• Save
Patterns of Personalization
Upcoming SlideShare
Loading in...5
×
 

Patterns of Personalization

on

  • 4,228 views

This presentation describes a framework for describing and categorising personalisation as experienced within eCommerce. It explores its application to a number of examples, and discusses the ...

This presentation describes a framework for describing and categorising personalisation as experienced within eCommerce. It explores its application to a number of examples, and discusses the implications (e.g. instances that could in theory exist but don’t).

Statistics

Views

Total Views
4,228
Views on SlideShare
1,867
Embed Views
2,361

Actions

Likes
12
Downloads
0
Comments
2

5 Embeds 2,361

http://isquared.wordpress.com 1474
http://java.dzone.com 879
http://architects.dzone.com 4
http://www.dzone.com 2
http://css.dzone.com 2

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • I guess its because I'd prefer that people contact me directly if they want a copy of the actual deck.
    Are you sure you want to
    Your message goes here
    Processing…
  • Dear author, why did you disable the download feature???
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Patterns of Personalization Patterns of Personalization Presentation Transcript

  • Tony Russell-Rose, PhD
    UXLabs Ltd.
    Patterns of Personalisation
  • Contents
    Definitions
    Personalisation vs. customization
    Dimensions of Personalisation
    Profiling Data
    Profiling Method
    Target
    Scope
    Persistence
    Personalization Patterns
    Examples
    2
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Definitions
    Personalisation vs. customisation
    System-led vs. user-led
    Levels of personalisation
    What is minimum?
    Entry of postcode
    Recently viewed / purchased (e.g. Amazon, Asda)
    Simple on-page display settings (e.g. Argos, Asda)
    Ability to provide an individual user experience
    Contrast with static site experience
    3
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Dimensions of Personalisation
    Profiling data
    what data is acquired?
    From whom is it acquired?
    Profiling method
    how is the profile data acquired & applied?
    Target
    where are effects of personalisation experienced?
    Scope
    what is the extent of the personalisation experience?
    Persistence
    what is the focus of the personalisation approach?
    4
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Profiling data (what data is acquired? From whom?)
    User-provided
    demographic data, interests, location, etc.
    e.g. iGoogle, BBC
    Behavioural
    buying / viewing history
    e.g. Amazon, Virgin
    Individual
    Your personal buying / viewing history
    Collective
    Aggregate viewing / buying patterns
    5
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Profiling method (how is the profile acquired & applied?)
    Explicit
    Preferences + interests (e.g. BBC, Monster, B&Q)
    Implicit
    Role-based
    Segment (e.g. new vs. repeat visitor)
    Behavioural (e.g. Amazon, Virgin Wines)
    Content-based
    Suggestions based on product similarity
    e.g. Amazon
    User-based
    Suggestions based on user similarity
    e.g. Last.fm
    6
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Target (where are the effects experienced?)
    User Interface
    Layout of tools & widgets, theme, colour scheme
    Content
    Widget configuration, content
    Display defaults
    Implicit pre-configuration of interface + content, e.g.
    greater support / richer content for certain types of user
    re-ranking of search results
    Merchandising
    Recommendations
    Related items: cross-sell, up-sell, etc.
    7
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Scope (what is the extent of the experience?)
    Site-wide settings (apply across whole site experience), e.g. BBC
    e.g. location
    Page-specific display options, e.g. Arrow, Farnell, Argos
    e.g. basic/advanced filtering, column pickers, list/gallery view, etc.
    No independent user model
    8
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Persistence (what is the focus of the approach?)
    Short-term temporary interests
    Long-term stable interests
    Default is long-term, across sessions
    9
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Personalization ‘patterns’
    Content portal (e.g. BBC)
    Data=user-provided
    Method=explicit
    Target=interface + content
    Scope=site-wide
    Persistence=long-term
    Basic eCommerce (e.g. Farnell)
    Data=user provided
    Method=explicit
    Target=display defaults
    Scope=page-specific
    Persistence=long-term
    10
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Personalization ‘patterns’
    Behavioural eCommerce (e.g. Amazon)
    Data=behavioural
    Method=implicit
    Target=merchandising
    Scope=site-wide
    Persistence=long-term
    Blended eCommerce (e.g. RS)
    Data=user provided + behavioural
    Method= explicit + implicit
    Target=display defaults + merchandising
    Scope=page-specific (display defaults) + site-wide (merchandising)
    Persistence=long-term
    11
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Personalisation Examples
    User-driven customisation
    Branded Content Portals
    Personal Web Portals
    Collective recommendations
    Most ‘popular’ (purchased, viewed, emailed, etc.)
    Search results re-ranking
    e.g. Google
    Collaborative filtering
    e.g. Last.fm (Music) http://www.last.fm
    User-created alerts
    e.g. Google / Yahoo Alerts
    Implicit + explicit personalisation
    e.g. Amazon
    Deep linking
    e.g. Google search, email promotion
    12
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • User-driven Customisation
    Branded Content Portals
    Drag & drop arrangement of widgets / content panes
    Examples:
    BBC (News) http://www.bbc.co.uk
    Tutsplus (Education) http://tutsplus.com/
    13
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • UXLabs - User Experience Research and Design - www.uxlabs.co.uk
    Personal Web Portals
    Layout & content of tools & widgets
    Theme / colour scheme, widget features, configuration, content
    14
  • Collective Recommendations
    Other items bought by purchasers of the target item
    At the same time
    Over a longer period
    15
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Search Results Re-ranking
    User can explicitly promote or remove results
    Changes preserved for same query
    Could be used to re-weight related queries
    16
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • UXLabs - User Experience Research and Design - www.uxlabs.co.uk
    Collaborative Filtering
    Suggestions based on user similarity
    Best suited to content that is taste-oriented
    Films, music, etc.
    17
  • User-created Alerts
    User builds profile for topic of interest
    e.g. set of terms for monitoring news & web sites
    e.g. Yahoo, Google, Amazon
    18
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Implicit + Explicit Personalisation
    Amazon
    Implicit
    Buying / viewing history
    Buying history is strong endorsement
    Explicit
    Improve recommendations
    Turn off browsing history
    Opt out of 3rd party personalized ads
    19
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Personalisation through Deep Linking
    Implicit data
    Referrer (Google search)
    Unique ID (promotional email)
    Bypass internal search
    Pre-qualified buyers
    UX depends on priorities
    e.g. rapid transaction, stickiness, return visit, other?
    Challenge is to indicate breadth of content, branding, reliability, service, etc.
    20
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Conclusions
    Alignment with overall business/marketing strategy
    What are the priorities? e.g.
    customer acquisition vs. customer retention
    long-time loyalists vs. newer customers
    frequent shoppers vs. biggest spenders
    individual vs. segmented
    Personalisation strategy should fitwithin overall strategy
    Different conceptual model
    Navigational model:
    Where am I? What is here? Where can I go next?
    Personalisation model:
    Who do you think I am? (profile data)
    Why is this here? (rationale / business rules)
    What am I missing?(default experience)
    UI needs to answer these questions
    21
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Conclusions
    Highly differentiated journeys risk alienating misclassified users
    Making accurate predictions is difficult
    Needs and goals change over time
    Can lead to inconsistent UX, missed opportunities
    Confidence in user segmentation is crucial
    What proportion of site users are logged in?
    What does their registration data tell us?
    How accurate is it?
    Defensive strategy is to apply suitable defaults
    e.g. highly visible support vs. hidden (but accessible) support
    Results page: images, columns
    Line level: image, Technical reference (+ data sheet), Attributes, Overview, Range Overview, etc.
    Additional UI controls required
    Excel-style hide columns / button
    Accordion controls, etc.
    22
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk
  • Conclusions
    Balance needed:
    What the merchandiser wants to sell vs. what the user wants to buy
    Margin vs. relevance
    Personalization should not constrain information access
    User must always be able to exit the personalized experience
    Challenges in implicit personalisation
    Offline channel interactions
    Each purchase degrades the training set
    Crude product relationship modelling
    Popular items tell us little
    Directed purchasing behaviour
    Recommendations may be of limited value to a buyer with no purchasing discretion
    Challenges in explicit personalisation
    Many users just accept the default
    Default design must be appropriate + scalable
    Privacy concerns
    Too much explicit user involvement can be counter-productive
    23
    UXLabs - User Experience Research and Design - www.uxlabs.co.uk