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
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?
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
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.
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.
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.
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.
It forces us to be more responsible with peoples data,
How we ask people for data and what we use it for is important.
It forces us to be more responsible with peoples data, You don’t like it when people bombard you why should your clients.
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
Now look at content strategies that are responsive to live date, processed planed and aggregated live on site.