1.
<ul><li>Making and Measuring Marketing Magic </li></ul><ul><li>LBi “Truman Session” talk </li></ul><ul><li>London, February 2010 </li></ul><ul><li>www.ianjindal.com </li></ul>

2.
<ul><li>Making and Measuring Marketing Magic </li></ul><ul><li>Epiphenomenology and Magic </li></ul><ul><li>Point of Sale </li></ul><ul><li>Obama-Preedy Pricing Principle </li></ul><ul><li>Metrics </li></ul>

3.
Number of freely connected nodes where exchange cost is near zero

4.
Number of freely connected nodes where exchange cost is near zero Hyperdata Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+

5.
Number of freely connected nodes where exchange cost is near zero Hyperdata Web2.0 Social Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Business analysis Services Mashups Responsive and self-configuring Responsive; ‘agents’ APML Microformats

6.
When the network knows more than anyone

7.
Number of freely connected nodes where exchange cost is near zero When the network knows more than anyone Hyperdata Web2.0 Social Network Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Business analysis Services Mashups Responsive and self-configuring Responsive; ‘agents’

8.
<ul><li>Over 100 million customers profiled </li></ul><ul><li>40% active across multiple retailers </li></ul><ul><li>70 billion user-generated reviews </li></ul><ul><li>4.2 billion reviews served each month </li></ul><ul><li>575 retailers (October 2009) </li></ul><ul><li>http://www.texastechpulse.com/bazaarvoice_releases_traffic_stats/s-0021683.html </li></ul>When the network knows more than anyone

9.
Number of freely connected nodes where exchange cost is near zero When the network knows more than anyone Hyperdata Web2.0 Social Network Phenomena Metadata Data Endodata Hypodata 0 1 3 10 100 1000s+ Business analysis Services Mashups Responsive and self-configuring Responsive; ‘agents’ Predictive

10.
Hypodata

11.
“ Brain Scanners can predict a decision up to 10 seconds before a person is aware of making that decision” Nature Neuroscience, April 2008 Hypodata

12.
“ ABSTRACTA 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/ Hypodata

13.
Hypodata - MIT’s “Sixth Sense” http://www.flickr.com/photos/whiteafrican/3253881037/ Pattie Maes’s TED talk on MIT’s “Sixth Sense”: http://www.youtube.com/watch?v=blBohrmyo-I Mashups Responsive and self-configuring Responsive; ‘agents’

14.
Augmented and supplemented Reality University of Washington, 2008: http://uwnews.org/article.asp?Search=contact+lens&articleid=39094 <ul><li>Easy to dismiss as hacked-up geekery... </li></ul><ul><li>But - contact lenses with LEDs being developed </li></ul><ul><li>Even without 3D AR, even coloured ‘confirmation lights’ could have impact. </li></ul>

15.
Any sufficiently advanced technology is indistinguishable from magic

16.
Nodes Open Structured Data Business Web 2.0 Social Network Phenomena Epiphenomena ESP?

17.
“ They’re ‘doing’, not just ‘viewing’ – appropriation, not passive “use” <ul><li>With us everywhere </li></ul><ul><li>Accesses data </li></ul><ul><li>Interlinking </li></ul><ul><li>Connected </li></ul><ul><li>Visualisation </li></ul><ul><li>Touch interface... </li></ul><ul><li>Location (GPS) </li></ul><ul><li>Direction (compass) </li></ul><ul><li>Image processing... </li></ul>

18.
“ They’re ‘doing’, not just ‘viewing’ – appropriating the digital domain

19.
Augmented Reality - brands and interaction http://www.metroparisiphone.com/index_en.html

20.
The “Outernet” – Marking and spraying… http://www.junaio.com / <ul><li>Mobile device </li></ul><ul><li>Screen </li></ul><ul><li>GPS </li></ul><ul><li>Gameplay overlays </li></ul><ul><li>Personal scores </li></ul><ul><li>Network scores </li></ul><ul><li>Network-invoked events </li></ul><ul><li>‘ hearts’ and ‘kisses’ left for others... </li></ul><ul><li>Oh, and SMS </li></ul>

23.
How will this impact digital businesses and eCommerce?

24.
<ul><li>“ Concept Store” – taking control of the brand image, ambiance and merchandising </li></ul><ul><li>Training the customer </li></ul><ul><li>Vendor Relationship Management and live prototyping opportunities </li></ul><ul><li>Direct brand engagement - what about the retailer? What’s their value? </li></ul>What’s the point of a store?

25.
Obama-Preedy Pricing Principle <ul><li>“ It’s been a long time coming, but tonight, because of what we did </li></ul><ul><li>on this day, </li></ul><ul><li>2. in this election, </li></ul><ul><li>3. at this defining moment </li></ul><ul><li>Change has come to America” </li></ul><ul><li>President-Elect Obama, Victory Speech, November 2008 </li></ul><ul><li>http://news.bbc.co.uk/1/hi/world/americas/7434843.stm </li></ul>

26.
Obama-Preedy Pricing Principle <ul><li>“ It’s been a long time coming, but today, thanks to profit-algorithms and data, </li></ul><ul><li>on this day, </li></ul><ul><li>in this location, </li></ul><ul><li>for this defining product </li></ul><ul><li>for this individual customer </li></ul><ul><li>Fixed pricing has ended in retail” </li></ul>

27.
Obama-Preedy Pricing Principle <ul><li>Vouchers - expire when stock reaches 10 or fewer (eg buy it now) </li></ul><ul><li>Stacked discounts: sale, cashback, card-holder, end of sale, channel... Why? </li></ul><ul><li>According to availability </li></ul><ul><li>Proximity of alternatives (time and space) </li></ul><ul><li>Time limited: premium for first (eg 3GS), nephew’s birthday </li></ul><ul><li>Extent to which has shopped around </li></ul>

28.
Performance With thanks to Michael Ross, eCommera. Bad analogy is not his responsibility though... KPI type What Example Volume Feed the machine Site visitors, viewers Margin Extract maximum nutrients. Chew well. Profit per visit, per session, per episode Minimise waste Don’t dribble; don’t leave the stalks Minimise out-of-stock visits Return on capital employed Calories in > effort to eat. ie “no salad” ;) Return on inventory, return on equipment, return on staff Retention Eat again, more easily (remember the potato patch) Customer satisfaction, order frequency, churn.

29.
eCommerce metric Sales/Square foot?

30.
eCommerce metric π/εs

31.
π/εs Profit per engagement second. http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel-second

32.
π/εs Profit per engagement second. http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel-second <ul><li>Customer-focused </li></ul><ul><li>Limits are attention and display-space </li></ul><ul><li>Pan-channel </li></ul><ul><li>Commercial </li></ul><ul><li>Cross-functional </li></ul>

33.
Thank you

34.
www.ianjindal.com www.internetretailing.net www.innoparticularorder.com www.europeanecommerceforum.org http://twitter.com/ianjindal Thank you

35.
<ul><li>Making and Measuring Marketing Magic </li></ul><ul><li>LBi “Truman Session” talk </li></ul><ul><li>London, February 2010 </li></ul><ul><li>www.ianjindal.com </li></ul>Thank you