How to Get Your Brand Elected: Big Data Presentation at NRF by David Selinger


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RichRelevance CEO David Selinger's presentation at NRF's Big Show 2014 on "How to Get Your Brand Elected" using big data.

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  • AnalyticsInsightsReportingPrediction & InterruptionFraud detectionProduct recommendationsOptimizationTweakingHill-climbing
  • (no version 2.0, no better quarterly earnings), & there’s no “PERFECT” but keep going (Hill Climbing!)
  • (but might be like YOUR org! (aligning/motivating the behemoth & personalizing at vast scales)
  • AnalyticsInsightsReportingPrediction & InterruptionFraud detectionProduct recommendationsOptimizationTweakingHill-climbing
  • They subsidize with advertising.
  • Product/Infrastructure: 5 projects/yearIT/Finance: IT budget as % of salesTalent: Recruiting against FB, Google, AmazonPace of Innovation: Same 5 projects…Extensive economies of scale
  • Velocity: Data are moving faster—real-time means real-value.Volume: 100’s of PB’s of data vs TB’s of data—e.g., Walmart’s 1995Variety: Social data is messy, rich, and valuable, but how the heck do we work with it?!??!
  • How we got elected with data:We used mostly basic data storage tech—and Hadoop only when appropriate.
  • SELLY Data can be big and dumb--that is unwieldy for the task at hand ... or pursued just because it CAN be, with no thought for value. Especially when 10X the data doesn't mean 10X the insight (i.e. the law of diminishing returns), sometimes small data is just fine. What your data has to be 24/7 is SMART. (Taken from Forbes “In Defense of Big Data” article) Consider the images on this screen to illustrate. Is there more information in the high-resolution picture at the left or the middle?  Absolutely!  But although that image depicts a 10X bigger data set, it’s not conveying 10X the insight.  If I had to speculate, I’d say that informational content is proportional to the logarithm of the quantity of data – a fancy way of reiterating the law of diminishing returns.My third and final point is that one should focus on the diversity of data available.  Don’t give me 10X or 100X or 1000X finer detail.  Instead, I derive far more insight by introducing data of a different type even if it’s small data.  To illustrate: low resolution with color tells a richer story than the high resolution black-and-white.But data can’t just be big. It has to be timely. “We’re in a world now where you can’t wait a whole day to get your data,” says Julie Kim, VP of product at the furniture etailer One Kings Lane. President of Retail Prophet,Doug Mack, agrees that feedback speed is vital: “Big data is not enough. It has to be real-time data that we can act on during the course of the day.” And to be actionable, data must be personal.
  • Learning, Working on it, Integrated into work, Dependent upon it, DNA & Culture
  • How to Get Your Brand Elected: Big Data Presentation at NRF by David Selinger

    1. 1. How Relevance Can Get Your Brand Elected DAVID SELINGER RAYID GHANI
    2. 2. #richrelevance
    3. 3. Our Platform 1. The History of Data 2. How Data Make Money, Win Elections and Other Such Nonsense 3. Demystifying the Mystical Creature, Big Data 4. Next Steps: How to Take the Next Steps #richrelevance
    4. 4. SECTION 1 The History of Data #richrelevance
    6. 6. Getting Value from Data The 3 Tools: 1 Analytics { } 2 Prediction & Interruption Relevance 3 Optimization #richrelevance
    7. 7. SECTION 2 Getting Value from Data OR Getting Elected with Data #richrelevance
    8. 8. Getting Elected with Data #richrelevance
    9. 9. I am a data geek. Intelligence Social Ineptitude Dweeb Nerd Geek Dork Obsession #richrelevance
    10. 10. What am I good at? #richrelevance Data Scientist for Obama How can I make a difference?
    11. 11. The Campaign Everyone’s NOT a Winner… It’s Binary #richrelevance
    12. 12. The Campaign Legacy Orgs Are Nothing Like Start-ups #richrelevance
    13. 13. 1 Analytics Key Campaign Lessons 2 Prediction & Interruption Don’t Disdain Successful Evergreen Channels 3 Optimization DIRECT MAIL #richrelevance EMAIL ADVERTISING
    14. 14. 1 Key Campaign Lessons Predicting vs. changing behavior Analytics 2 Prediction & Interruption 3 Optimization Perfect predictions do not necessarily alter hearts and minds. #richrelevance
    15. 15. 1 Key Campaign Lessons Influencing the Right People #richrelevance Analytics 2 Prediction & Interruption 3 Optimization
    16. 16. 1 2 Prediction & Interruption 3 Key Campaign Lessons Analytics Optimization Q: Who are the best people to persuade and influence? A: The people who are just like you #richrelevance
    17. 17. 1 2 Prediction & Interruption 3 Key Campaign Lessons Analytics Optimization Turning supporters into advocates DONATE #richrelevance
    18. 18. Getting Value from Data The 3 Tools In an Election 1 Analytics 2 Prediction & Interruption 3 Optimization #richrelevance
    19. 19. David Selinger #richrelevance
    20. 20. Legacy Shopping Engagement Conversion Awareness Signage Consideration Retention Retention Purchase Preference Flow Clarity Source: Omni-channel Retail: The Future of Shopping (A study in partnership with Paco Underhill) #richrelevance
    21. 21. Omni-Channel Engagement Awareness Retention Purchase Preference Source: Omni-channel Retail: The Future of Shopping (A study in partnership with Paco Underhill) #richrelevance
    22. 22. 1 Analytics 2 Prediction & Interruption 3 Optimization research #richrelevance Personalization and Data Mining
    23. 23. #richrelevance
    24. 24. Big Data: The 3 V’s Velocity #richrelevance Volume Variety
    25. 25. Surprise! HOW WE GOT ELECTED WITH DATA: Lots of good ol’ data integration with sprinkles of Hadoop dust and other” big data” technologies when appropriate. #richrelevance
    26. 26. Big Data vs. Smart Data #richrelevance
    27. 27. SECTION 4 Let’s Get Going #richrelevance
    28. 28. Where are we? #richrelevance
    29. 29. Where are we? Advanced eCommerce Vendors BIG DATA IQ eCommerce Pureplays Omnichannel Retailers Traditional eCommerce Vendors DATA IQ #richrelevance Niche eCommerce Upstarts Catalogers/ DR Marketers
    30. 30. How do we apply these to our businesses? • Getting Started: Learn Quickly • Getting Integrated: Bite-sized Chunks • Measure yourself: The right metrics • Experiment, Experiment, Experiment. • Scale: Rinse and Repeat or “Big Project” #richrelevance
    31. 31. Some Initial Big Data Retail Projects • • • • SUCCESSFUL Multi-channel marketing attribution Social network demographics for content targeting Multi-channel customer profile creation for analytics, targeting, personalization Datamart creation #richrelevance • • • • UNSUCCESSFUL Data warehouse replacement “Big Data Experiment” Online analytics/segmentation Board of Directors “Check the Box” Project: “Yes, ma’am we’ve got Big Data”
    32. 32. Closing Thoughts #richrelevance
    33. 33. Thank you! ACCESS THE HANDOUT: CONTINUE THE CONVERSATION: DAVID SELINGER To track the progress of Big Data for retailers, subscribe to our regular newsletter “Big Data for Retailers” retail-bigdata@ #richrelevance RAYID GHANI