SlideShare a Scribd company logo
“Helping startups grow into success stories!”
Mobile Metrics and Analytics
• VP Product @ MoEngage
• Product Manager and a Developer Advocate @ Vserv -
Drove developer products - AppWrapper & SDKs
• Android Developer @ TechJini - Lead Android developer
& Project Manager
• Co-organizer @ Blrdroid, a 7000 strong Android
Community.
• Masters in Computer Science from Florida State University
• My Current interests lie in Analytics, Growth & SAAS.
• Twitter : @ravivyas84
• Email: ravi@vyas.me or ravivyas@moengage.com
• Medium: medium.com/ravivyas
• WWW: Ravivyas.com
About Me
What we will work on today:
• Overview of analytics platforms and tools (3-4PM)
• Introduction to relevant mobile metrics for different stages of the Growth
Hacking Funnel (4-5PM)
• How to extract actionable insights from data (5-6PM)
• Learn how to build a culture of being data driven (6-7PM)
AGENDA
What we will work on today:
• Overview of analytics
• Mobile metrics for different stages of the Growth Hacking Funnel
• How to extract actionable insights from data
• Overview platforms and tools
• Learn how to build a culture of being data driven
AGENDA
Overview of analytics
1
What is Analytics?
OVERVIEW OF ANALYTICS
What is Analytics?
From Wikipedia : Analytics is the discovery and communication of
meaningful patterns in data
OVERVIEW OF ANALYTICS
What is Analytics?
From Wikipedia : Analytics is the discovery and communication of
meaningful patterns in data
MY TRAVELS VISUALIZED
OVERVIEW OF ANALYTICS
http://www.statista.com/statistics/430830/share-of-
FACEBOOK AD REACH
0
20000000
40000000
60000000
80000000
100000000
120000000
140000000
160000000
Nigeria India South Africa Indonesia
FB Ad reach
FACEBOOK AD REACH TO POPULATION
0
200000000
400000000
600000000
800000000
1000000000
1200000000
1400000000
Nigeria India South Africa Indonesia
FB Ad reach Population
Descriptive – What HAS happened? - Our MAUs were up 10% last month
- Descriptive Analytics is the examination of data or content, usually manually
performed, to answer the question “What happened?” (or What is happening?)
- Traditional business intelligence (BI) and visualizations such as pie charts, bar
charts, line graphs, tables, or generated narratives
Diagnostic – WHY did this happen?
- Diagnostic Analytics is a form of advance analytics which examines data or
content to answer the question “Why did it happen?”
- Drill-down, data discovery, data mining and correlations.
Predictive – What COULD happen?
- Predictive Analytics is a form of advanced analytics which examines data or
content to answer the question “What is going to happen?” or more precisely,
“What is likely to happen?
- Regression analysis, forecasting, multivariate statistics, pattern matching,
predictive modeling, and forecasting.
Prescriptive – What SHOULD happen?
- Prescriptive Analytics is a form of advanced analytics which examines data or
content to answer the question “What should be done?” or “What can we do to
make _______ happen?”
- Graph analysis, simulation, complex event processing, neural networks,
recommendation engines, heuristics, and machine learning.
TYPES OF ANALYTICS – THE THEORY
TYPES OF ANALYTICS – THE THEORY
http://www.ciandt.com/card/four-types-of-
analytics-and-cognition
• What Happened
• Why it happened
• What may happen
• How to prevent bad things from happening and make good things happen
TYPES OF ANALYTICS – THE THEORY
WHAT HAPPENED
WHY IT HAPPENED
ET TechCrunch Product Hunt
WHAT MAY HAPPEN
WHAT MAY HAPPEN
WHAT MAY HAPPEN
Mobile metrics for different stages of the
Growth Hacking Funnel
2
Mobile metrics for different stages of the
Growth Hacking Funnel
2
Hacking Analytics
HACKING ANALYTICS
HACKING ANALYTICS
HACKING ANALYTICS
HACKING ANALYTICS
- TRUE NORTH - HEALTH METRICS
HACKING ANALYTICS
HACKING ANALYTICS
HACKING ANALYTICS
• Acquisition
• Activation
• Referrals
• Retention
• Revenue
• True North
• Health Metrics
METRICS - HAPTIK
• Acquisition
• Ad Group
• City
• CAC
• ??
• Activation
• Signed Up
• Send Message
• Tutorial done
• Push Preference
• Referrals
• Referrals (if done)
• Social mentions
• Retention
• 1D, 7D , 30D
• Revenue
• Recharges, orders etc.
• True North
• Sessions/ Users
• Health Metrics
• Messages per user
• Avg Topics per user
METRICS - HAPTIK
• Acquisition
• Activation
• Referrals
• Retention
• Revenue
• True North
• Health Metrics
METRICS - SNAPDEAL
• Acquisition
• Ad Group
• City
• CAC
• Reinstalls, ??
• Activation
• Product Viewed
• Product Search
• Referral
• Referrals (if done)
• Social mentions, shares
• Retention
• 1D, 7D , 30D, 60D, Email Opens & Clicks
• Revenue
• Purchases
• True North
• Purchases
• Health Metrics
• Searches per user, Products viewed per user
• Add to Carts per user
• Avg sessions to first purchase
• Avg Product viewed per user
METRICS - SNAPDEAL
• Acquisition
• Activation
• Referrals
• Retention
• Revenue
• True North
• Health Metrics
METRICS - ZOMATO
• Acquisition
• Ad Group
• City
• CAC
• ??
• Activation
• Restaurant Search
• City Selection
• Referrals
• Referrals (if done)
• Social mentions, shares
• Retention
• 1D, 14D , 30D, Email Opens & Clicks
• Revenue
• Calls
• True North
• Views per user
• Health Metrics
• Feedback per user
• Rating per user
• check-in per user
METRICS - ZOMATO
• Acquisition
• Activation
• Referrals
• Retention
• Revenue
• True North
• Health Metrics
METRICS - CLEARTRIP
• Acquisition
• Ad Group
• City
• CAC
• Reinstalls
• Activation
• Trip Search
• Referrals
• Referrals (if done)
• Social mentions, Ticket Forwards
• Retention
• 1D, 30D, 90D, 360D, Email Opens & Clicks
• Revenue
• Booking
• True North
• Booking
• Health Metrics
• Bookings per user
• Categories per user
• Booking per user per Quarter
METRICS - CLEARTRIP
• Acquisition
• Activation
• Referrals
• Retention
• Revenue
• True North
• Health Metrics
METRICS – EXERCISE
How to extract actionable insights
from data
3
• Charts
• Pie
• Bar
• Stacked
• Cohorts
• Funnels
• Pivots
• Data Filters
TOOLS TO EXTRACT DATA
• Charts
• Pie
• Bar
• Stacked
• Cohorts
• Funnels
• Pivots
• Data Filters
TOOLS TO EXTRACT DATA
Plan
NEEDLE IN A HAYSTACK
PLAN
• Figure out what is the data you need
• Figure out what are the inputs you will need
• Go to war with the tech team
Exercise - Clients who sent a campaign each week where user base was > 5000
• What are the inputs?
PLAN
• Figure out what is the data you need
• Figure out what are the inputs you will need
• Go to war with the tech team
Exercise - Clients who sent a campaign each week where user base was > 5000
• What are the inputs?
• Campaigns sent
• date time
• User count
• Aggregate it to each week
• Put on a date histogram
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
LETS LOOK AT SOME DATA
https://medium.com/swlh/diligence-at-social-capital-part-1-accounting-for-user-growth-
4a8a449fddfc#.4p7jqeq4z
https://goo.gl/sKzq8s
LETS LOOK AT SOME DATA
https://medium.com/swlh/diligence-at-social-capital-part-1-accounting-for-user-growth-
4a8a449fddfc#.4p7jqeq4z
https://goo.gl/sKzq8s
TOOLS – PIE CHARTS
TOOLS – STACKED CHARTS
TOOLS – AREA CHARTS
TOOLS – COHORTS
TOOLS – COHORTS
TOOLS – COHORTS http://bslatkin.github.io/cohorts/
TOOLS – COHORTS http://bslatkin.github.io/cohorts/
TOOLS – FUNNELS
TOOLS – PIVOTS
TOOLS – PIVOTS
Overview of Tools & Platforms
4
AMPLITUDE https://amplitude.com/
Cohorts Yes
Timelines Yes
Flows Yes
Funnels Yes
Pivots
Growth Discovery
Engine
Others SQL
Segmentation Yes
A/B Testing
Engagement
Attribution
FLURRY http://flurry.com
Cohorts Yes
Timelines
Flows
Funnels Yes
Pivots
Others
Personas,
Demographics
Segmentation Yes
A/B Testing
Engagement
Attribution
GOOGLE ANALYTICS http:// Google it ;)
Cohorts Beta
Timelines
Flows Yes
Funnels Partly
Pivots Yes
Others
Export to Sheets, Ad
Integration
Segmentation Partly
A/B Testing
Engagement
Attribution
MIXPANEL http://mixpanel.com
Cohorts Yes
Timelines
Flows
Funnels Yes
Pivots
Others
Segmentation Yes
A/B Testing Yes
Engagement Yes
Attribution
LOCALYTICS http://localytics.com
Cohorts Yes
Timelines
Flows
Funnels Yes
Pivots
Others
Segmentation Yes
A/B Testing
Engagement Yes
Attribution Yes
MOENGAGE http://moengage.com
Cohorts Yes
Timelines Yes
Flows
Funnels
Pivots
Others
Segmentation Yes
A/B Testing
Engagement Yes
Attribution Yes
OTHERS
Excel Dies at 1M rows, great for small data sets
Google Sheets Can’t work offline
Tableau Not free
Qlikview Free on windows & qlikview web
Kibana
Technically challenging, powerful
visualization
SQL
Caveman approach, non tech folks will run
the other way
…...
......
OTHERS
Learn how to build a culture of being data
driven
5
CULTURE
• Building Culture is inherently hard
CULTURE
CULTURE
• Open Data culture
• Be driven by numbers
• Plan to track at product launch
• Product specs should have details on what to track
• Retrospect on the data post launch
• Question the data
• Baseline
• Drilldown
• Provide tools to visualize the data
• Live Dashboards
• Data Export tools
Mobile Metrics and Analytics

More Related Content

PPTX
Enterprise Search as a Service at PwC - Viren Patel, PricewaterhouseCoopers
PPTX
Using Signals in Lucidworks Fusion
PDF
Results from the Enterprise Search and Findability Survey 2012
PDF
Search Analytics in Practice
PPTX
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
PDF
Enterprise Search and Findability in 2013
PPTX
Introduction to business analytics.pptx
PPTX
Business Analytics.pptx
Enterprise Search as a Service at PwC - Viren Patel, PricewaterhouseCoopers
Using Signals in Lucidworks Fusion
Results from the Enterprise Search and Findability Survey 2012
Search Analytics in Practice
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
Enterprise Search and Findability in 2013
Introduction to business analytics.pptx
Business Analytics.pptx

What's hot (15)

PPTX
Big Data for HR
PDF
UX Research Workshop, DTX 2020
PDF
Creating a Data-Driven Organization -- thisismetis meetup
KEY
The Why and How of Findability
PDF
Northwestern data visualization - why why why
PPTX
Big Data, Big Investment
PDF
Setting up Data Science for Success: The Data Layer
PPTX
Why Insight Engines Matter in 2020 and Beyond
PPTX
Big Data: How does it fit in your data strategy?
PPTX
#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transf...
PDF
Big Data for HR
PDF
Data Quality: principles, approaches, and best practices
PDF
Leveraging an in-house modeling framework for fun and profit
PDF
The ABC of Data Governance: driving Information Excellence
PDF
Gartner Business Intelligence & Analytics Summit - Munich
Big Data for HR
UX Research Workshop, DTX 2020
Creating a Data-Driven Organization -- thisismetis meetup
The Why and How of Findability
Northwestern data visualization - why why why
Big Data, Big Investment
Setting up Data Science for Success: The Data Layer
Why Insight Engines Matter in 2020 and Beyond
Big Data: How does it fit in your data strategy?
#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transf...
Big Data for HR
Data Quality: principles, approaches, and best practices
Leveraging an in-house modeling framework for fun and profit
The ABC of Data Governance: driving Information Excellence
Gartner Business Intelligence & Analytics Summit - Munich
Ad

Similar to Mobile Metrics and Analytics (20)

PDF
Rapid UX Research Cycles
PDF
Business Analytics and Data mining.pdf
PDF
Approaching Big Data: Lesson Plan
PPTX
BI: Beyond Intelligence
PDF
Mark Farmer - Google Analytics: Business Intelligence for Non-profits
PPTX
Google Analytics Training - full 2017
PDF
CDOVision - RJA Presentation FINAL
PPTX
Data-Science-Fundamentals- Session 2.pptx
PPTX
Unit-I_Big data life cycle.pptx, sources of Big Data
PDF
Growth marketing
PDF
The "Secret" on - How to "read" peoples minds? -
PDF
Data-driven Product Management
PPTX
Intro to Data and Analytics for Startups
PPT
Network Conference LMS Big Data Final 1.24.14
PPTX
How to use your data science team: Becoming a data-driven organization
PPTX
SEO - What is it?
PDF
Winning in Today's Data-Centric Economy (Part 1)
PPTX
Wanta OConnell Presentation 2012 v4
PPTX
All Things Data - Core Tools for Economic Development Practitioners
PPTX
BIG DATA CHAPTER 2 IN DSS.pptx
Rapid UX Research Cycles
Business Analytics and Data mining.pdf
Approaching Big Data: Lesson Plan
BI: Beyond Intelligence
Mark Farmer - Google Analytics: Business Intelligence for Non-profits
Google Analytics Training - full 2017
CDOVision - RJA Presentation FINAL
Data-Science-Fundamentals- Session 2.pptx
Unit-I_Big data life cycle.pptx, sources of Big Data
Growth marketing
The "Secret" on - How to "read" peoples minds? -
Data-driven Product Management
Intro to Data and Analytics for Startups
Network Conference LMS Big Data Final 1.24.14
How to use your data science team: Becoming a data-driven organization
SEO - What is it?
Winning in Today's Data-Centric Economy (Part 1)
Wanta OConnell Presentation 2012 v4
All Things Data - Core Tools for Economic Development Practitioners
BIG DATA CHAPTER 2 IN DSS.pptx
Ad

More from Ravi Vyas (8)

PDF
What Product Market Fit is not
PDF
Key User Lifecycle Metrics for Growth & Engagement
PDF
Android workshop
PDF
Why android first
PPTX
Creating apps that work on all screen sizes
PPTX
Android v 1.1
PPTX
Know thy code
PPTX
Android
What Product Market Fit is not
Key User Lifecycle Metrics for Growth & Engagement
Android workshop
Why android first
Creating apps that work on all screen sizes
Android v 1.1
Know thy code
Android

Recently uploaded (6)

PPTX
Introduction to Packet Tracer Course Overview - Aug 21 (1).pptx
DOC
Camb毕业证学历认证,格罗斯泰斯特主教大学毕业证仿冒文凭毕业证
PDF
Lesson 13- HEREDITY _ pedSAWEREGFVCXZDSASEWFigree.pdf
DOC
证书学历UoA毕业证,澳大利亚中汇学院毕业证国外大学毕业证
PDF
6-UseCfgfhgfhgfhgfhgfhfhhaseActivity.pdf
PPTX
ASMS Telecommunication company Profile
Introduction to Packet Tracer Course Overview - Aug 21 (1).pptx
Camb毕业证学历认证,格罗斯泰斯特主教大学毕业证仿冒文凭毕业证
Lesson 13- HEREDITY _ pedSAWEREGFVCXZDSASEWFigree.pdf
证书学历UoA毕业证,澳大利亚中汇学院毕业证国外大学毕业证
6-UseCfgfhgfhgfhgfhgfhfhhaseActivity.pdf
ASMS Telecommunication company Profile

Mobile Metrics and Analytics

  • 1. “Helping startups grow into success stories!” Mobile Metrics and Analytics
  • 2. • VP Product @ MoEngage • Product Manager and a Developer Advocate @ Vserv - Drove developer products - AppWrapper & SDKs • Android Developer @ TechJini - Lead Android developer & Project Manager • Co-organizer @ Blrdroid, a 7000 strong Android Community. • Masters in Computer Science from Florida State University • My Current interests lie in Analytics, Growth & SAAS. • Twitter : @ravivyas84 • Email: ravi@vyas.me or ravivyas@moengage.com • Medium: medium.com/ravivyas • WWW: Ravivyas.com About Me
  • 3. What we will work on today: • Overview of analytics platforms and tools (3-4PM) • Introduction to relevant mobile metrics for different stages of the Growth Hacking Funnel (4-5PM) • How to extract actionable insights from data (5-6PM) • Learn how to build a culture of being data driven (6-7PM) AGENDA
  • 4. What we will work on today: • Overview of analytics • Mobile metrics for different stages of the Growth Hacking Funnel • How to extract actionable insights from data • Overview platforms and tools • Learn how to build a culture of being data driven AGENDA
  • 7. What is Analytics? From Wikipedia : Analytics is the discovery and communication of meaningful patterns in data OVERVIEW OF ANALYTICS
  • 8. What is Analytics? From Wikipedia : Analytics is the discovery and communication of meaningful patterns in data MY TRAVELS VISUALIZED
  • 11. FACEBOOK AD REACH TO POPULATION 0 200000000 400000000 600000000 800000000 1000000000 1200000000 1400000000 Nigeria India South Africa Indonesia FB Ad reach Population
  • 12. Descriptive – What HAS happened? - Our MAUs were up 10% last month - Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?” (or What is happening?) - Traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives Diagnostic – WHY did this happen? - Diagnostic Analytics is a form of advance analytics which examines data or content to answer the question “Why did it happen?” - Drill-down, data discovery, data mining and correlations. Predictive – What COULD happen? - Predictive Analytics is a form of advanced analytics which examines data or content to answer the question “What is going to happen?” or more precisely, “What is likely to happen? - Regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting. Prescriptive – What SHOULD happen? - Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make _______ happen?” - Graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. TYPES OF ANALYTICS – THE THEORY
  • 13. TYPES OF ANALYTICS – THE THEORY http://www.ciandt.com/card/four-types-of- analytics-and-cognition
  • 14. • What Happened • Why it happened • What may happen • How to prevent bad things from happening and make good things happen TYPES OF ANALYTICS – THE THEORY
  • 16. WHY IT HAPPENED ET TechCrunch Product Hunt
  • 20. Mobile metrics for different stages of the Growth Hacking Funnel 2
  • 21. Mobile metrics for different stages of the Growth Hacking Funnel 2 Hacking Analytics
  • 25. HACKING ANALYTICS - TRUE NORTH - HEALTH METRICS
  • 29. • Acquisition • Activation • Referrals • Retention • Revenue • True North • Health Metrics METRICS - HAPTIK
  • 30. • Acquisition • Ad Group • City • CAC • ?? • Activation • Signed Up • Send Message • Tutorial done • Push Preference • Referrals • Referrals (if done) • Social mentions • Retention • 1D, 7D , 30D • Revenue • Recharges, orders etc. • True North • Sessions/ Users • Health Metrics • Messages per user • Avg Topics per user METRICS - HAPTIK
  • 31. • Acquisition • Activation • Referrals • Retention • Revenue • True North • Health Metrics METRICS - SNAPDEAL
  • 32. • Acquisition • Ad Group • City • CAC • Reinstalls, ?? • Activation • Product Viewed • Product Search • Referral • Referrals (if done) • Social mentions, shares • Retention • 1D, 7D , 30D, 60D, Email Opens & Clicks • Revenue • Purchases • True North • Purchases • Health Metrics • Searches per user, Products viewed per user • Add to Carts per user • Avg sessions to first purchase • Avg Product viewed per user METRICS - SNAPDEAL
  • 33. • Acquisition • Activation • Referrals • Retention • Revenue • True North • Health Metrics METRICS - ZOMATO
  • 34. • Acquisition • Ad Group • City • CAC • ?? • Activation • Restaurant Search • City Selection • Referrals • Referrals (if done) • Social mentions, shares • Retention • 1D, 14D , 30D, Email Opens & Clicks • Revenue • Calls • True North • Views per user • Health Metrics • Feedback per user • Rating per user • check-in per user METRICS - ZOMATO
  • 35. • Acquisition • Activation • Referrals • Retention • Revenue • True North • Health Metrics METRICS - CLEARTRIP
  • 36. • Acquisition • Ad Group • City • CAC • Reinstalls • Activation • Trip Search • Referrals • Referrals (if done) • Social mentions, Ticket Forwards • Retention • 1D, 30D, 90D, 360D, Email Opens & Clicks • Revenue • Booking • True North • Booking • Health Metrics • Bookings per user • Categories per user • Booking per user per Quarter METRICS - CLEARTRIP
  • 37. • Acquisition • Activation • Referrals • Retention • Revenue • True North • Health Metrics METRICS – EXERCISE
  • 38. How to extract actionable insights from data 3
  • 39. • Charts • Pie • Bar • Stacked • Cohorts • Funnels • Pivots • Data Filters TOOLS TO EXTRACT DATA
  • 40. • Charts • Pie • Bar • Stacked • Cohorts • Funnels • Pivots • Data Filters TOOLS TO EXTRACT DATA Plan
  • 41. NEEDLE IN A HAYSTACK
  • 42. PLAN • Figure out what is the data you need • Figure out what are the inputs you will need • Go to war with the tech team Exercise - Clients who sent a campaign each week where user base was > 5000 • What are the inputs?
  • 43. PLAN • Figure out what is the data you need • Figure out what are the inputs you will need • Go to war with the tech team Exercise - Clients who sent a campaign each week where user base was > 5000 • What are the inputs? • Campaigns sent • date time • User count • Aggregate it to each week • Put on a date histogram
  • 44. LETS LOOK AT SOME DATA
  • 45. LETS LOOK AT SOME DATA
  • 46. LETS LOOK AT SOME DATA
  • 47. LETS LOOK AT SOME DATA
  • 48. LETS LOOK AT SOME DATA
  • 49. LETS LOOK AT SOME DATA
  • 50. LETS LOOK AT SOME DATA https://medium.com/swlh/diligence-at-social-capital-part-1-accounting-for-user-growth- 4a8a449fddfc#.4p7jqeq4z https://goo.gl/sKzq8s
  • 51. LETS LOOK AT SOME DATA https://medium.com/swlh/diligence-at-social-capital-part-1-accounting-for-user-growth- 4a8a449fddfc#.4p7jqeq4z https://goo.gl/sKzq8s
  • 52. TOOLS – PIE CHARTS
  • 54. TOOLS – AREA CHARTS
  • 57. TOOLS – COHORTS http://bslatkin.github.io/cohorts/
  • 58. TOOLS – COHORTS http://bslatkin.github.io/cohorts/
  • 62. Overview of Tools & Platforms 4
  • 63. AMPLITUDE https://amplitude.com/ Cohorts Yes Timelines Yes Flows Yes Funnels Yes Pivots Growth Discovery Engine Others SQL Segmentation Yes A/B Testing Engagement Attribution
  • 64. FLURRY http://flurry.com Cohorts Yes Timelines Flows Funnels Yes Pivots Others Personas, Demographics Segmentation Yes A/B Testing Engagement Attribution
  • 65. GOOGLE ANALYTICS http:// Google it ;) Cohorts Beta Timelines Flows Yes Funnels Partly Pivots Yes Others Export to Sheets, Ad Integration Segmentation Partly A/B Testing Engagement Attribution
  • 66. MIXPANEL http://mixpanel.com Cohorts Yes Timelines Flows Funnels Yes Pivots Others Segmentation Yes A/B Testing Yes Engagement Yes Attribution
  • 67. LOCALYTICS http://localytics.com Cohorts Yes Timelines Flows Funnels Yes Pivots Others Segmentation Yes A/B Testing Engagement Yes Attribution Yes
  • 68. MOENGAGE http://moengage.com Cohorts Yes Timelines Yes Flows Funnels Pivots Others Segmentation Yes A/B Testing Engagement Yes Attribution Yes
  • 69. OTHERS Excel Dies at 1M rows, great for small data sets Google Sheets Can’t work offline Tableau Not free Qlikview Free on windows & qlikview web Kibana Technically challenging, powerful visualization SQL Caveman approach, non tech folks will run the other way …... ......
  • 71. Learn how to build a culture of being data driven 5
  • 72. CULTURE • Building Culture is inherently hard
  • 74. CULTURE • Open Data culture • Be driven by numbers • Plan to track at product launch • Product specs should have details on what to track • Retrospect on the data post launch • Question the data • Baseline • Drilldown • Provide tools to visualize the data • Live Dashboards • Data Export tools