Patterns of Personalization


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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).

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Patterns of Personalization

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