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.
  • Email research by Victoria Bellotti from PARC

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