This talk was given by Jörg von Frantzius, Systems Architect at Aperto, at Magnolia Conference 2015 in Basel. Your site visitors aren't all the same, and you want to target your content to visitor segments: be it for cross- or up-selling, or simply showing the content that matters the most to your visitors. Magnolia provides great concepts to leverage your specific sources of user data for that purpose. Learn how to integrate external user data into Magnolia using Custom Traits, so you can easily target your content to Segments, and preview targeted results without hassles using Personas. Jörg will also demonstrate how content targeting can be achieved on the level of individual page components as opposed to whole pages, substantially reducing content maintenance for Personalization in Magnolia.

1. Building on Magnolia‘s Personalization
Magnolia Conference 2015, Technical Track
Basel, June 9th 2015

2. About Jörg
• Software engineering background
• Joined Aperto in 2010
• Technical Architect for exciting and
demanding Magnolia projects since then
E.g.
• Middle East Broadcasting Company Dubai
http://www.mbc.net/
• Kanton Basel-Stadt http://www.bs.ch/
Jörg von Frantzius
Systems Architect

3. Of the largest owner-
runned advertising
agencies in Germany
BERLIN
BASEL
DUBAI
BEIJING
SAIGON
Top
10
Mio
32
Turnover
in 2014
Platz
01
highest share of digital
services of all full-
service agencies in
Germany
heads
370
Employees
worldwide
MIAMI
We‘re designing the digitalized World.

6. Aperto + Magnolia: strong partnership since 2006
• More than 40 successfully implemented Magnolia projects
• High competence in the execution and implementation of large and
technologically sophisticated projects, e.g. business integration,
high performance and security
• Joint product development with Magnolia
(STK – Standard Templating Kit)
• Over 50 Magnolia-experienced and trained employees
in our departments technology, UX Design, Design and Project
Management
• Experienced with international projects in Germany, China, Dubai,
Switzerland and UK

7. Personalization?

8. What it‘s about
Visitors of your site
Aren’t all the same!
So why show them
All the same content?
Positive effects
Depend on what your site’s goals are …

9. Positive effects
In general:
• Content more relevant to user
• More engaging website
With monetization:
• Increase KPIs (Key Performance Indicators),
e.g. time spent, views per session
• Increase sales
• Upselling (e.g. show next higher tariff)
• Cross-selling (e.g. show complementary product)

10. Required for Personalization: data about visitor
Requirement for Personalization:
Information about the visitor!
Can come from any digital source of information you have
Can use multiple (different) sources at the same time
Must be surfaced in Magnolia as „Custom Traits“
Requires some custom programming
(more on that later..)

11. Possible sources of information about visitors
• Registration data from log-in area
• CRM system, in combination with log-in area
• Predictive behavioural targeting provider
• Own user behaviour tracking
• Social login
(e.g. Facebook, Google+)

12. Personalization in real life
Imagine you go to a shop in real life,
and ask a shop assistant
for recommendations …
He/she will certainly look at you (age, gender, clothes, etc.)
To base his recommendations!
(Likely also learns more about you than any tracking provider will ever do)

13. Connect with back office sales data
In a banking or insurance site, connecting with back office data
Allows to recommend products for cross- or upselling
Or to simply provide access to recommendable information
E.g. health insurance may want to recommend prevention courses
Or specific health information, based on age or gender
Forms, PDFs or frequently needed information
related to specific product that visitor is known to own
Can be prominently shown to reduce calls into call center

14. Reuse back office connection existing already for online services
If you already have online self-services
Connected to user database
This could mean a treasure you can lift with personalization
If you don’t have user self-services yet, or only simple ones
Creating or expanding them and combining with personalization
May create win-win situation:
• Site gains value for the customer (with new services)
• Site gains value for you (with benefits from personalization)

15. Magnolia’s Personalization feature
most of all allows you to
Target editorial content
Maintained within Magnolia itself
You could also develop a component with custom logic based on user data
That works without any of Magnolia’s Personalization features
But that’s a different story …
(content residing outside of Magnolia)
Personalization in Magnolia = targeting content maintained within Magnolia itself

16. Segmentation
Of course, you cannot target content for each user individually
(unless you have very few visitors and a lot of editors …)
So you need to identify “Segments” within your user base
Users within same Segment will receive the same content
Users within same segment have traits in common
Segmentation entirely depends on your business
E.g. following your marketing strategies

17. OK, getting more techical now
(this is not the business track after all)

18. Technical key concepts of Personalization in Magnolia
Magnolia 5.3 introduced
Key concepts for Personalization
As APIs and in UI:
• Traits of visitors
• Rules matching on traits
• Segments making rules reusable
• Page variants containing personalized content
• Personas for previewing personalized results

19. Traits
An example trait definition (found in module configuration):

20. Available Traits
Available traits are:
all traits defined in all “traits” folders of all modules
Magnolia collects them for you and offers them for selection
where applicable, e.g. in rule definitions:

21. Sample Traits
Magnolia contains 3 sample traits out-of-the-box
• Date: allows to target content to date range
(simply the current date, not really a trait of the visitor…)
• Visitor: allows to target new users vs. returning users via cookies
• Country: maps visitor IP to country
These may already be useful to some degree,
but Custom Traits is where things start to get interesting
(to be discussed in detail later on..)

22. Rules
Rules are defined to match visitors / audiences
A rule may allow
to define a value range
For a trait with a discrete
value, e.g. a date
Multiple traits may be
combined
(either matching all
or any of the traits)

23. Segments
A Segment simply gives a name to a rule,
and makes it re-usable in multiple places
Maintained in “Segments” App
Sample shows EMEA Segment
Matching any of its countries

24. Page Variants I
Out-of-the box, targeting currently is only possible on page level

25. Page Variants II
Creating a page variant effectively creates a copy of the page
(component-based variants to be shown in proof-of-concept later on)
For each page-variant, the desired audience can be chosen
By selecting segments, as ad-hoc rules, or both

26. Personas
Personas define sample trait values, maintained in “Personas” App
that you can use for previewing targeted content
Can also add ad-hoc trait
values in „Preview as
visitor“
In addition to values
defined in selected
Persona

27. Personas have discrete trait values
Entering persona trait values is different from entering rules:
While a rule may allow a range, e.g. for a date,
the persona will allow only a single value
That’s because you
want to
preview as a
single visitor,
not as a range of
visitors!

28. You will need
Custom Traits

29. Custom traits
A custom trait is required if you want to connect
Your specific user profile data
with Magnolia’s Personalization
Maybe over time 3rd party modules will be developed
Contributing connection e.g. with Facebook or other social login profiles
Custom traits require custom development
Complex at first sight, but well worth the effort!

30. Custom traits development
Very recommendable documentation in Magnolia Wiki
Let’s look at previous trait definition again …

31. Custom traits development
traitClass e.g. java.util.Date
Java class of the trait value, value determined with each request
ruleField
For entering rules for this trait:
valueField
For entering a single value
of this trait in Persona
and ad-hoc
in “Preview as visitor”

32. Custom traits development
defaultPreviewTrait
Optional. Adds the trait to the Preview app by default, will otherwise turn up
only in Personas’ Traits
defaultRuleTrait
Optional. Adds the trait in the
Choose audience dialog in the
Pages app by default.
Set this property to true for
any traits that editors use
routinely.

33. How does it work?
(For developers …)

34. Custom traits: how does it work?
Let’s look at the two remaining parts of the trait definition:
voterClass
Decides wheter a trait’s concrete value matches
a given rule, e.g. DateVoter: does given single date
fall within date range given in rule?
converterClass
Needed to simulate trait values as request parameters
during “Preview as visitor”
Turns request parameter into trait value, and vice-versa

35. Trait detection in filter class
What you don’t see in the trait definition:
How is the trait’s value determined during request execution?
Happens in specialized request filter class
Puts result in TraitCollector (available for injection in request-scope)
In previous example: DateDetectorFilter
So how do filter, trait collector and voter
work together?

36. Filter, trait collector and voter collaboration
Filter stores trait value in TraitCollector, where voter gets it from

37. Detection and evaluation decoupled through TraitCollector
Trait detection and evaluation (rule matching) are decoupled
Both “real” trait detection on public and trait simulation in preview
put their trait values in TraitCollector
And that’s where trait evaluation gets it from, in both cases
That means:
You can edit and target personalized content
before real trait detection is fully developed

38. Leverage personalization concepts without content variants
Above Personalization API can be leveraged
without using content variants at all
Well-designed concepts that offer a lot of flexibility
E.g. track categories of pages visited in persistent cookie
Build component
that displays automated list of recent articles in these categories
Enjoy using “Preview as visitor” to test the result!

39. Personalization use-cases without variants
We used Rules + Segments to target whole pages (without variants)
In a customer PoC
I.e. audience doesn’t see variant, but is able to see page at all
Implementation:
• Filter for hiding pages that don’t match current visitor
• Customized navigation logic
• Hides navigation items
of pages hidden from current visitor

40. Component-based
Personalization
A Proof of Concept

41. Page variations
Currently targeted variations
must be created on page-level
Traits and segments are assigned
to an entire page
One page copy must be maintained
for each target audience

42. Reducing content maintenance
Being able to target individual components
significantly reduces editorial work
Magnolia has this on roadmap for 5.5
(from what I understood)
Meanwhile PoC demonstrates feasability
with some custom development

43. Targeting components
New “component box”
is introduced
All component variants easily
managed in a tabbed box
Each tab provides content for
one audience group
Traits and segments
directly assigned to a tab

44. Component variations
Segments and rules
can be assigned per component
No longer necessary
to create duplicate content
One content page can be used
to address several audiences
“Preview as visitor”
works as expected
4 component variations
for different IP locations

45. Storage structure
• “Component box” is a component with
• area “tabItems” containing
• “componentBoxItem” components, each carrying
• mgnl:variantTitle
• Actual component to be rendered
• mgnl:ruleSet
mgnl:ruleSet stored by extending Magnolia’s own chooseAudience dialog
Deserialized using Content2Bean,
just like Magnolia does itself with page variants

46. Rendering
Model of area “tabItems”
evaluates componentBoxItems’ rules + segments to determine matching
component, stores result as request attribute
ComponentBoxItemModel.execute()
checks whether component was elected
Returns RenderingModel.SKIP_RENDERING if it wasn’t

47. Thank you!
Jörg von Frantzius, Technical Architect
E-Mail joerg.frantzius@aperto.com
Telefon +49 (0) 30 283 921-0
Aperto AG | Chausseestr. 5 | 10115 Berlin