Check your bias; Feed your empathy

Presented during the Data Driven Business Day at StrataConf + HadoopWorld in NYC, October, 2014

Transcript

1. Hi, I’m
   @farrahbostic
   

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3. Check your bias. 

   Feed your empathy.
   #strataconf
   @farrahbostic
   

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4. What we’ll talk about
   Data
   Structure
   Bias
   Experiments
   Empathy
   

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5. Data
   

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6. Much data.
   

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7. “Drinking from the fire hose”
   

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8. “The Data Will Tell Us
   What to Do”
   

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9. I come from advertising.
   

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10. Structure
    

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11. How (a lot of) marketing
    people deal with data
    

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12. Is this anything?
    

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13. We’re all gonna get fired.
    http://www.rswus.com/images_and_uploads/2014-RSWUS-Agency-Client-New-Business-Report1.pdf
    

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14. It’s not even a new problem!
    “What do you want from me? Fine
    writing? Or do you want to see the
    goddamned sales curve stop moving down
    and start moving up?” 

    - Rosser Reeves
    

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16. Theory v. Information
    “Who needs theory when you have so
    much information? But this is categorically
    the wrong attitude to take toward
    forecasting, especially… where the data
    is so noisy. Statistical inferences are
    much stronger when backed up by
    theory or at least some deeper
    thinking about their root causes.”
    

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17. Bias
    

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18. Deep understanding and empathy,
    developed over time, 

    counteract bias.
    

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19. People are Data
    

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20. But all numbers are not equal
    Some numbers tell you what, 

    but not why
    Some numbers are only ‘half true’
    And some numbers 

    are just lying at scale
    

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21. So why don’t we talk to
    humans?
    Money
    Time
    And…
    

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22. People lie.
    

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23. Professional
    Respondents?
    

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24. “statistically significant”
    “rigorous sample method”
    “information with authority”
    

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26. Bias
    begins
    at
    home.
    Who cares about affluent baby boomers & 

    the advertisers who love them? 

    I want the 18-24 demo!!
    

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27. Marketers rely on vanity metrics
    *
    *
    

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28. Cool story, bro.
    

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29. We need to understand 

    people and behavior 

    to make better predictions 

    and measure outcomes
    more effectively.
    

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30. Experiments
    

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31. How to Hypothesize
    

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32. What’s a hypothesis?
    ὑπόθεσις “to suppose”
    It’s a proposed explanation for something.
    You have to be able to test it.
    The simplest explanation should (usually) be the best.
    It should apply to more than one instance of the thing happening.
    It should help explain other things in the future.
    It should fit with the evidence.
    

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33. The main flaw in
    marketing hypotheses:
    We’re usually
    focused on
    explaining the
    brand, 

    not understanding
    the customer.
    

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34. Empathy
    

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35. We should explain what
    creates value for customers
    We should craft and test hypotheses that:
    Help us make decisions
    Help us create value for our customers
    Help us develop empathy for people so deep we can
    anticipate solutions to problems they can’t yet express
    

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36. We should always 

    be checking for bias
    

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37. What matters is value creation
    CONVERSION ENGAGEMENT
    To your 

    BUSINESS
    To your 

    CUSTOMER
    

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38. Conversion = Business Value
    Acquisition - drawing people into the brand experience
    Revenue - converting visitors into customers
    Referral - converting customers into advocates
    

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39. Engagement = Customer Value
    Activation - people enjoying the experience...
    Retention - enough to come back often...
    Referral - and recommend the experience to others
    

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40. Go and see.
    This is the new intimacy.
    

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41. We have to be prepared to be wrong.
    We have to 

    state our beliefs, 

    encounter reality, 

    learn from the experience, 

    and adjust our beliefs.
    (over and over and over…)
    

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42. Be nice, 

    and listen.
    

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