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iLive2014 Presentation | Casper Blicher Olsen - Internal barriers from taking action upon data - and how to overcome them.

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iLive Conference
iLive Conference

While clients are consuming more and more data, crucial business insights get lost in a sea of pixels. In his presentation Casper will discuss how different data visualization concepts and organizational techniques can be applied to the analytical processes to better help organizations integrate actionable data best practices the influences change and create business value. iLive2014 attendees can expect to get another view on why their current digital marketing indicatives sucks, and what they can do about it.

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Barriers preventing action-oriented analytics - and how to overcome them ! Casper Blicher Olsen v2.0
Barriers preventing action-oriented analytics - and how to overcome them
Barriers preventing action-oriented analytics - and how to overcome them ! Specialists in motivation the users online • Digital strategies, analysis and data visualisation • Digital Marketing • Digital Analytics • Teaching & Training ! • Over 12 years experience • Nordic: Denmark – Norway – Sweden • International Clients
Barriers preventing action-oriented analytics - and how to overcome them
It’s all about the data, right? (What the statistics say)
The digital economy last year Source: Google Internal
All that wonderful data Today 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010
All that wonderful data Tomorrow 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 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Marketers love their data!
The (Second) Evolution of Big Data Source: Google Trends
Then came Dan Ariely… (…with his definition of big data)
Source: Google Trends Big Data Big Data is like Teenager Sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it… ! ! Quote from Dan Ariely
The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Source: MIT Sloan Management Review
The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Source: MIT Sloan Management Review
The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Research findings: • Top-performing organisations are twice as likely to apply analytics to activities. • The biggest challenges in adopting analytics are managerial and cultural. • Visualising data differently will become increasingly valuable. Source: MIT Sloan Management Review
It takes more than one person
We might already have individual analytics NINJAS rocking data Chuck Norris style
…but the truth about the rest is something else! And much more boring.
Aligning data visualisation to your analytics processes for better data understanding
The definition of visual analytics From data to visuals to action Visual analytics is the representation and presentation of data that EXPLOITS OUR VISUAL PERCEPTION ABILITIES in order to AMPLIFY COGNITION. - Andy Kirk, author of “Data Visualisation: a successful design process”
The core principals of data visualisation From data to visuals to action What this means…
The visual sense is our dominating sense From data to visuals to action Source: astro.ku.dk/lys/synet.html
The core principles of data visualisation The visual elements
The core principles of data visualisation From data to visuals to action
The core principles of data visualisation From data to visuals to action Color (Hue)
The core principles of data visualisation From data to visuals to action Enclosure + Color (Hue)
Digital Analytics Maturity Framework Making analytics part of the organisation The digital analytics maturity steps
Digital Analytics Maturity Framework Making analytics part of the organisation Technology-centric, engineering-oriented Integrate General Level Clean Collect Convert Report Store Inspired by the work of Stephen Few
Digital Analytics Maturity Framework Making analytics part of the organisation Human-centric, analytics-oriented Report General Level Clean Collect Integrate Convert Store Analyse Explore Monitor Communicate Predict Technology-centric, engineering-oriented Inspired by the work of Stephen Few
Human-centric, analytics-oriented Business-centric, changing behaviour Report Transform Analyse Monitor General Level CXO Level Clean Collect Integrate Convert Store Scale Embed Explore Communicate Predict Digital Analytics Maturity Framework Making analytics part of the organisation Technology-centric, engineering-oriented Inspired by the work of Stephen Few
Defining a data-driven organisation From data to visuals to action The Data-Driven Organisation
Defining a data-driven organisation From data to visuals to action Illustration: Google GACP Scale - Transform Explore - Predict Collect - Report
Defining a data-driven organisation From data to visuals to action Going From Zero To Hero
Give the right people the data they can ACT on From data to visuals to action Illustration: Google GACP
Access to tools and methodologies for better analysis and decisions From data to visuals to action Illustration: Google GACP Visualise Analyse Take Action!
Automate actions in real time From data to visuals to action Illustration: Google GACP Automatisation
How to asses your organisations maturity (Online Analytics Maturity Model)
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
Chief Marketing Officer Digital Analyst Marketing Manager
Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path. Measure your maturity here: http://goo.gl/r7hMdH
Case: Kleenex
How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN What can we learn from Kleenex’s approach? • Applying data to support an adaptive planning process beats the best ‘guesstimates’ • If you can’t find the information you need, think outside the box • When you have the data make sure to visualise it, and deliver it to people who can take action • Make the case an internal success story, and get other parts of the business engaged
Case: Target
How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE
How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE Source:forbes.com
How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE What did Target do, and what can we learn? • In 2002 Target hired Andrew Pole as an statistician ! • Two guys from marketing: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?” ! • They were able to identify about 25 different products that were indicators of pregnancy, including items like unscented lotion, vitamin supplements, hand sanitizers and washcloths. • Andrew was able to create a model that assigned each shopper a pregnancy prediction score based on their purchases ! • This score was then used to send out relevant coupons and advertisements tailored to each woman at a specific point in her pregnancy ! • Target’s Mom and Baby department skyrocketed. Source:newyorktimes.com
Case: The HIPPO approach
Should we optimise our website for mobile devices? Two scenarios Scenario #1 (without data) Gut feeling
Business challenge: Should we optimise our website for mobile?
Business challenge: Should we optimise our website for mobile? Answer: In the absence of data, the HiPPO says no!
Scenario #2 (with data) Data-oriented decision making
Business challenge: Should we optimise our website for mobile? Answer: Yes, because 11,42% of all our visits are from mobile devices
Woot! What does this mean? Oh boy!
Our mobile traffic is equivalent to: the capacity of Michigan Stadium
Takeaways
Recommendation #1 Something to take home First, Think Biggest Focus on the biggest and highest- value opportunities Inspiration: MIT Sloan Management Review
Recommendation #2 Something to take home Start in the Middle Within each opportunity, start with questions, not data Inspiration: MIT Sloan Management Review
Recommendation #3 Something to take home Make Analytics Come Alive Embed insights to drive actions and deliver value Inspiration: MIT Sloan Management Review
Recommendation #4 Something to take home Add, Don’t Detract Keep existing capabilities while adding new ones Inspiration: MIT Sloan Management Review
Recommendation #5 Something to take home Build the Parts, Plan the Whole Use an information agenda to plan for the future Inspiration: MIT Sloan Management Review
The core principals of data visualization From data to visuals to action Finally…
Don’t only think about data, think about action! A key takeaway “Information is powerful. But it is how we use it that will define us.” - Zack Matere, Farmer (Soy, Kenya)
Don’t only think about data, think about action! A key takeaway “Information is powerful. But it is how we use it that will define us.” - Zack Matere, Farmer (Soy, Kenya)
CASPER BLICHER OLSEN Digital Analytics & Insights Lead Mail: casper@iihnordic.com Cell: +45 6 1 3 1 4 0 0 7 Office: +45 7 0 2 0 2 9 1 9 IIH Nordic A/S, Lille Strandstraede 6 1254 Copenhagen K, Denmark Denmark . Norway . Sweden www.iihnordic.com Thank you

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iLive2014 Presentation | Casper Blicher Olsen - Internal barriers from taking action upon data - and how to overcome them.

  • 1. Barriers preventing action-oriented analytics - and how to overcome them ! Casper Blicher Olsen v2.0
  • 2. Barriers preventing action-oriented analytics - and how to overcome them
  • 3. Barriers preventing action-oriented analytics - and how to overcome them ! Specialists in motivation the users online • Digital strategies, analysis and data visualisation • Digital Marketing • Digital Analytics • Teaching & Training ! • Over 12 years experience • Nordic: Denmark – Norway – Sweden • International Clients
  • 4. Barriers preventing action-oriented analytics - and how to overcome them
  • 5. It’s all about the data, right? (What the statistics say)
  • 6. The digital economy last year Source: Google Internal
  • 7. All that wonderful data Today 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010
  • 8. All that wonderful data Tomorrow 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 1010101010100101010010101010101010010101010101010101010101010101010101010010101001010101010101001010101010101010101010101010101010101010101010101010 0101010101001010100101010101010100101010101010101010101010101010101010100101010010101010101010010101010101010101010101010101010101010101010101010101 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  • 10. The (Second) Evolution of Big Data Source: Google Trends
  • 11. Then came Dan Ariely… (…with his definition of big data)
  • 12. Source: Google Trends Big Data Big Data is like Teenager Sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it… ! ! Quote from Dan Ariely
  • 13. The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Source: MIT Sloan Management Review
  • 14. The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Source: MIT Sloan Management Review
  • 15. The impediments to becoming more data driven The adoption barriers organisations face most are managerial and cultural rather than related to data and technology. Research findings: • Top-performing organisations are twice as likely to apply analytics to activities. • The biggest challenges in adopting analytics are managerial and cultural. • Visualising data differently will become increasingly valuable. Source: MIT Sloan Management Review
  • 16. It takes more than one person
  • 17. We might already have individual analytics NINJAS rocking data Chuck Norris style
  • 18. …but the truth about the rest is something else! And much more boring.
  • 19. Aligning data visualisation to your analytics processes for better data understanding
  • 20. The definition of visual analytics From data to visuals to action Visual analytics is the representation and presentation of data that EXPLOITS OUR VISUAL PERCEPTION ABILITIES in order to AMPLIFY COGNITION. - Andy Kirk, author of “Data Visualisation: a successful design process”
  • 21. The core principals of data visualisation From data to visuals to action What this means…
  • 22. The visual sense is our dominating sense From data to visuals to action Source: astro.ku.dk/lys/synet.html
  • 23. The core principles of data visualisation The visual elements
  • 24. The core principles of data visualisation From data to visuals to action
  • 25. The core principles of data visualisation From data to visuals to action Color (Hue)
  • 26. The core principles of data visualisation From data to visuals to action Enclosure + Color (Hue)
  • 27. Digital Analytics Maturity Framework Making analytics part of the organisation The digital analytics maturity steps
  • 28. Digital Analytics Maturity Framework Making analytics part of the organisation Technology-centric, engineering-oriented Integrate General Level Clean Collect Convert Report Store Inspired by the work of Stephen Few
  • 29. Digital Analytics Maturity Framework Making analytics part of the organisation Human-centric, analytics-oriented Report General Level Clean Collect Integrate Convert Store Analyse Explore Monitor Communicate Predict Technology-centric, engineering-oriented Inspired by the work of Stephen Few
  • 30. Human-centric, analytics-oriented Business-centric, changing behaviour Report Transform Analyse Monitor General Level CXO Level Clean Collect Integrate Convert Store Scale Embed Explore Communicate Predict Digital Analytics Maturity Framework Making analytics part of the organisation Technology-centric, engineering-oriented Inspired by the work of Stephen Few
  • 31. Defining a data-driven organisation From data to visuals to action The Data-Driven Organisation
  • 32. Defining a data-driven organisation From data to visuals to action Illustration: Google GACP Scale - Transform Explore - Predict Collect - Report
  • 33. Defining a data-driven organisation From data to visuals to action Going From Zero To Hero
  • 34. Give the right people the data they can ACT on From data to visuals to action Illustration: Google GACP
  • 35. Access to tools and methodologies for better analysis and decisions From data to visuals to action Illustration: Google GACP Visualise Analyse Take Action!
  • 36. Automate actions in real time From data to visuals to action Illustration: Google GACP Automatisation
  • 37. How to asses your organisations maturity (Online Analytics Maturity Model)
  • 38. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
  • 39. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
  • 40. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
  • 41. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
  • 42. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path.
  • 43. Chief Marketing Officer Digital Analyst Marketing Manager
  • 44. Online Analytics Maturity Model Stéphane Hamel, Director of Innovation, Cardinal Path. Measure your maturity here: http://goo.gl/r7hMdH
  • 46. How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
  • 47. How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
  • 48. How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN
  • 49. How Kleenex applied action-oriented analytics COLD AND FLU CAMPAIGN What can we learn from Kleenex’s approach? • Applying data to support an adaptive planning process beats the best ‘guesstimates’ • If you can’t find the information you need, think outside the box • When you have the data make sure to visualise it, and deliver it to people who can take action • Make the case an internal success story, and get other parts of the business engaged
  • 51. How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE
  • 52. How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE Source:forbes.com
  • 53. How Target applied action-oriented analytics PREGNANCY PREDICTION SCORE What did Target do, and what can we learn? • In 2002 Target hired Andrew Pole as an statistician ! • Two guys from marketing: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?” ! • They were able to identify about 25 different products that were indicators of pregnancy, including items like unscented lotion, vitamin supplements, hand sanitizers and washcloths. • Andrew was able to create a model that assigned each shopper a pregnancy prediction score based on their purchases ! • This score was then used to send out relevant coupons and advertisements tailored to each woman at a specific point in her pregnancy ! • Target’s Mom and Baby department skyrocketed. Source:newyorktimes.com
  • 54. Case: The HIPPO approach
  • 55. Should we optimise our website for mobile devices? Two scenarios Scenario #1 (without data) Gut feeling
  • 56. Business challenge: Should we optimise our website for mobile?
  • 57. Business challenge: Should we optimise our website for mobile? Answer: In the absence of data, the HiPPO says no!
  • 58. Scenario #2 (with data) Data-oriented decision making
  • 59. Business challenge: Should we optimise our website for mobile? Answer: Yes, because 11,42% of all our visits are from mobile devices
  • 60. Woot! What does this mean? Oh boy!
  • 61. Our mobile traffic is equivalent to: the capacity of Michigan Stadium
  • 63. Recommendation #1 Something to take home First, Think Biggest Focus on the biggest and highest- value opportunities Inspiration: MIT Sloan Management Review
  • 64. Recommendation #2 Something to take home Start in the Middle Within each opportunity, start with questions, not data Inspiration: MIT Sloan Management Review
  • 65. Recommendation #3 Something to take home Make Analytics Come Alive Embed insights to drive actions and deliver value Inspiration: MIT Sloan Management Review
  • 66. Recommendation #4 Something to take home Add, Don’t Detract Keep existing capabilities while adding new ones Inspiration: MIT Sloan Management Review
  • 67. Recommendation #5 Something to take home Build the Parts, Plan the Whole Use an information agenda to plan for the future Inspiration: MIT Sloan Management Review
  • 68. The core principals of data visualization From data to visuals to action Finally…
  • 69. Don’t only think about data, think about action! A key takeaway “Information is powerful. But it is how we use it that will define us.” - Zack Matere, Farmer (Soy, Kenya)
  • 70. Don’t only think about data, think about action! A key takeaway “Information is powerful. But it is how we use it that will define us.” - Zack Matere, Farmer (Soy, Kenya)
  • 71. CASPER BLICHER OLSEN Digital Analytics & Insights Lead Mail: casper@iihnordic.com Cell: +45 6 1 3 1 4 0 0 7 Office: +45 7 0 2 0 2 9 1 9 IIH Nordic A/S, Lille Strandstraede 6 1254 Copenhagen K, Denmark Denmark . Norway . Sweden www.iihnordic.com Thank you