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Email research by Victoria Bellotti from PARC
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Email research by Victoria Bellotti from PARC


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  • If a typical knowledge worker has 70 to-dos at any given time of which 80% get done in 2 weeks, that means that people do about 1400 to-dos a year. Think about how often you can recall failing to do an important to do; it’s not very often and certainly a drop in the ocean compared to the hundreds you succeed in doing in time.
  • Transcript

    • 1. PIM Research at PARC
      Victoria Bellotti
      Principal Scientist (
    • 2. Overview
      Personal information management (PIM) in the wild
      And overload
      Embedding resources in email
      Activity management
      What is PIM?
      Personal information management means dealing with documents, messages, scheduling events, to-dos, contacts, notes
      Essentially the work we do to make it possible to do our work
      © 2010 PARC | Confidential
    • 3. Postulating PIM
      3 of 25
    • 4. The Reality of PIM
      PARC | 4
    • 5. Overload: Analysis of Time Spent in Email
      Microanalysis of samples of video observation of email triage
      The time that people are focused on dealing with incoming email
      Heavily interleaved with:
      Reading, skimming, editing, organizing, prioritizing, phone calls etc.
      Breakdown of time spent
      23.1% reading email
      6.2% scanning inbox
      2.4% deleting messages
      2% looking for messages
      9.5% filing messages
      1.1% spent adding attachments
      0.8% opening attachments
      Most of the rest spent writing email and editing documents
      20% of time looking around, searching for and organizing information
      This likely overflows into the rest of the day since email is an archive
      5 of 25
    • 6. Overload: Analysis of Thread Complexity
      Quality not quantity
      ~50% messages are threaded
      Index of complexity
      No. of threads X (days per thread/steps per thread)
      Seems to be a better indicator of overloading than quantity
      Obviously because there’s more to remember to keep track of
      6 of 25
      Active threads of the manager who complained the most about overload
    • 7. Personal Knowledge Pad
    • 8. Snapshot To-do Study
      Average about 70 to-dos and 11 places
      Only 14% of to-dos on paper-lists and e-lists
      2/3 online, 36% in email, 12% in e-calendar
      Distributed across the workplace and elsewhere
      The to-do doesn’t describe the task
      Natural language may not be used
      Contextual and personal cue
      To-dos have multiple roles:
      Reminders: “I would like to remember to do this at an appropriate time”
      Planning tools: “What must I do next?”; “What needs doing soon?”
      Status indicators: “Done”; “Important”; “Priority”
      Indices: “What content is involved in this task?”; “How do I access it?”
      A significant minority of to-dos may not get done
    • 9. All(most) in the Head
      A relatively tidy and explicit list
      Non specific
      Incomplete sentences
      An untidy and less explicit list
      “Beth blah blah”
      Manager at PARC
    • 10. To-dos in the Wild
      We interviewed people in detail about their to-dos once a week for four weeks with a final 5th interview.
      We classified them
      What they were about and where they were stored
      We also coded them for about 30 factors that might affect their getting done, e.g., importance, consequence of not doing, difficulty, etc.
      Each week we asked whether the last week’s to-dos were done
      PARC | 10
    • 11. Significant Determinants of Prioritization: Getting Things Done in a Week
      Hard-to-forget tasks
      Can’t-do-it-now tasks
      Factor Significance (random chance of data)
      Urgency <0.1%
      Customer <0.1%
      Is a meeting <0.1%
      Involving others (not mtg) <0.1%
      Importance 0.1%
      Non-discretionary 1.5%
      Common 5.6%
      Having no reminder 1.2%
      On a to-do list negative <0.1%
    • 12. Conclusions
      People are good at prioritizing
      Only 1% of cases of dropping the ball (but none high priority)
      They just need more help with the PIM
      Resources need to be embedded in their work habitat
      PARC | 12
    • 13. Significant Determinants of Prioritization: Getting Things Done in a Week
      Are these more important?
      Are these less important?
      Factor Significance (random chance of data)
      Urgency <0.1%
      Customer <0.1%
      Is a meeting <0.1%
      Involving others (not mtg) <0.1%
      Importance 0.1%
      Non-discretionary 1.5%
      Common 5.6%
      Why should we care about this data?
      Aren’t people supposed to be bad at prioritization?
      Having no reminder 1.2%
      On a to-do list negative <0.1%
    • 14. Prioritization and “Dropping the Ball”
      • Well how bad are they?
      68% done in a week
      81% done by final interview
      79% I1,81% I2,83% I3and 80% I4done by final (I5) interview
      Little happens after two weeks (lifespan of active to-dos)
      16% were not done but with good reason (16+81=97)
      Only 3% cases of dropping the ball (all non-critical)
      Our participants are successfully optimizing
      Contradicts the popular press
      Resources are working as reminders, status and prioritizers
    • 15. Optimization
      No. Tasks
      The Challenge
      The challenge is to keep the dotted line as far to the left as possible
      This may move to the right in cases of overload, but that’s OK...
      As long as the line is straight
      Popular idea of poor prioritization is not supported
      So we probably don’t need to help with this
      But why do people think they are bad prioritizers?
      Slight evidence that assessing low importance tasks takes so much time that you might as well do them
      But... task management time and effort contributes to overload
      Documented as 20% of time in email
      How do we lower this cost?
      Make it MUCH easier; automate the drudge work
      Not Done
      High Value
      Low Value
      Poor prioritization
    • 16. Embedding Resources in Email
      16 of 32
    • 17. 17 of 25
      In a small trial half of its users continued using it for months after end of study even though it lacked many features of Outlook
    • 18. Optimizing for Activity Inferencing(under DARPA CALO Program)
      PARC | 18
    • 19. Project Objectives
      Simplify PIM and activity management
      UI that increases explicitness of activity context for better ML
      Design Innovation
      UX construct “Activities” that people can interact with
      System offers different human-meaningful ‘types’ (e.g. meeting, hiring)
      User creates instances of each type
      System populates the instance with predetermined containers & behaviors
      When user drags content to activity good stuff happens
      Meanwhile machine learns about this instance of the human activity
      RQ1. Will users adopt pre-designed structures?
      RQ2. Can we incent users to label their content precisely?
    • 20. TV-ACTA
      TaskVista (TV) to-do list
      Activity-Centered Task Assistant (ACTA) embedded in Outlook
      Pre-designed folder component structure
      Paper to-dos
      Drag-and-drop anything into Activity: automatic organization into contacts, documents, correspondence
      Drag-and-drop or type-in to-do and Promote to Activity
    • 21. More Features: Unified Content Collection
    • 22. Structured Documents
      Drag-and-drop Agenda with Attendees and Final Materials Presentations and Documents
    • 23. Structured Email:One menu-selection to email agenda to all Attendees
    • 24. Useful Activity-Related Forms Links
    • 25. Instant Map
      No need to type in address again; address came from agenda
    • 26. Evaluation
      RQ1. Will users adopt pre-designed structures?
      Yes, more Activities created than folders
      RQ2. Can we incent users to label their content?
      Yes, users selected specific Activity types and used components
      Users find Activity template approach appealing in spite of bugs and even without ML benefits
      Justifies further exploration of this approach
    • 27. Ongoing Research: Logging and Visualizing plus Activity Inferencing
      PARC | 27
    • 28. Hybrid Field Research
      PARC | 28