"Digital Trends" presentation given to the first meeting of the eConsultancy.com 'Alumni' evening, with people from the eConsultancy MSc courses and the Graduate Academy and other guests.

1. Digital Trends
London, January 2009
www.ianjindal.com
www.internetretailing.net

2. Digital Trends
London, January 2009
www.ianjindal.com
www.internetretailing.net

3. Digital Trends
1.Context
2.Retail and Christmas
3.Social
•Data

4. 1. Context

5. 1. Context

6. 2. Christmas

7. •£4.7bn online over Christmas: 14% YOY
•November - highest YOY of 26%
•Growth is slowing in ecommerce but still above offline
• eCommerce - 16% for H2 (IMRG)
• Offline - 0% for H2 (source: ONS)
• Christmas Day: 3.8m (14% down), but sales 21% higher at £102m.
• Customers bargain-hunting
• Pre-Christmas sales and January started immediately after last posting date!
2. Christmas

8. Product. Price. Promise
2. Christmas
118%

9. 3. Social

10. 3. Social

11. • Top style-setters become “Mavens”
• Based on page views AND product clicks
3. Social

12. • Configure all
panels,
materials,
colour
• Preview your
custom shoe
• nikeid.nike.com
3. Social

13. • Configured
option
• Buy “your”
show
• Feedback to
Nike on
trends, colours
• May put into
production
3. Social

14. 4. Data
• Data evolution
– accurate
– enriched
– meta
– actionable
– mashed
– semantic
– autonomous &
predictive

15. 4. Data
• Data evolution
– accurate
– enriched
– meta
– actionable
– mashed
– semantic
– autonomous &
predictive

16. 4. Data
★ Metadata
– Segmentation and
metadata for
increased
relevance.“Behavi
our” is useful
metadata - if you
can capture it -
‘ignore ratio’?
Capture offline as
well as online -
warranties...

17. 4. Data
★ Metadata
– New services
– Links to
commerce

18. 4. Data
• “Doing” not
“viewing”
– Making Social
Networks
“transactional”You
can “do”, not just
“view”From web
to desktopWeb to
web

19. 4. Data
• “Doing” not
“viewing”
– Making Social
Networks
“transactional”You
can “do”, not just
“view”From web
to desktopWeb to
web

20. 4. 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. 4. Data: personal and placeful and propagated

22. 4. Data
– More is More - the network effect
– Network ‘knows’ more than any individual
component or contributor
– ‘ambush’ marketing?

23. 4. Data
– Attention Profiling Markup Language - www.apml.org
– Beyond ‘personal’: interchange and exchange value of behaviour

24. 4. Data
Show us some APML!!

25. 4. Data
Location, location, location (and mobile)

26. 4. Data: epiphenomena and ESP

27. 4. Data
Hyperdata
Epidata
Metadata
Data
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero
Business
analysis
Services
Mashups
Predictive
Responsive and
self-configuring
ESP?

28. 4. Data
Hyperdata
Epidata
Metadata
Data
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero
Business
analysis
Services
Mashups
Predictive
Responsive and
self-configuring
ESP?
Business Web
Social Web
1
Social Web
2
Networked
Web

29. So?

30. ★Customers - cynical, savvy, disloyal, discriminating, demanding
★Market - slowing, saturated, competitive
★Insights - partial, expensive, temporary
★CRM, VRM - just the start. Rounded behaviour generated rounded, multivariate, multi-co-
ordinated, oddly-permissioned data
So?

31. ★Proposition - value, clear, consistent - must “ring true”
★Use don’t own - build on ‘free’
★Invest in data - create, collect, exchange, borrow, give, sell, rent... combine
★Time and place - every time, every place, everywhere: selling and serving by mobile in time
and place
★Look and learn - success is temporary: learn and respond to customers rather than ‘telling
them’ to comply
So what?

32. Thank you
Digital Trends
London, January 2009
www.ianjindal.com
www.internetretailing.net