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

3

4

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7

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6

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

Email

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)

7

Deploy predictive model (based on what actually happened)

3 + 45 + 6

5 + 6

8

J-Score + other informationis updated daily, and

output via API or to

the Joberate dashboard

API