India
Recruiter / New Feature — Corporate Kundli (3)
(Dated
24 May 2006)
No
corporate HR manager will ever hire a candidate without first reading /
assessing his ImageBuilder.
On
a similar line, no candidate should consider talking to an HR manager
(interview?) without first reading that company’s Corporate Kundli.
Like
in case of ImageBuilder, we will try to construct each Corporate Kundli
(whether a corporate is our subscriber or not) in the form of different boxes /
frames for:
- Write-ups
(text descriptions)
- Data
tables (financial etc.)
- Graphics
I
suppose there are many websites (financial / share broking / credit-rating
agencies, etc.) which already provide “write-ups” and “data tables” for a large
number of companies. Perhaps we may link to these sites (e.g. CMIE / BSE / NSE
/ NASSCOM / CII etc.) rather than re-invent the wheel. Since they specialize,
they will have latest data / info.
Our
(India Recruiter’s) domain knowledge will come into picture if we can
succeed in answering the following questions (of prospective candidates) —
preferably graphically:
→
How long employees stay / work with this company? What is its ATTRITION RATE?
(Graph
sketch)
Y-axis
: % of executives who worked in this company
X-axis
: Years of stay (tenure)
A
typical “Employee Stay Distribution” curve plotted as bell-shaped.
In
ImageBuilder, we are already plotting a Tenure Profile, which
comprises:
- All
executives from all companies
- All
tenures during each executive’s career
What
I have drawn above is a very narrow subset of the total database comprising:
- All
executives who ever worked in a given company
- Duration
(tenure) of working by each such executive
The
graphs will be very revealing!
For
Company A, it could look like this →
(steep
early exit curve – low average tenure, ≈ 2 years)
Whereas,
for Company B, it could look like this →
(balanced
bell curve – average tenure ≈ 5 years)
And
for Company C, it could look like this →
(broad,
right-shifted bell – average tenure ≈ 8 years)
So,
obviously, Company C manages to retain its employees for longer durations
(and therefore may be more desirable to work for).
What
could be the reasons?
Possible
“Reasons”
Could
these “reasons” be —
- Higher
starting salaries at each level of entry / induction, compared to
industry norms
- Higher
annual increment percentages
- Faster
promotions (from one designation level to the next)
- Higher
perks
- Shorter
working hours
- More
holidays / privilege leave / casual leave / sick leave
- Flatter
hierarchy (fewer designation levels)
- Better
training / learning opportunities
- Better
workplace environment
- More
transparent systems
- Objective
performance evaluation
- Performance-linked
incentive pay
- Mentoring
- Above-industry-average
sales growth / profit growth
- Stock
options
- etc.
etc. …
Maybe,
someday, we will develop an
Employee
Attitude Survey
(for
employees — past & present)
…and
quantify the responses (to above motivational questions) on a 1–5 scale.
As
long as we promise not to reveal their identities, past employees may
feel free to participate in such a survey.
This could be their only chance to air their honest opinions about their past
employers without facing any blowback!
Of
course, as an incentive, we could enable such willing participants (past
employees) to view the findings of the survey (tables / graphs) dynamically
/ online as soon as they enter or submit their own responses online.
An
Online Survey Questionnaire can be filled in only by verified,
genuine past employees (by invitation to do so) from our own database
server.
In
the meantime, we are going to capture the following transactions (performed by
various registered jobseekers in relation to a given company — hopefully a
subscriber):
- How
many job ads released?
- •
Time-period-wise
- •
Vacancy-name-wise
- •
Designation-level-wise
- •
Posting-city (location-wise)
- •
Function-wise
- •
Salary-offered-wise
- •
Edu-level-wise
- •
Preferred exp-wise
- •
Industry-background desired-wise
- •
etc. etc.
Jobseekers
would be especially interested to see a graph such as …
(Graph
sketch)
Y-axis
→ Avg. Salary offered (Relative)
X-axis
→ Designation Level
Company
1 → Normal slope
Company
2 → Above-industry-average (steeper)
Company
3 → Below-industry-average (flatter)
In
fact, competitor companies would be extremely keen (perhaps even keener
than jobseekers!) to see such a graph!
For
presenting such a graph, we would have captured all data from job ads.
How
many jobseekers — and who — applied against this
company’s jobs?
Jobseekers
from:
- Which
cities
- What
education level
- Which
designation level
- What
current salary level
- What
industry background
- What
total experience level
- What
functional background
- What
Functional Competence Raw Score (FCRS) wise
We
are capturing data about:
- Who
(which jobseekers)
- Clicked
/ applied online
- For
which job ads
So,
we have to store all “Apply-Online” jobseekers’ responses —
→
Job Ad–wise
→
Company–wise
We
can do data mining of such captured data to develop interesting
graphs.
In
any case, we need to capture such data/statistics to determine trends and
patterns for each and every jobseeker’s or employer’s transaction — in
order to develop a probability-based “Recommendation System”
(what
John Battelle calls Google’s Database of Intentions)
(Dated
24 May 2006)
