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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.


  • 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