advanced personalization
Post on 15-Aug-2015
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About me
Dirk Arend
Senior IT Manager
§ Software-engineering background
§ Joined Aperto in 2011
§ Now leading a technology team for Aperto PYCO
§ Experienced in complex Magnolia projects for clients in the U.S. and Europe
§ Always interested in new technologies for realizing web solutions
Aperto PYCO
Is a joint venture of Aperto and the PYCO Group after a partnership of many years
Provides creative, strategic and technical skills to bring clients to the next level
Has a growing team which is specialized for the most complex Magnolia projects:
State-of-the-art services for Magnolia development
§ Java engineers
§ Frontend developers
§ System architects
§ Business analysts
§ Quality managers
§ IT project managers
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BERLIN
MIAMI
DUBAI
BEIJING
HO CHI MINH CITY
Continents covered
BASEL
The Right Partners for the Digital Age
20 Years servicing digital agencies and Fortune 500 companies
700+ Employees World-wide
PARIS BRUSSELS
HONG KONG
SEATTLE NEW YORK
NAIROBI
JOHANNESBURG
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Development centers in Ho Chi Minh City and Berlin
Aperto Group PYCO Group Aperto PYCO
Offices around the globe
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„ „ 74% of online consumers get frustrated with websites when content appears that has nothing to do with their interests. Source: 2013 Online Personal Experience Study, by Janrain
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Typical reasons for low user engagement
Static audience group for all
visitors
Individual user interests are not
considered
One site behavior for
various contexts
Less relevant content is delivered
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Objectives for advanced personalization
Deliver the right message at the right time to the right audience.
Providing best matching content
Analyzing user context and user behavior
Using a variety of external data sources
Optimizing content in real time
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Proof of concept for advanced personalization
Optimizing user segmentation with
custom traits
Managing personalized content on component level
Previewing dynamic content in Admin
Central
Understanding user interests with social login
Using customer behavior for automated
recommendations
Improved retargeting with connected CRM
Efficient toolset for enhancing Magnolia CMS
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Proof of concept applied for news website
Use case
§ Providing most relevant news to individual users
Business goals
§ Higher customer engagement
§ Increase revenue generation
Implementation
§ Various enhancements for content personalization with Magnolia 5.3
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Custom messages
Individual recommendations
Localized content
User segmentation with custom traits
Magnolia CMS already provides a set of traits to target a website visitor
Segmentation results are optimized with additional custom traits
Achieving a more personal website experience
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location
user agent
User context browsing
history
User behavior age
gender
interests
Social media account type
date of account creation
CRM
Standard personalization on page level
Content variants are created on page level
Traits and segments are assigned to an entire page
One page copy must be edited for each audience group
Functional scope of Magnolia CMS 5.3
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Advanced personalization on component level
Creating duplicate content is no longer necessary
One content page can be used for diverse audience groups
Well known editing process for personalization of components
Enhancements for Magnolia CMS
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4 component variants for several IP locations
Assigning traits to components
New “component box” is introduced
All component variants are easily managed in a tabbed box
Each tab provides content for one audience group
Traits and segments are directly assigned to a tab
Efficient process for editing personalized content
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Implementation of personalized components
Steps during rendering process:
1. Collecting assigned traits of all variants in a component box
2. Comparing traits with context of incoming page request
3. The first matching component variant is displayed
4. If no match is found, a component variant without assigned trait is displayed
Code changes are limited to rendering model of component box:
§ No changes in CMS core
§ Magnolia CMS can be updated without limitations
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Preview of personalized components
Setting up personas with all available (custom) traits
Previewing pages by selecting personas and traits
Only matching component variants are displayed
Using Magnolia CMS for valuable tests before publishing content
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Advantages of integrating a social login
Customer gets easy access to member areas
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Magnolia CMS retrieves latest social profile each time the user signs in
Value of customer profile increases significantly with social interests
Users’ content consumption can be predicted with increased accuracy
Understanding user interests with Facebook
RESTful webservice of Facebook Graph API provides profile information:
These custom traits are used for delivering personalized content
Customers are retargeted with unique ID of Facebook profile
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§ Location
§ Interests
§ Gender
§ Age
Increasing content consumption with automated recommendations
Teaser carousel is automatically filled with personalized content
Recommendations are determined by applying browsing behavior of user
No manual efforts for selecting content
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Recommendations based on custom trait
“News Interests”
Real-time recommendations with behavioral targeting
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Magnolia CMS updates user statistic about news interests
Magnolia CMS determines content pages which fit to current user interests
Page is delivered with most relevant recommendations
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User requests a webpage which is tagged with news categories
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Building a unified view of customer data
Segmentation is more successful with combined user data of various sources
Magnolia CMS is connected via REST service to CRM OpenCRX
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Customer data is enhanced with additional targeting criteria
Returning users are recognized by existing account in OpenCRX
All targeting information is stored in a single database
Connecting Magnolia CMS to OpenCRX
Social identity is linked with account in OpenCRX
Accounts are automatically created
Each account contains targeting criteria of various sources
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Customer management
Social network
Browsing behavior
Overview of advanced personalization
Social media profile
Unified view of customer information
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User context and browsing behavior
Data sources for user targeting
Personalized website
Logic for aggregating most relevant content
Combined data for user segmentation
Latest user information
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