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The swing towards sites
as intelligence centres
T H E S H I F T
We have evolved
T H E S H I F T
We have evolved
We have become more
More immersive
Personalised
User Journeys
Sites as intellegent centers
T H E N C A M E
GDPR
A M A R K E T T E E R S
Nightmare
But relax…
It’s not all doom and gloom!
GDPR is a good thing
No more Cowboys
UI ISSUE
I t ’ s N o t a D a t a I s s u e
I t ’ s a
I t ’ s N o t a D a t a I s s u e
We don’t ask people for
irrelevant data or personal
data upfront!
We need to do it subtly
I n s t e a d o f
Segmenting our audience
W e u s e i n t e r e s t b u c k e t s
Based on User Behaviour
M i x e d w i t h
Machine Learning
F o r I n s t a n c e
Traditionally we have defined
persona groups.
These persona types can be quite
rigid, people are individuals and
don’t fit neatly into persona groups.
In reality, one size doesn’t
fit all,
people are individuals.
They have a diverse
range of interests
needs that don’t fit
neatly within a broad
categorisation.
Does a 16 year old care about
the latest Forbes News?
E x a m p l e
S h e m i g h t b e !
16.7%
83.3%
Interested
Not Interested
We are looking for conversion not aimless traffic,
it’s always been about getting the right audience
to spend the most time, and buy the right stuff.
Whether that’s a click, a download,
a share, a view or a purchase.
The Age?
S o w h a t s r e l e v a n t
The interest?Or
The king
Human testing in process
Many variables of data, such as age,
location, past purchases, weather,
frequency of transaction, response to
ads, price sensitivity and many
others to start to illuminate
identifiable clusters of behaviour
Analysis of data
This example show how you
can start to:
1. Visualise clusters and
2. Describe the many
discriminatory factors which
inform each cluster.
Data Clusters
Once you have modelled clusters of behaviour
and have them underpinning your digital
channels, then it becomes possible to drive
increasingly personalised experiences
Driving the
Right Offers Right Content Right Behaviours
So where have Valtech
been doing this?
Laterooms.com is an
on-line hotel business
in the UK and allows
customers to choose and
book from a choice of over
200,000 hotels across the UK.
A Valtech data team worked with
Laterooms to explore the many
data variables, in order to
elaborate behavioural clusters
and a data model
What did we do?
So when people booked
We made friends with
external Partners
Drawing data from external sources
We used ads to Profile
Maybe a Male?
The steps that we took to create the initial
data model prototypes included:
• Cleanse data
• Agree what attributes make up an identity
• Apply fuzzy matching on identity attributes.
• Identify behavioural clusters and agree
decision rules for each cluster
• Flag the preferred action against each cluster,
as well as known identity.
Now apply this to all forms of data
Hmm..
I ‘ve got it Live data
mixed with epic
content strategies
Just Genius!
Sites as intellegent centers
Content is king and digital
is the horse it rides
W H A T D I D W E L E A R N
We need to developer
longer relationships with
content strategies
W H A T D I D W E L E A R N
We need to define
behavioural cluster
W H A T D I D W E L E A R N
We need to look at how we
use CRM data differently and
start to use live data better
W H A T D I D W E L E A R N
We need to look at
machine learning to aid in
handling live data
W H A T D I D W E L E A R N
thank you
3/14/18 42

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Sites as intellegent centers

  • 1. The swing towards sites as intelligence centres T H E S H I F T
  • 2. We have evolved T H E S H I F T We have evolved We have become more More immersive
  • 5. T H E N C A M E GDPR
  • 6. A M A R K E T T E E R S Nightmare
  • 7. But relax… It’s not all doom and gloom!
  • 8. GDPR is a good thing
  • 10. UI ISSUE I t ’ s N o t a D a t a I s s u e I t ’ s a I t ’ s N o t a D a t a I s s u e
  • 11. We don’t ask people for irrelevant data or personal data upfront!
  • 12. We need to do it subtly
  • 13. I n s t e a d o f Segmenting our audience
  • 14. W e u s e i n t e r e s t b u c k e t s Based on User Behaviour
  • 15. M i x e d w i t h Machine Learning
  • 16. F o r I n s t a n c e
  • 17. Traditionally we have defined persona groups. These persona types can be quite rigid, people are individuals and don’t fit neatly into persona groups.
  • 18. In reality, one size doesn’t fit all, people are individuals. They have a diverse range of interests needs that don’t fit neatly within a broad categorisation.
  • 19. Does a 16 year old care about the latest Forbes News? E x a m p l e
  • 20. S h e m i g h t b e ! 16.7% 83.3% Interested Not Interested
  • 21. We are looking for conversion not aimless traffic, it’s always been about getting the right audience to spend the most time, and buy the right stuff. Whether that’s a click, a download, a share, a view or a purchase.
  • 22. The Age? S o w h a t s r e l e v a n t The interest?Or
  • 24. Many variables of data, such as age, location, past purchases, weather, frequency of transaction, response to ads, price sensitivity and many others to start to illuminate identifiable clusters of behaviour Analysis of data
  • 25. This example show how you can start to: 1. Visualise clusters and 2. Describe the many discriminatory factors which inform each cluster. Data Clusters
  • 26. Once you have modelled clusters of behaviour and have them underpinning your digital channels, then it becomes possible to drive increasingly personalised experiences Driving the Right Offers Right Content Right Behaviours
  • 27. So where have Valtech been doing this?
  • 28. Laterooms.com is an on-line hotel business in the UK and allows customers to choose and book from a choice of over 200,000 hotels across the UK.
  • 29. A Valtech data team worked with Laterooms to explore the many data variables, in order to elaborate behavioural clusters and a data model What did we do?
  • 30. So when people booked
  • 31. We made friends with external Partners
  • 32. Drawing data from external sources
  • 33. We used ads to Profile Maybe a Male?
  • 34. The steps that we took to create the initial data model prototypes included: • Cleanse data • Agree what attributes make up an identity • Apply fuzzy matching on identity attributes. • Identify behavioural clusters and agree decision rules for each cluster • Flag the preferred action against each cluster, as well as known identity. Now apply this to all forms of data
  • 35. Hmm.. I ‘ve got it Live data mixed with epic content strategies Just Genius!
  • 37. Content is king and digital is the horse it rides W H A T D I D W E L E A R N
  • 38. We need to developer longer relationships with content strategies W H A T D I D W E L E A R N
  • 39. We need to define behavioural cluster W H A T D I D W E L E A R N
  • 40. We need to look at how we use CRM data differently and start to use live data better W H A T D I D W E L E A R N
  • 41. We need to look at machine learning to aid in handling live data W H A T D I D W E L E A R N

Editor's Notes

  1. Long gone of the days with statistic boring sites. Sites that didn’t move, Site that sit in iframes, use lots of navigations. We have evolved and moved towards more engaging and dynamics sites.
  2. We have moved to more Engaging more Dynamic site, we create sites with out boundaries, that offer up jesters instead of clicks. Making the User interface seamless and intuitive.
  3. We started engaging techniques that allowed us to segment our audience, allowing them to personalise there user journeys. Allowing us to develop deeper understandings about user behaviour, in turn creating deeper more meaningful relationships with our audience while boosting brand equity.
  4. GDPR is actually a good thing, it gets rid of the cowboys, people who bombard you with rubbish. You don’t like it when people bombard you why should your clients.
  5. It forces us to be more responsible with peoples data,
  6. How we ask people for data and what we use it for is important.
  7. It forces us to be more responsible with peoples data, You don’t like it when people bombard you why should your clients.
  8. Netflix uses content to profile you. They don’t need to ask you what you like, where you live, your age etc to show you relevant They just need your bank details. The rest is done through profiling what you watch and how far you watch through certain videos to make an assumption
  9. Now look at content strategies that are responsive to live date, processed planed and aggregated live on site.
  10. We need to develop a relationship with content.