Process for scoringjob seeking behavior
...The Predictive Analytics Life Cycle in context of Joberate technology
User input ofperson theywant to track
Build (update)person’s unique predictive model
Enrich with Social Data
Deploy predictive model
Output J-Score move to step 3 (happens daily)
Prepare and format person’s data record
Validate and test the predictivemodel
Select and/or Transform
Who is likely to leave?
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2
Tell the system which people youare interested in tracking
(manual input, CSV file, or API)
API
2
Prepare each person’s unique IDfor Social Data enrichment
UIDHRIS / CRMATS / Job board Social Profile
Or manual input or import
Name
Location
Title
...
3 + 4
3 + 4
Analyze and enrich each person’s 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 person’s published content or activities
Following links in people’s
social profiles, adding rele-
vant new content
Following links in the per-
son’s 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
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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
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Extract relevant data, and
build (update) the person’s unique
predictive model
Validate and test the current model
(leverage training and real time data)
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Deploy predictive model (based on what actually happened)
3 + 45 + 6
5 + 6
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J-Score + other informationis updated daily, and
output via API or to
the Joberate dashboard
API