
1. Require VPN
2. New Report takes weeks to develop
3. New portal features takes months
4. Tableau incompatibility with BigQuery
5. Reports are produce by a centralized team
6. Role management is becoming out of
control
1. Global access with App Engine
2. Programmatic approach (SQL + metadata)
3. Lightweight App Engine framework
(Go/AngularJS) using G API
4. Google Charts and native BQ SQL
5. Google Drive API
6. Google Groups
Moto Insights (GAE+BQ+gChart)
47

48. Product Architecture
Big Query
datasets
Moto
Insights
App
Engine
Google
Analytics
data
Device
Instrumentation
App Engine
Tableau
reports
Big
Feed
App
Engine
Users, Reports
Datastore
Users
Machine
Learned
Models
48

49. Features of Moto Insights
● Responsive design
● Report metadata, Chart widget metadata
● Report sharing
● Report viewing
● Google Drive integration
49

50. Demo Moto Insights:
Main menu & Responsive Design
responsive
design
Front-end based on Bootstrap CSS
and AngularJS
50

51. Demo Moto Insights: Report Details
51

52. Demo Moto Insights: 1st Chart Details
52

53. Demo Moto Insights: Chart Types
53

54. Demo Moto Insights: Dummy Example
54

55. ● Industry overview (mobile)
● Demos
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
Agenda
55

56. Enlightenment Questions for an Analyst
When was this BigQuery table last refreshed?
How often is it refreshed?
How was it created?
Which underlying data sources/tables is it
using?
Who created this table?
Who knows how to use this table?
Where can I find this great query I ran?
Who knows how to use this tag?
How much bandwidth am I using in BigQuery?
How much space are my tables using?
How much does my usage of BigQuery cost?
www.holymolecartoon.com
56

57. How to track Big Query usage?
Google does not provide a data feed on Motorola’s usage of BigQuery.
However three API can help us:
bigquery.jobs.list
List all the Jobs
in a specified project.
Note: use projection = full
to get email of user
bigquery.jobs.get
Retrieve the
specified job by ID.
We created an App Engine (Moto Monitor) to crawl Google API so we can recursively
collect all queries ran (since mid 2013; for a specific list of projects).
The queries are parsed to extract underlying tables used, and the data is stored in the
App Engine datastore as well as in Big Query through the streaming API (every 15
minutes).
bigquery.projects.list
List all (visible) projects
57

58. Product Architecture
Moto Monitor App Engine (Web Service)
default
module
web pages,
CSS, JS, etc.
bqusage
module
user requests
worker
module
CRON jobs Big Querydatastore
queries/tables
information
Google
APIs
58

59. Moto Monitor Browsing
Browse
a table
Browse
a job
Browse
a flow
https://moto-monitor.appspot.com/bq/info/{long table name}
e.g. https://moto-monitor.appspot.com/bq/info/motorola.com:analytics-data:activations.gcp_activations_shipments
https://moto-monitor.appspot.com/bq/jobinfo/{long job name}
e.g. https://moto-monitor.appspot.com/bq/jobinfo/bold-site-589:job_pz_J3anj2HjIz5AEX0_STXPtWb4
https://moto-monitor.appspot.com/bq/flow/{long table name}
e.g. https://moto-monitor.appspot.com/bq/flow/motorola.com:analytics-data:devices.asn_r12
Browse
your usage
https://moto-monitor.appspot.com/bq/about/me
59

60. How often is activations.gcp_foundation3 refreshed?
60

61. How is activations.gcp_foundation3 being populated?
61

62. Example: 1000 files limit bug for BQ Load (Apr 17th)
SELECT
STRING(MSEC_TO_TIMESTAMP(creationtime)) AS creationtime,
id,
User_email,
DestinationProjectId,
DestinationDatasetId,
InputFiles
FROM
[moto-monitor:usage.bq_raw]
WHERE
creationtime>=TIMESTAMP_TO_MSEC(TIMESTAMP('2015-04-16 00:00:00'))
AND JobType='Load'
AND InputFiles>=1000
ORDER BY creationtime
Project: motorola.com:ds-prod
Job:job_qrH3tNUgKT_R84PKWJXM_AATRPM
State: DONE
Statistics
CreationTime: Thu Apr 16 12:30:16 2015
StartTime: Thu Apr 16 12:33:41 2015
EndTime: Thu Apr 16 12:36:08 2015
Gate Time: 205259
Run Time: 146249
TotalBytesProcessed: 0
InputFileBytes: 134834356368
InputFiles: 2048
OutputBytes: 39957719968
OutputRows: 89549136
Dry Run flag: false
Load
AllowJaggedRows: false
AllowQuotedNewlines: false
IgnoreUnknownValues: false
Load.MaxBadRecords: 0
SkipLeadingRows: 0
SourceFormat:NEWLINE_DELIMITED_JSON
SourceUris: [gs://dspipeline-event-
export/transfer/20150416_12/job_gdi_1429186
260434*]
Project: motorola.com:ds-prod
Job: job_XWekiaqjKnHg2kpZQZp_BJbclS0
State: DONE
Statistics
CreationTime: Fri Apr 17 12:27:02 2015
StartTime: Fri Apr 17 12:28:48 2015
EndTime: Fri Apr 17 12:30:02 2015
Gate Time: 105788
Run Time: 74061
TotalBytesProcessed: 0
InputFileBytes: 67622530704
InputFiles: 1000
OutputBytes: 20037554506
OutputRows: 44919153
Dry Run flag: false
Load
AllowJaggedRows: false
AllowQuotedNewlines: false
IgnoreUnknownValues: false
Load.MaxBadRecords: 0
SkipLeadingRows: 0
SourceFormat: NEWLINE_DELIMITED_JSON
SourceUris: [gs://dspipeline-event-
export/transfer/20150417_12/job_gdi_14292726
60897*]
Moto Monitor portal Moto Monitor BQ data
62

63. Which table are impacted by motorola.com:analytics-
data:devices.asn_r12?
63

64. Where can I find this great query I ran the other day?
https://moto-monitor.appspot.com/bq/about/me
https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09
https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09&limit=500
https://moto-monitor.appspot.com/bq/about/me?before=1418223600&limit=500
64

65. Moto Monitor is available in BigQuery too
Id STRING Unique Identifier for the job (ProjectId:JobId)
ProjectId STRING Project Id under which the job was run
JobId STRING Job Id
CreationTime INTEGER Unix Time when the job was submitted
StartTime INTEGER Unix Time when the job started
EndTime INTEGER Unix Time when the job finished
GateTime INTEGER Gating time in ms between CreationTime and
StartTime
RunTime INTEGER Running time in ms between StartTime and
EndTime
TotalBytesProcessed INTEGER Total Bytes scanned for the job
CacheHit BOOLEAN Boolean flag to indicate if cache was used
User STRING Email of user running the job
MD5 STRING MD5 of the full query
Query STRING Query truncated to 18,000 characters
Status STRING Status of the job (=DONE here)
AllowLargeResults BOOLEAN If true, allows the query to produce arbitrarily
large result tables at a slight cost in
performance. Requires destinationTable to be
set.
Priority STRING Specifies a priority for the query. Possible
values include INTERACTIVE and BATCH.
UseQueryCache BOOLEAN Boolean flag to indicate if cache was
requested in the job
DestinationProjectId STRING Define the project where the results of the
query will be written.
DestinationDatasetId STRING Define the dataset where the results of the
query will be written.
DestinationTableId STRING Define the table where the results of the
query will be written.
ErrorLocation STRING Specifies where the error occurred, if present.
ErrorMessage STRING A human-readable description of the error.
ErrorReason STRING A short error code that summarizes the error.
moto-monitor:usage.bq_raw
65

66. Who was using the tag MOT_DEVICE_STATS_L1 in the last 7 days?
66

67. How much bandwidth am I using in BigQuery?
Use the view moto-monitor:usage.bq_view
67

68. Beyond Queries, we also scan Tables
bigquery.projects.list
List projects visible
bigquery.tables.list
List tables within a dataset
bigquery.datasets.list
List datasets within a project
bigquery.tables.get
Get details about a table
datastore
queries
information
user
email
store
details
68

69. A snapshot of Table statistics is kept as well
moto-monitor:usage.daily_table (daily snapshot)
or moto-monitor:usage.snapshot (latest manual snapshot with self-destruction after 3 days)
CreationTime INTEGER Table Creation Time (Unix)
Description STRING Table Description
Etag STRING NULLABLE
ExpirationTime INTEGER Expiration Time (Unix)
FriendlyName STRING Friendly name
Id STRING Unique Id
LastModifiedTime INTEGER Last Modified Time (Unix)
NumBytes INTEGER Number of bytes
NumRows INTEGER Number of rows
Fields STRING Schema definition
ProjectId STRING Project Id
DatasetId STRING Dataset Id
TableId STRING Table Id
Type STRING TABLE or VIEW
View STRING View Query
User STRING Last user who populated
Query STRING Last query used to populate
JobId STRING Last Job Id to populate
RefreshedTime INTEGER Last time it was populated
SnapshotTime STRING Snapshot timestamp
69

70. How did the size of a dataset grow over time?
70

71. How much space are my tables using?
Bigquery Storing Cost =
$0.02 per GB per month,
i.e. $6.83 per TB per day ,
i.e. $2,458 per TB per year
71

72. How much does my usage of BigQuery cost?
$0.02 per GB per month
$6.83 per TB per day
$2,458 per TB per year
Storage Cost
Query Cost $5 per TB
$20,000 per month
for 5 GB/s unit,
i.e. $1.58 per TB*
On-demand Reserved capacity
* Note: for continuous usage of the 5 GB/s bandwidth
72

73. How much does my usage of BigQuery cost?
Assuming that the Motorola bandwidth is elastic, i.e. we always pay for the
optimal number of units (5 Gb/s), we can use $1.58 per TB as a proxy
Caveat:
API Volume ~ Billing Volume
<> Real Volume Used
73

74. Weekly Email to largest BQ users
74

75. Usage statistics
Caveat:
API Volume ~ Billing Volume
<> Real Volume Used
75

76. Usage statistics
Caveat:
API Volume ~ Billing Volume
<> Real Volume Used
76

77. BigQuery outage?
Caveat:
API Volume ~ Billing Volume
<> Real Volume Used
77

78. What’s next with
Moto Monitor ?
Alert & Exceptions Report
78

79. Examples
Data Issue (illustrative)
time (day)
#
Active
Users
Number of Active Users using their camera in US
Possible Root Causes
● Some files don’t get loaded properly
in BigQuery, creating gaps in user
count.
● The instrumentation changed on the
device
● Customer behavior
Business Issue (real life)
#
System
Restarts
Number of System Restarts in Brazil in Oct ‘14
Real life Root Cause
A buggy Android app (Color Notes) doesn’t
handle the timezone change in Brazil
properly, crashing the devices.
79

80. Approach
1. Define a multi-dimensional
cubes with real data. For example:
Day, Product, Market, # Users
taking a picture
2. Each cell becomes then a
time series
3. Clean the data (remove
seasonality, weekday cycle and
any other know perturbation)
* Note: (Bayesian likelihood with knowledge base)
4. Fit trend and establish
volatility band (2 std deviations)
5. Measure variance versus
prediction for each cell (e.g.
market/product/metric) and trigger
an exception if outside band
6. Collect all exceptions into a
matrix and apply fuzzy logic* to
propose potential root causes
(prescriptive analytics)
markets
BR
products
80

81. Exceptions Report POC
https://moto-monitor.appspot.com/fcst/matrix
https://moto-monitor.appspot.com/fcst/matrix?d=2015-01-06
Note: During POC, access is strictly restricted
81

82. Exceptions Report POC
Real life example with Moto E in Spain
https://moto-monitor.appspot.com/fcst/trend?market=Spain&product=Moto%20E
Trend: where we should have been
Actuals: where we are
Story
Investigating with the Spain
GTM team, this large increase is
seasonal and due to “The Three
Kings Day” (Día De Los Reyes
Magos) where sales are usually
larger than pre-Holidays.
82

83. Demo Exception Report
Daily Email on Exceptions/Anomalies Online Report & Drilldown Immediate Learning & Findings
Juno Storm impact on Daily Activations
Daily Activation WoW on Jan 27th 2014
83

84. ● Industry overview (mobile)
● Demos
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q&A
Agenda
84

85. How to democratize
daily/weekly email
report with an
App Engine solution?
Existing Situation
- Numerous teams use Spreadsheet to
send weekly/daily email
- Enable very agile development of email
body
- Ease of connection to Big Query
- Can’t enable easily customization and
open-rate tracking at a user level
- Can’t leverage advance statistics (R in
GCE)
Self-Service Email System
85

86. Self-Service Email System
Email Widget
HTML Header template
HTML Body template
HTML Footer template
SQL to produce data for Body
gDrive Objects
(image,
attachment)
Underlying
Widgets
AppEngine
86

87. ● Industry overview (mobile)
● Demos
o Daily Activations Report (BQ + Spreadsheet)
o Big Feed (Big Query ETL)
o Moto Insights (BQ + Charts + gDrive + GA)
o Moto Monitor (BQ usage & cost optimization)
o Self-Serve Email (Next gen of self-service)
● Q & A
Agenda
?
87