CORPORATE KUNDLI

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)

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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.

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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)