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PS084 Future of Digital Marketing - "Making and Measuring Marketing Magic"
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PS084 Future of Digital Marketing - "Making and Measuring Marketing Magic"

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My keynote to Econsultancy's Future of Digital Marketing (#fodm) 2009 conference. …

My keynote to Econsultancy's Future of Digital Marketing (#fodm) 2009 conference.

I take as my theme the contention that marketing's entering an 'age of magic', based upon a data/interaction model which goes beyond 'phenomena' to 'epiphenomena' - giving the appearance of magic.

I then cover some of the challenges this gives in ecommerce and retail: the challenge to the store and point of sale in particular.

Then I introduce the "Obama-Preedy Pricing Principle" (yes, there's an article coming to fully explain this...) and finish by proposing an encompassing KPI for this multichannel, multi-modal etail world: profit per engagement-second.

The keynote was given to 350 delegates in London on 17 June 2009.

http://econsultancy.com/events/future-of-digital-marketing

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  • 1. Magic Making and measuring marketing magic Econsultancy’s “Future of Digital Marketing”, London, June 2009 www.ianjindal.com
  • 2. Future of Digital Marketing 1.Previous Futures 2.Epiphenomenology and Magic 3.Point of Sale 4.Obama-Preedy Pricing Principle 5.Metrics
  • 3. Previous Futures 2006 - “Web 2.0” “The future’s already here...” “The future’s already here: it’s just not evenly distributed”
  • 4. Previous Futures 2007 - “Harnessing Social Media” The “P” word... Mobile from Korea...
  • 5. Previous Futures 2008 - “Meta” “Data, metadata, attention” “The year of mobile”. Really. “Meta” The Future of Digital Marketing: London, June 2008 www.ianjindal.com © Ian Jindal, contents and images unless noted.
  • 6. Future Fatigue - now what? • Still busy with Web2.0! “The future’s already here: it’s just not evenly distributed” • Inwardly-focused • Tools, cranking handles, over- specificity. But - a new synthesis heralds an ‘Age of Magic’ “Meta” The Future of Digital Marketing: London, June 2008 www.ianjindal.com © Ian Jindal, contents and images unless noted.
  • 7. Epiphenomenology. It starts with data
  • 8. Data: We need a framework! Hyperdata Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 9. Data: We need a framework! Hyperdata Metadata Data Business analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 10. Data: We need a framework! Hyperdata Metadata Data Business Services analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 11. Data: We need a framework! Hyperdata Metadata Data Business Mashups Services analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 12. Data: We need a framework! ★ Metadata – Segmentation and metadata for increased relevance. – “Behaviour” is useful metadata - if you can capture it - ‘ignore ratio’? – Capture offline as well as online - warranties...
  • 13. Data: We need a framework! Hyperdata Metadata Data Business Mashups Services analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 14. Data: We 4. Data need a framework! Hyperdata Web2.0 Metadata Data Business Mashups Services analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 15. Data: We need a framework! Hyperdata Web2.0 Metadata Data Business Mashups Services analysis Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 16. Towards Epiphenomenology. No longer your father’s data
  • 17. Data Hyperdata Web2.0 Social Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 18. Data Hyperdata Web2.0 Social APML Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata Microformats 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 19. Data • “Doing” not “viewing” – Making Social Networks “transactional” – You can “do”, not just “view” – From web to desktop – Web to web
  • 20. Data – Google - hCard format added to Search results. – One click to add the result to your address book. – Could do the same with calendar items, or recommentations
  • 21. Data – Attention Profiling Markup Language - www.apml.org – Beyond ‘personal’: interchange and exchange value of behaviour
  • 22. Data Is it better than last year?
  • 23. Data Hyperdata Web2.0 Social APML Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata Microformats 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 24. When the network knows more than anyone
  • 25. When the network knows more than anyone Hyperdata Web2.0 Social Network Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 26. When the network knows more than anyone Hyperdata Web2.0 Social Network Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 27. When the network knows more than anyone • Over 100 million customers profiled • 40% active across multiple retailers • 50 billion user-generated reviews • 4 billion reviews served each month • 525 retailers (May 2009) • http://www.texastechpulse.com/bazaarvoice_releases_traffic_stats/s-0021683.html
  • 28. When the network knows more than anyone Hyperdata Web2.0 Social Network Predictive Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 29. When the network knows more than anyone Hyperdata Web2.0 Social Network Phenomena Predictive Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 30. Predictive? You must need hypodata!
  • 31. Hypodata Hyperdata Web2.0 Social Network Phenomena “Brain Scanners can predict a Metadata Predictive decision up to 10 seconds Responsive and self-configuring before a person is aware of Data Business Mashups Services analysis making that decision” Endodata Responsive; Nature Neuroscience, April 2008 ‘agents’ Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 32. Hypodata - MIT’s “Sixth Sense” Hyperdata Web2.0 Social Network Phenomena Predictive Metadata Responsive and self-configuring Data Business Mashups Services analysis Endodata Responsive; ‘agents’ Hypodata http://www.flickr.com/photos/whiteafrican/3253881037/ Pattie Maes’s TED talk on MIT’s “Sixth Sense”: 0 http://www.youtube.com/watch?v=blBohrmyo-I 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 33. Hypodata - mashed up “ABSTRACT A headphone-type gaze detector for a full-time wearable interface is proposed. It uses a Kalman filter to analyze multiple channels of EOG signals measured at the locations of headphone cushions to estimate gaze direction. Evaluations show that the average estimation error is 4.4® (horizontal) and 8.3® (vertical), and that the drift is suppressed to the same level as in ordinary EOG. The method is especially robust against signal anomalies. Selecting a real object from among many surrounding ones is one possible application of this headphone gaze detector.” http://portal.acm.org/citation.cfm?doid=1125451.1125655 via http://liftlab.com/think/nova/ 2006/06/09/wearable-gaze-detector-in-the-form-of-headphones/
  • 34. But what about hyperdata?
  • 35. Hyperdata: Location-specific Plus gestures, image processing, bio-input... (heartbeat? Match.com?...).
  • 36. Any sufficiently advanced technology is indistinguishable from magic
  • 37. The Gamut of data Hyperdata Web2.0 Social Network Phenomena Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 38. 6. Next? Hyperdata Web2.0 Social Network Phenomena Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Number of freely connected nodes where exchange cost is near zero
  • 39. Any sufficiently advanced technology is indistinguishable from magic
  • 40. Any sufficiently advanced technology is indistinguishable from magic Nodes
  • 41. Any sufficiently advanced technology is indistinguishable from magic Open Structured Data Nodes
  • 42. Any sufficiently advanced technology is indistinguishable from magic ESP? Epiphenomena Open Structured Data Phenomena Network Social Web 2.0 Business Nodes
  • 43. Enabling epiphenomenology: device integration • With us everywhere • Accesses data • Interlinking • Connected • Visualisation • Touch interface... • Location (GPS) • Direction (compass) • Image processing...
  • 44. Bringing data alive PlazeMarks 2D codes Neat, but very “now”
  • 45. Augmented Reality... Layar.eu - overlay ‘realtor’ data on street scenes
  • 46. Augmented Reality
  • 47. Augmented Reality • Easy to dismiss as hacked-up geekery... • But - contact lenses with LEDs being developed • Even without 3D AR, even coloured ‘confirmation lights’ could have impact. University of Washington, 2008: http://uwnews.org/article.asp?Search=contact+lens&articleid=39094
  • 48. How will this impact retail and eCommerce?
  • 49. Basic level - it already does.
  • 50. Basic level - it already does. • AR barcode - by SMS • Price comparison? • Colour options? • Buy online?
  • 51. Shopping: present, yet elsewhere
  • 52. Shopping: present, yet elsewhere
  • 53. Shopping: present, yet elsewhere
  • 54. Shopping: present, yet elsewhere
  • 55. Shopping: present, yet elsewhere
  • 56. Shopping: present, yet elsewhere
  • 57. Shopping: present, yet elsewhere
  • 58. Shopping: present, yet elsewhere
  • 59. Shopping: present, yet elsewhere
  • 60. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 61. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 62. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 63. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 64. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 65. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 66. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 67. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 68. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 69. Elswhere, yet present • Oasis – iPhone App (by NN4M) – Good integration with product – Newsletter and storefinder – Basket... but web checkout for now – Just very, very ordinary and do- able!
  • 70. Yesterday’s future looks more prescient • Nearly 2 years old • All components now exist and are free Copyright Icon Nicholson - http://www.iconnicholson.com/ nrf07/
  • 71. Challenges - some provocations
  • 72. What’s the point of a store? – “Concept Store” – taking control of the brand image, ambiance and merchandising – Training the customer – Vendor Relationship Management and live prototyping opportunities
  • 73. What’s the point of a store? – Nappies - should be a subscription product. Draw for supermarkets, but can’t be great for Pampers... – Razors - POS an “experience shave” or “info point” – Configuring a sofa: the resulting bar code is an augmented reality image of the sofa, with embedded voucher and tracking code – Service point - think “Collect+”: delivered, tested, accepted or returned... from 0700-2300 7 days a week
  • 74. Obama-Preedy Pricing Principle “It’s been a long time coming, but tonight, because of what we did 1. on this day, 2. in this election, 3. at this defining moment Change has come to America” President-Elect Obama, Victory Speech, November 2008 http://news.bbc.co.uk/1/hi/world/americas/7434843.stm
  • 75. Obama-Preedy Pricing Principle “It’s been a long time coming, but today, thanks to profit-algorithms and data, 1. on this day, 2. in this location, 3. for this defining product 4. for this individual customer Fixed pricing has ended in retail”
  • 76. Obama-Preedy Pricing Principle • Vouchers - expire when stock reaches 10 or fewer (eg buy it now) • Stacked discounts: sale, cashback, card- holder, end of sale, channel... Why? • According to availability • Proximity of alternatives (time and space) • Time limited: premium for first (eg 3GS), nephew’s birthday • Extent to which has shopped around
  • 77. Performance KPI type What Example Volume Feed the machine Site visitors, viewers Margin Extract maximum nutrients. Profit per visit, per session, Chew well. per episode Minimise waste Don’t dribble; don’t leave the Minimise out-of-stock visits stalks Return on capital employed Calories in > effort to eat. ie Return on inventory, return “no salad” ;) on equipment, return on staff Retention Eat again, more easily Customer satisfaction, order (remember the potato patch) frequency, churn. With thanks to Michael Ross, eCommera. Bad analogy is not his responsibility though...
  • 78. eCommerce metric Conversion.
  • 79. eCommerce metric Sales/Square foot?
  • 80. eCommerce metric π/εs
  • 81. eCommerce metric π/εs Profit per engagement second. http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel-second
  • 82. eCommerce metric π/εs • Customer- focused Profit per engagement second. • Limits are attention and http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel- second display-space • Pan-channel • Commercial • Cross-functional
  • 83. Taking aim
  • 84. Taking aim - today’s futures • Unprecedented data, integration and access: challenges our notions of ‘shop’ and of activity: “time” is the new “place” • No substitute for wonderful, rich data - it’s the coal to the industrial revolution. But watch the revolution, not the mine! • Channibalisation and other conflict is laughable and inept. • Use of the basics is free: now need retail ‘sparkle’ and credible personality and products to win: for this customer, at this time, for this product!
  • 85. Thank you “Magic” Making and measuring marketing magic Econsultancy’s “Future of Digital Marketing” London, June 2009 www.ianjindal.com www.internetretailing.net