Infographic: Process For Scoring Job Seeking Behavior @Joberate

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Text of Infographic: Process For Scoring Job Seeking Behavior @Joberate

  • Process for scoringjob seeking behavior

    ...The Predictive Analytics Life Cycle in context of Joberate technology

    User input ofperson theywant to track

    Build (update)persons unique predictive model

    Enrich with Social Data

    Deploy predictive model

    Output J-Score move to step 3 (happens daily)

    Prepare and format persons data record

    Validate and test the predictivemodel

    Select and/or Transform

    Who is likely to leave?











    Tell the system which people youare interested in tracking

    (manual input, CSV file, or API)



    Prepare each persons unique IDfor Social Data enrichment

    UIDHRIS / CRMATS / Job board Social Profile

    Or manual input or import






    3 + 4

    3 + 4

    Analyze and enrich each persons unique record using publicly available Social Data

    Example of triggers captured from training data that change the score and/or weighting of the data:

    Person who constantly

    follows or likes new compa-

    ny accounts starts to follow

    a new company

    = 1 point increase

    By using people search engines and Social Data aggregators

    - Ensure that the person who is designated for tracking is correct

    - Look for changes in the persons published content or activities

    Following links in peoples

    social profiles, adding rele-

    vant new content

    Following links in the per-

    sons shared content to iden-

    tify other social content or

    social profiles, adding rele-

    vant new content

    Ongoing Social Data valida-

    tion to ensure the person

    being tracked is the same

    Analyze meta-data for the

    sites where the person is

    sharing content, to discover

    potential API related that can

    be leveraged via paid sources

    Person with little following or liking of any job related content starts to follow/subscribe to a new source of job related content

    = 5 point increase

    Person who actively follows or likes job related content starts to follow/subscribe to a new source of job related content

    = 2 point increase

    Person who does not

    actively update profession-

    al section(s) of social media

    profiles, makes an update

    = 9 point increase

    Person who actively updates

    professional section(s) of

    their social media profile, makes an update

    = 3 point increase

    Person who has only a

    few connections with

    recruiters, connects with a

    single new recruiter

    = 4 point increase

    Other factors like timing (frequency, time of day) of the Social Data changes, and also simultaneous Social Data changes in multiple sites have a cumulative impact

    Deploy model:

    Person with very littlefollowing or liking of anycompany accounts startsto follow a new company

    = 8 point increase

    All right reserved.



    3 5


    Content exploration via external paid Social Data providers

    5-Day Replay

    Format normalization

    URL Expansion

    Klout Scores

    Language Detection and Filtering

    Phrase and Keyword Filters

    GGeo Filters

    User Filters

    Format normalization

    URL Expansion

    Plug-and-Play Streams

    Duplicate Exclusion

    Optimized Polling

    Choice of Protocols

    Format normalization

    URL Expansion

    Language Detection

    Data Stream Data Stream Data Stream



    Extract relevant data, and

    build (update) the persons unique

    predictive model

    Validate and test the current model

    (leverage training and real time data)


    Deploy predictive model (based on what actually happened)

    3 + 45 + 6

    5 + 6


    J-Score + other informationis updated daily, and

    output via API or to

    the Joberate dashboard