Ps096 Harvest

Epiphenomenology and engagement

Harvest Digital’s Retail Briefing

London, November 2009

www.ianjindal.com

Number of freely connected nodes where exchange cost is near zero

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

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

When the network knows more than anyone

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’

Over 100 million customers profiled

40% active across multiple retailers

70 billion user-generated reviews

4.2 billion reviews served each month

575 retailers (October 2009)

http://www.texastechpulse.com/bazaarvoice_releases_traffic_stats/s-0021683.html

When the network knows more than anyone

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

Hypodata

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

“ 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

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’

Augmented and supplemented Reality University of Washington, 2008: http://uwnews.org/article.asp?Search=contact+lens&articleid=39094

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.

Any sufficiently advanced technology is indistinguishable from magic

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

“ They’re ‘doing’, not just ‘viewing’ – appropriation, not passive “use”

With us everywhere

Accesses data

Interlinking

Connected

Visualisation

Touch interface...

Location (GPS)

Direction (compass)

Image processing...

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

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

The “Outernet” – Marking and spraying… http://www.junaio.com /

Mobile device

Screen

GPS

Gameplay overlays

Personal scores

Network scores

Network-invoked events

‘ hearts’ and ‘kisses’ left for others...

Oh, and SMS

How will this impact digital businesses and eCommerce?

“ Concept Store” – taking control of the brand image, ambiance and merchandising

Training the customer

Vendor Relationship Management and live prototyping opportunities

Direct brand engagement - what about the retailer? What’s their value?

What’s the point of a store?

Obama-Preedy Pricing Principle

“ It’s been a long time coming, but tonight, because of what we did

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

“ It’s been a long time coming, but today, thanks to profit-algorithms and data,

on this day,

in this location,

for this defining product

for this individual customer

Fixed pricing has ended in retail”

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

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.

eCommerce metric Sales/Square foot?

eCommerce metric π/εs

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