Notes

Abhi

cc: Kartavya

Job Advt Analysis Tabulation

Function: _______

Advt. No	Position / Advt. Name	Jobsite Uploaded on	Resumes Received (Descending Order)	Cumulative No. of Resumes	% of Resumes
S/No	Curr %

[Sketch showing “10” resumes → cumulative “80” resumes]

Date: 03/05/03

Refining “Project Manhattan”

During last 6/8 months, we must have uploaded some 3000/4000 job-adverts on different jobsites (Monster, Jobstreet, JobsDB) and received over 40,000 email resumes.
It is now high time to carefully analyze what has been happening before (blindly) continuing with Project Manhattan.

Such an analysis becomes all-the-more important/urgent, considering that we are:

about to sign up Naukri @ Rs. 60,000/-
may also add PlacementIndia & CareerAge, before long
continuing to explore more jobsites which have job-alert / FTP / large-resume databases.

We need to do similar analysis for all the 4000 job-adverts & all the 40,000 resumes received during last 6 months (ever since we launched Project Manhattan) & do it FAST!
We should also analyze this entire database (4000 advts / 40,000 resumes),

• Industry-wise
• Function-wise
• Position Name-wise
• Jobsite-wise (from where resumes came), etc.

Since we are not revealing the “Name” of the advertiser, there is no question of that influencing the quantity/quality of response.

Not only do we want to discover:

→ Which jobsites give more resumes,

but also:

→ Which jobsites give more resumes for which functions.

The underlying assumption is that even on a given jobsite, there is no uniform distribution of resumes amongst all the functions.

So, the trick is to unearth:

→ Which jobsites are having good quantity/quality of resumes in:

• Marketing

• Sales

• R&D

• Software

• etc., etc.

→ Which websites produce better (quantity) responses for:

• Senior Positions

• Middle ”

• Junior ”

By conducting some very simple statistical analysis (A/B/C analysis = 80:20 ratio etc.), we would be in a position to discover:

PATTERNS / PROBABILITIES
With help of these emerging ‘patterns,’ we would be able to predict (with reasonable accuracy):
• Expected Response (No. of Resumes that are likely to be received) from each website, for each function, if we were to upload a given job-advt. on that website. This is merely an “extrapolation” of past into future.
A tabulation such as following will emerge:

Function: MKTG

Jobsite (on which advt is uploaded)	Probabilities of Getting 5 Resumes	25 Resumes	50 Resumes	100 Resumes
Monster	0.90	0.70	0.60	0.50
Naukri	0.50	0.40	0.20	0.40
Jobs Ahead	0.30	0.25	0.20	0.10
Jobs DB	0.10	0.08	0.07	0.05
Grand Total

Such tabulations have to be constructed (from existing 4000 advts / 40,000 resumes received) for each FUNCTION.

[Side notes scribbled:]

We have scraped Project Manhattan → so computation of such probabilities is no more required.
BUT it would be of considerable interest to any (Recruitment) Subscriptions to use such analysis.
Simultaneously, this type of analysis should be extended to Jobsite efficiency (response tracking).
This will enable us to advise clients for better ROI on postings.
Applications: resume database mgmt., subscription design, interactive database creation, etc.
- Of course, as more & more job-adverts get uploaded daily & more & more resumes keep arriving daily, the software should automatically re-calculate the “PROBABILITIES” & re-populate the tables.
- Now it is easy to develop “DECISION-RULES” which software will automatically apply (to decide, on which jobsite to upload a given job-advt), the moment Sayida downloads a job-advt.
  
  In fact, the software will, on its own & without human intervention, actually UPLOAD each downloaded job-advt on the BEST / MOST APPROPRIATE jobsite!
  
  With this, we have:
  → Taken the “Guess-Work” out of the process
  → Automated the process (human use of human-beings)
  → Ensured high rate of success
  → Eliminated a lot of “wasted” time / effort / money & vastly increased cost/benefit ratio
We must make Project Manhattan “graduate” to a “Pin-point / Targetted / Laser-guided bomb”!
How soon?

[Signature/initials]
03/05/03

Analysis of Resumes Recd. Between 15/04 & 30/04

No. of Job Advts in the Slab	No. of Resumes in the Slab	CUMULATIVE No. of Job Advts	% of Job Advts	CUMULATIVE No. of Resumes	% of Resumes
3	1131	3		1131
11	1991	14		3122
16	1141	30	2.1	4263	57.6%
13	915	43	3.0	5178	69.9%
17	709	60	4.3	5887	79.5%
22	563	82	5.9	6450	87.1%
36	424	118	8.5	6874	92.9%
64	297	182	13.0	7171	96.9%
137	176	319	22.9	7347	99.2%
1076	55	1395	100.0	7402	100.0%

Interpretation:

This analysis shows that just 4% of the total job-adverts uploaded (i.e., 60 advts) produced nearly 80% of the resumes (i.e., 5887).

That is, 96% of our time/money/effort was WASTE!

So what we need to figure out is:

What Industries / Functions / Design. levels did these 60 job-adverts belong to?
Which jobsite gave best response against which job-advert?
COVERING LETTER
EDITABLE covering-letter (email) which will accompany a job-advt. when it gets “broadcast” to all the delivery-channels selected by the Advertiser/Subscriber.
Subscriber/Advertiser can use this as it is, edit/modify parts of it OR completely substitute it with a totally different draft.
In course of time (V 2.0?), it should be possible for a subscriber (advertiser to create/use), totally different/unique/customised “covering-letters”, for each category/type of delivery-channel.
Sample Draft:
Dear Sir/Madam,
Your subscription – viewership – clientele depends upon how much good-news/hope you bring every day to your readers/subscribers/customers/visitors/viewers etc.

There is no doubt, a job-opening/vacancy is one such news to 42 million unemployed graduates, registered with 900+ employment-exchanges in our country.

Saturday, 3 May 2003