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The rise of the data scientist

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Intelligent Content Conference Keynote
March 29, 2017
Everyone seems to talking about data scientists, but few really know what they do and how they can help you as a marketer. Katrina will share her personal journey as a marketer to understanding one of our greatest challenges in a new era of evidence based decision making – the science of marketing analytics.

Key takeaways:

A working knowledge of data science in a marketing context
A cliff notes guide to Descriptive, Predictive, and Prescriptive Analytics
The implications of big data and data science on marketing.

Published in: Marketing
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The rise of the data scientist

  1. 1. @katrina_neal • #intelcontent The Rise of the Data Scientist Katrina Neal Content Marketing Evangelist, LinkedIn @katrina_neal
  2. 2. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Plan MeasureCreate
  3. 3. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent I think… I know… VSVS Florian Zettelmeyer Professor of Marketing, Kellogg School of Management
  4. 4. @katrina_neal • #intelcontent
  5. 5. @katrina_neal • #intelcontent “Marketing is the heart of every organisation!” — The Younger Me
  6. 6. @katrina_neal • #intelcontent
  7. 7. @katrina_neal • #intelcontent
  8. 8. @katrina_neal • #intelcontent
  9. 9. @katrina_neal • #intelcontent
  10. 10. @katrina_neal • #intelcontent of you feel somewhat or very respected 66% As a marketer, how respected do you feel in your business? 41% 25%
  11. 11. @katrina_neal • #intelcontent % of CEOs don’t trust marketers at all
  12. 12. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Compliments are cheap. Jaime Pham Content Marketing Evangelist, LinkedIn
  13. 13. @katrina_neal • #intelcontent of you plan or receive tactical budgets 64% How would you describe your budgeting process? 23% 21% 20%
  14. 14. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent When are marketers going to finally realise that their job is to generate incremental (measurable and P&L quantifiable) customer demand for their organisations products and services, and when are they going to start tracking their marketing effectiveness accordingly? Jerome Fontaine Global CEO & Marketing Performance Chief, Fournaise
  15. 15. @katrina_neal • #intelcontent 65% of CEOs think marketers live in Marketing la-la land
  16. 16. @katrina_neal • #intelcontent of you feel you can confidently measure your value 20% Are you able to prove you generated more customer demand for your products and services in a business quantifiable and business measurable way? 45% 20%
  17. 17. @katrina_neal • #intelcontent you are not trained in ROMI 60% Are you trained in Marketing Performance & Return On Marketing Investment (ROMI)?
  18. 18. @katrina_neal • #intelcontent
  19. 19. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Plan MeasureCreate
  20. 20. @katrina_neal • #intelcontent What exactly is a data scientist? Source: Marketing Distillery Mathematics expertise Technology: Hacking skills Data science Business/Strategy acumen DataJobs.com
  21. 21. @TwitterHandle • #intelcontent AGENDA @katrina_neal • #intelcontent Your “Cliff Notes” guide to data science • Descriptive • Predictive • Prescriptive
  22. 22. @katrina_neal • #intelcontent What happened? Response rate Cost per lead Conversion rate Google Analytics Radian 6
  23. 23. @katrina_neal • #intelcontent Which of the following do you use for marketing purposes? Descriptive Analytics: A historical view of results; e.g., Google Analytics and Radian 6 Prescriptive Analytics: e.g., IBM prescriptive analytics solutions Predictive Analytics: Predictive Lead Scoring, Predictive Demand Generation, Predictive Segmentation; e.g., Lattice Engines, Mintigo, EverString None
  24. 24. @katrina_neal • #intelcontent
  25. 25. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Predictability allows small business owners to march into ABC’s “Shark Tank” and capture six-figure deals from investors. Predictability is how the world’s largest brands continuously delight Wall Street investors and increase stock prices. CMOs are under particular scrutiny to transform marketing from a cost center to a predictable profit center. Neil Barlow Enterprise B2B Sales Director at NewsCred
  26. 26. @katrina_neal • #intelcontent
  27. 27. @katrina_neal • #intelcontent
  28. 28. @katrina_neal • #intelcontent
  29. 29. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent One of the biggest threats to measuring marketing impact is unreliable data – from a human or machine. It doesn’t matter; either is damaging. Katrina Neal Content Marketing Evangelist, LinkedIn
  30. 30. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Plan
  31. 31. @katrina_neal • #intelcontent Data-scientist-in-a-box: Predictive Marketing ANALYTICSBIG DATA DECISIONS
  32. 32. @katrina_neal • #intelcontent
  33. 33. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Total addressable market (TAM) identification How big of an opportunity exists?
  34. 34. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Companies based on fit and intent Total addressable market (TAM) identification Segmentation and account selection Demand generation
  35. 35. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Signals that are correlated with propensity to buy Total addressable market (TAM) identification Segmentation and account selection Demand generation Lead scoring
  36. 36. @katrina_neal • #intelcontent Predictive lead scoring discovers patterns in the data that rules-based scoring or gut instinct would simply miss. 131xReturn on Investment SQLs % Lift in conversions ASP Predictive scoring cost
  37. 37. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Create
  38. 38. @katrina_neal • #intelcontent “Creativity” “Data Science” “Research”
  39. 39. @katrina_neal • #intelcontent David Fincher (director) “House of Cards” (U.K.) “House of Cards” (U.S.) Kevin Spacey (Actor)
  40. 40. @katrina_neal • #intelcontent
  41. 41. @katrina_neal • #intelcontent BEFOREBEFORE AFTERAFTER
  42. 42. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Ya Xu Head of Experimentation Principal Staff Engineer & Statistician LinkedIn
  43. 43. @katrina_neal • #intelcontent VSVS
  44. 44. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Measure
  45. 45. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Attribution: The missing link between engagement and revenue?
  46. 46. @katrina_neal • #intelcontent Single touch attribution models First click attribution Last click attribution
  47. 47. @katrina_neal • #intelcontent
  48. 48. @katrina_neal • #intelcontent Q: What is the probability that an Engagement Activity would result in an SQL? Q: Which activities would result in a Return Visit? Content as seen through a Bayesian Network Arcs: showing higher probabilistic relationships Nodes: variables
  49. 49. @katrina_neal • #intelcontent
  50. 50. @katrina_neal • #intelcontent Do you have a data scientist as part of your marketing team today? Do you have any plans to hire a data scientist into your marketing team?
  51. 51. @katrina_neal • #intelcontent Data Scientist Data Scientist-in-a-box Become your own Head of Experimentation
  52. 52. @katrina_neal • #intelcontent
  53. 53. @katrina_neal • #intelcontent Roses are red, Violets are blue. We heart this teacher and hope you do too. Roses are red, Violets are blue. Give to a teacher's classroom near you. Roses are red, Violets are blue. Give to a teacher with the same name as you.
  54. 54. @katrina_neal • #intelcontent
  55. 55. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent
  56. 56. @katrina_neal • #intelcontent Trustworthy ● Data complete ● Statistically valid ● Transparent Design/Deploy/Analyze Ya’s Hierarchy of Experimentation Needs Trustworthy 2010 2014
  57. 57. @katrina_neal • #intelcontent Scalable/Ubiquitous ● Scalable computation ● Self-serve ● Common language Ya’s Hierarchy of Experimentation Needs Trustworthy Scalable/Ubiquitous 2014 2015
  58. 58. @katrina_neal • #intelcontent Fast ● Mitigate risk ● Speed up iterations Ya’s Hierarchy of Experimentation Needs Trustworthy Scalable/Ubiquitous Fast 2015 2016
  59. 59. @katrina_neal • #intelcontent Seamless ● Social activity ● Daily routine ● Regular workflow Experimentation as part of everyone’s: Ya’s Hierarchy of Experimentation Needs Trustworthy Scalable/Ubiquitous Fast Seamless 2016 20172017
  60. 60. @katrina_neal • #intelcontent Intelligent ● Guided decisions ● Knowledge discovery Ya’s Hierarchy of Experimentation Needs Trustworthy Scalable/Ubiquitous Fast Seamless Intelligent 20172017
  61. 61. @katrina_neal • #intelcontent Project Bulletproof Gaurav Nihalani Adam Yinger Miguel Leano Alex Chen
  62. 62. @katrina_neal • #intelcontent The Rise of the Data Scientist…… 01 Be prepared for “I know” vs. “I think” 02 Acknowledge human bias 05 Become your own Head of Experimentation 04 Buy a data-scientist-in-a-box 03 Champion hiring a data scientist
  63. 63. @TwitterHandle • #intelcontent@katrina_neal • #intelcontent Plan MeasureCreate
  64. 64. @katrina_neal • #intelcontent

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