Category Archives: Candidates

Job seekers: Do you exist to Recruiters?

In mid October 2010,  my friend David Perry called me and shared some of his insight.

“Donato, in the Detroit area,  there are hundreds of Exhaust System Engineers, yet when I do a Google Search, I can only find a handful of them.  ”

David was explaining this while speaking to a group of recruiters.  “This is a problem,  these engineers are not on the radar of recruiters.”

The Back story: A recruiters first step in finding a candidate is his own database.  Next, recruiters leverage the Internet for candidates. Job Boards, Social Media and open web searches are the tools of the trade.  Only after the immediate sources are exhausted do recruiters start the process of “direct recruiting”;  looking for new candidates via referrals and many, many conversations.

Most job seekers don’t understand this.

If you are not in the recruiters database and you are not present on the Internet, to the recruiter, you don’t exist.

David had impeccable timing.  Over the previous year, I had been absorbing all I could in the realm of search engine optimization (SEO).   In my own initiatives, I had earned the top spot in Google many times.   “How can SEO help job seekers”?  I thought.

While on the phone, I did a quick Google search for “Exhaust System Engineer”.  David was right; few of these engineers were available via a search engine query.  Next,  I proposed an hypothesis to David.  He liked it.

On October 19, 2010  I registered ExhaustSystemEngineer.com.  It cost about $8 from GoDaddy.com.

Using WordPress.org, I set up a blog and hosted the domain for an additional $20 for the year.  No technical knowledge is needed.  If you don’t know how to do it, the people at Godaddy are very helpful.  Total cost $28.

Next, I added a few excerpts from articles about exhaust systems.  The single paragraph had links to the original article.  After adding some content to the site, I found some articles about exhaust systems to comment on, leaving my blog address.  (it is important here to leave real comments and show an interest in someone else’s work, otherwise it is seen as comment spam).

As the last step I signed up for Twitter and created the username ExhaustEngineer.  My first Tweet was an announcement of my blog, ExhaustSystemEngineer.com.  The total time spent to do this exercise was about 1 hour.  If you were doing it for the first time, it may take you 2-3 hours to get familiar with WordPress.

On October 21, 2010,  2 days after registering the domain, creating a blog and adding some content,  a the first Google search result for Exhaust System Engineer was ExhaustSystemEngineer.com.

If I was a real Exhaust System Engineer, the next step would be to add my resume and contact information to blog and keep it updated with fresh content.

After 3 years: If you google: Exhaust System Engineer, the site I created almost 3 years ago is still #1.

Remember, if you are not present on the Internet, you don’t exist to most recruiters.  The difference between being found or not is taking action.

 

 

What you know about Internet Resume searching is wrong

Do you use search engines to look for resumes on the Internet?   Do you use exclusions such as “-jobs”  or  “-submit”?

If you do. Stop it. Read on and I’ll tell you why.

First a story about Easter hams.

To understand what I am going to say about searching for resumes, you will need to be in the right frame of mind. Here we go…

A little girl was closely watching her mother prepare the Easter Ham. She was five years old, a great age for asking questions about the world.  She watched her mother prepared the glaze, preheated the oven and brought out the large roasting pan.   In an automatic fashion, her mother took a large knife and sliced off 2 full inches of meat from each end of the ham.

The little girl,  Sarah, smiled as a question came to mind.

“Mommy, why do you cut the ends off the ham?”  she asked.

As if startled the mother replied, unconvincingly  “I don’t know Sarah, my mom always did it.  Maybe it is so the glaze gets inside. ”

Not being satisfied with the answer, Sarah tracked down Grandma.

“Grandma” She asked.  “I just saw mommy cut off the two ends of the Easter Ham.  She said that she learned it from you.  Why did you make the Easter Ham that way?”

Grandma answered.  “That is a good question, Sarah, but I learned it from my mom, your great grandma.  I always thought that it was so the Ham cooked faster.”

Again, unsatisfied, Sarah tracked down, Great Grandma, the family Matriarch.

“Great grandma”, She asked as she crawled up on her lap.  “Mommy cut the ends off the Easter Ham. She thought is was so the glaze flavor got into the ham.  She did this because Grandma did it.  Grandma thought it was so the Ham would cook faster.  Grandma learned it from you. ”

With anticipation, Sarah asked her Great Grandma. “Grandma, why did you cut the ends off the Easter Ham?”

Grandma, wise as she was old, chuckled and answered.  “Sarah, when I married your great grandfather, the roasting pan we got for our wedding was too small for a Christmas Ham.”

“We cut the ends off the Ham so it would fit in the pan”.
xmas ham

Such is the progression of knowledge.  There is no fault when we inherit a practical idea that worked in the past, yet is anachronistic.  In the case of the Easter Ham, a practical, real world solution should have lived and died within a single generation, a single iteration.  However, it continued until one with a child’s mind, a questioning mind, wanted to know why.  When she was not satisfied with the answer, went on a journey of discovery.

Looking at resume search with a “Beginners Mind”

In the past 2 year I’ve taken a bit of a journey in questioning how people use search engines to search the Internet.

Observation:  Top Internet searchers, myself included, had an innate set of beliefs that they held.  These observations eventually evolved into The 8 Laws of Internet Search,  which are a set of axioms for searching the Internet.

At this point I want to make a disclaimer:  I am really, really good at finding things on the Internet. This is not due to any formal training, nor did I have the advantage of a teacher or mentor.  I am self-taught.  I have literally been immersed in searching the Internet for the last 15 years.

Second disclaimer:  I do not include myself as one of the search-string guru’s out there.  To be a search string guru, you need to be current, know the latest websites that are out there, as well as the latest capabilities of each of the search engines; you need to be immersed in the searching.  My immersion is in the underlying rules.

I recently had a conversation with a search string guru .  We agreed that the best analogy was that I design the aircraft and the search string gurus are the pilots.  Works for me.

So what about resumes and searching the Internet?

If I attempted to research the state of resume search, without a basis or set of axioms to work from, I would not have known where to start.  Fortunately, I decided to use the 8 Laws of Internet Search as a starting point. With a special emphasis on the first 3.

8 laws of internet search

So the question I decided to ask myself is: How do the commonly taught practices of resume search stack up to the Laws of Internet Search?  This was a definable goal.   Caveat: My focus is “Open web” resume searches and not searches within a controlled environment like Monster.com or CareerBuilder.com.

1-law of environment
The Law of Environment. Trainers do an excellent good job talking about the various search engines, their capabilities and limitations.

Industry score on the Law of Environment: A+

2-law of permutation

In taking The Law of Permutation into consideration, I found 2 areas that were very different.

1.  Boolean search methods

Sub-score:  B.   Trainers are clear on the concepts that you must search using multiple permutations such as “VP of Sales”, “Vice President of Sales”,  “VP Sales”, etc.  However, the reality is that you may need 15-20 title combinations to reach all possible results.

2.  Semantic search methods

Sub-score: C.  A good deal of mis-information is being spread about semantic search.  Some of this stems from irresponsible vendors that are trying to make a buck.  It would not be a big deal,  if trainers actually tested, scientifically, what they started teaching.  The funny thing is that the value proposition is significant with semantic search.  Say what it can (and can not do) and those vendors will have happy customers with proper expectations.  I shouldn’t be too harsh here, in the early days, I believed the software from Broadlook was meant for everyone.  It is not.  Setting clear expectations of technology capabilities is the mark of a mature vendor.

Semantic search is great when you have a type of resume that is well identified and the rules have been built.  However, throw it a niche area that has not been cataloged and it will fall flat.  Advice:  If you are looking for a commodity position like a .NET programmer, semantic search can work marvels.  If you are working in a niche area, pick a semantic search engine that can be trained by inputting sample resume data.  In the later case, you may have to do the leg work with good old Boolean search first.  Also, ask your semantic search vendor if they use exclusions when they mine search engines.  If they do, twist their arm until they stop.  It’s an old Easter Ham.

Industry score on the Law of Permutation: C+


3-law of completeness
The Law of Completeness.    Widely taught methodologies, that have not been questioned in years (like the Easter Ham) are yielding approximately 65%.  If you get 65% on a math test, that is not a good grade.   The first example is not using the full available results from a search string query.  If a google search yields 380 results,  the Law of Completeness states that you must work with the entire set of results for maximum yield.

Completeness is not being reached. Why?  When trainers first started teaching how to use search engines (before google),  there were limitations in the technology.  Those limitations were:

(1) No high accuracy method to screen out page results that were NOT a resume.  Therefore search strings needed to be modified to exclude results that were not resumes.

(2) No method to extract all results from a search query.  Therefore search strings needed to be modified to reduce results to a manageable quantity

In both cases, the strategy worked, unfortunately there was a side effect:  Many good results were also thrown out.

Industry score on the Law of Completeness: D

Dropping the bomb on search string exclusions.

So where is the proof, where is the science?

First, I want to thank Cory Dickenson at Broadlook Technologies for leading the team of researchers on search string exclusion metrics.  Looking through tens of thousands of resumes, by hand, and then doing it two more times, is not a fun task.  The reality is that someone had to do it.  Hopefully when this study is reviewed both recruiters and technology vendors will have a better foundation in which to build upon.  I basically hate inefficiency.

Resume Exclusion Metrics (Broadlook project: FRET, Frikken Resume Exclusion Test)

The study was simple.  What was the effect of using exclusions on a resume search string?

The first thing we did for the study was to mine a bunch of social networks and sites that had advice on resume search strings.  We wanted examples, over the past 10 years, that experts were using.  From a few hundred examples, we made a list off all the popular resume search string exclusions that were being used (i.e. -job -job -you -your -submit).

Creating the resume data set

To set up the study, we created search strings for about job 50 positions.  The positions were a wide range: IT , biotechnology, health care, sales, business development, financial, etc.  Next for each search,  we made sure that the search string was specific enough so the results from the search engine was <1000. We did not use any exclusions.  Last step:  Hand verification of every single search engine result.  Each result was classified in one of 4 categories (1) Resume (2) Resume sample page  (3) resume book page (4) Junk: Not a resume.

At this point, we could bring automation into the equation.  Using Broadlook’s Eclipse tool, we automated each of the 50 searches with one of the exclusion terms.  We then repeated the each of the 50 searches with each of the exclusion terms.  Since we already hand-identified which search engine result pages were resumes, we were able to calculate, for each search-exclusion combination, how many REAL resumes were skipped by using each exclusion term. When the searching was done, we had average percentages, across many industries and titles.  We know, with high precision, what percentage of resumes you will lose by using an exclusion term.

Why did I do this study?  Too much time on my hands?..no.  I was interested in making the best open web resume search tool possible.  To accomplish that goal, the tool needs to work within the framework of the Laws of Internet Search.  Specifically the first 3:  Environment, Permutation, Completeness.  The end result was Broadlook Diver 3.0.  The resume search part of the tool *automatically* screens out pages that are not resumes.  In addition, since it is an automation tool, it allows the user to work with complete results from a search engine.   While you can only get Diver from Broadlook, the Resume Exclusion Metrics are free to all.  Enjoy.

The Axioms of Internet Resume Search

1.  Seek <1000 results per search.

You should conduct your search with enough specificity that the search engine reports that there are less than 1000 results.  If you are doing a search that yields many thousands, break up the search into a few separate searches

2.  Never use single-phrase exclusions

Otherwise you will miss a good percentage of resumes.  It is reasonable to use multi-word exclusions, as the level of ambiguity is low.

3.  Use multiple search engines.

There are varying reports of the cross over being as low as 20%.   (Happy to get comments from additional sources on this)

4.  Use automation to screen out non-resumes

Don’t do it by hand and don’t ignore the data below and use exclusions.  This is not 1998 anymore.  Let automation technology screen out Search Engine Result Pages (SERPS) that are not resumes. This includes sample resume pages, job pages, etc.

And now for the Exclusion metrics.

From pool of about 50 job descriptions,  100+ searches,  75,000 search engine results, 28200 resumes, hand verified.  The sort order is based on the worst offending term.  These exclusion terms were pulled from top experts answers on forums about resume search.  Remember the Easter Ham, it is not my intention to reduce the tremendous contribution of those people that freely answer questions (every day) about internet resume search.  It is my intention to give more data so that the entire industry has more facts in which to work with.

Exclusion % REAL Resumes Missed
-job 49.78%
-jobs 40.89%
-summary 37.33%
-intext:resumes 34.37%
-about 34.07%
-writing 32.74%
-your 29.19%
-you 27.41%
-example 25.78%
-required 25.19%
-require 23.70%
-free 23.26%
-list 19.11%
-“how to” 17.04%
-template 16.15%
-library 14.96%
-intitle:jobs 14.37%
-professor 13.48%
-intitle:job 13.19%
-inurl:aspx 12.74%
-send 12.44%
-write 11.56%
-inurl:php 11.41%
-requirement 10.22%
-apply 9.78%
-intitle:apply 9.78%
-sample 9.78%
-intitle:sample 9.48%
-“resume
service”
9.19%
-intitle:career 9.04%
-intitle:example 9.04%
-careers 8.89%
-submit 8.89%
-intitle:examples 8.59%
-intitle:write 8.59%
-intitle:how 8.44%
-intitle:submit 8.44%
-inurl:books 8.44%
-trainings 8.00%
-wizard 7.70%
-samples 7.41%
-inanchor:apply 6.67%
-opening 6.37%
-reply 6.22%
-wanted 6.07%
-applicant 4.89%
-inanchor:sample 4.59%
-inanchor:submit 4.00%
-eoe 3.70%

This resume research project yielded many other interesting facts, such as percentages of doc files vs. pdf, etc.  In the coming weeks, I will be publishing a white paper that breaks down the data in a bunch of categories… after I get back from DisneyWorld!

One reason for CRM failure; The Nature of Contact Information

Most CRM implementations fail.  This is a fact.  Look it up.

In my years in the industry, I’ve worked with many vendors on the consulting side to help reduce the possibility of CRM failure.  While there is a whole host of reason that failure occurs, I have a very unique perspective into one of those reasons.  The Nature of Contact Information.

The nature of contact information is fairly finite (i.e. Company, URL, Name, Title, Email, Phone, Social Network membership, etc). In addition, the concept of contact information is a simple one to grasp. It is so simple, in fact, that if often gets overlooked.

One of the most important concepts in business is “be brilliant at the basics”. If you are brilliant at your basics many more complex processes will fall naturally into place. So how are you treating contact information?

The miss-handling of contact information can lead to dire consequences across your company.

Take the following work flow as an example:
Continue reading One reason for CRM failure; The Nature of Contact Information