Reliable Email Address Verification

booleanstrings Uncategorized 5 Comments

How do you find someone’s email address? Using the symbol @ in searches on Google is useless since it’s ignored along with most of the special characters. We know ways to figure out a company email pattern via clever Google searches using the asterisk * (ask Gary Cozin ).

Unfortunately, our methods may not work in some cases. Besides, some companies, even large ones, don’t have a consistent pattern.

Here’s how to see which one of your guesses about someone’s email address is correct – in case the person is a LinkedIn member.

1. Enter ALL of your guesses into a text or a CSV file.

2. Upload it to LinkedIn as “Imported contacts”. (Don’t invite anybody! This may not be a good idea.)

3. Check out your “Imported contacts”.  See which email was the right one?

(By the way I am happy to connect on LinkedIn; see my profile.)


Top 4 Reasons to Source Candidates on the Internet

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žWhy we must use the Internet to source candidates:

  • To use its vast resources and get ahead of the competition
  • To find candidates that we would not find otherwise
  • To communicate with potential candidates in a network context
  • To cross-reference  prospects so that we are on the phone with the right people

How Many Resumes Are There on the Internet?

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TRU Source ATL

Here is a cool blog post contest. Post a blog (anywhere) with an estimate of the number of resumes on the Internet and win a ticket to #TRUsource in Atlanta.

Please email Geoff Webb at [email protected] with a reference to your post; please also share on the Boolean Strings network.

Also, to help get this off the ground, I am offering my Google-Based sourcing DVD to a randomly selected person out of the first five people who post.

Please feel free to post your answers here as comments on the Boolean Strings Network.

I looked around this morning and here are some interesting numbers I was able to pick:

* With more than 35 million resumes dispersed over 40000 Web-based locations, recruiters and hiring managers are spending more than 65% of their time

* Some experts say that there are now over 16 million resumes floating around the Internet. Monster.com, the largest online job board, has more than 20 …

* With Over 52 Million Resumes Floating Over The Internet Everyday,. How Will You Find Just One?…

* There are an estimated 100+ million resumes posted on the Internet. TalentHook allows you…

* There are over three million resumes on the larger internet sites. Thus, it is important to breakthrough the “clutter”…

* The Toolkit also includes our Search the Web feature, which provides access to over 7 million resumes posted on the internet.

* With an estimated 100 million resumes posted on the internet, rest assure that your next great hire can be found online. So stop paying huge recruiting 

* There are nearly 15 million resumes on the Internet today. Most are located on fee-based job boards, many of which cost thousands of dollars to purchase. …

* Is my resume really getting seen in a pile of 40 million other resumes on the internet? I have excellent skills and qualifications, but will anyone find me 

Have fun with the contest!

Irina

Deep Web Search Using Google

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For many of us, Deep Web means pages that Google – or any other search engine – can’t find. This is almost true, but not exactly true.

Deep web consists of  dynamic links, password-protected sites, pages with no links leading to them, and a few other kinds of “hidden” pages. It turns out (and you may have experienced that) that we see some dynamically-generated paged in out searches on Google. Here is an example that I got while searching for resume, Java, and Swing.

How does this happen? The underlying mechanism is that Google finds “promising” sites with forms and tries to generate pages by “throwing” some keywords to fill those forms. If the resulting pages seem to be “interesting” to Google’s algorithm, Google includes them among the static results.

If you know to to build Google’s custom search engines, you can control Google and only show the deep web results. This is because, unlike standard Google search, custom search accepts special characters such as the question mark.

Both special characters in custom search engine templates and deep web results among other results are cool Google features. I plan to post about Custom Search Engines in more detail soon.

Searching LinkedIn From Google: Hit and Miss

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Many of us X-ray LinkedIn from Google. If we are looking for profiles with certain keywords, we use the string

site:linkedin.com inurl:in OR inurl:pub -inurl:dir -inurl:jobs <your keywords here> …

Strangely though, Google forgets to add many words that belong to all LinkedIn profiles as keywords. If you search for “Public profile powered by”, industry, connections, current, past, etc., Google will miss many relevant results. It might be interesting, say, to look for people with few connections using the num-range expression  “1..10 connections”; alas, the majority of the results will be missed.

Yahoo does a better job at this; keep in mind though that the Yahoo search engine (initially created by folks at Inktomi) is going away by the end of 2010.

LinkedIn Profiles: an In-Depth Look – Webinar on June 8th

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If a LinkedIn member wishes not to have a public profile, LinkedIn promptly removes the info from the Web. However, did you know that what you see marked as private profiles in search results has nothing to do with personal privacy settings? The majority of LinkedIn profiles are public and can be viewed – without upgrading your account. Come to my webinar to get an in-depth look at searching and viewing LinkedIn profiles and contacting prospects on LinkedIn.

Space is limited.

Link to register: https://www2.gotomeeting.com/register/639581618

Content:

* Exploring Private Profiles
* Getting Info on Out-of-Network Profiles
* How to Find More than 100 Members in a Search
* Which Profile Info Can and Cannot Be Found
– LinkedIn Search
– Web Search
* Advanced People Search Dialog Tips
* Selecting Target Lists of Connections
* Email Addresses for LinkedIn Members
– Finding
– Verifying
* Cross-referencing Using LinkedIn
* Discovering LinkedIn Profiles on Blogs and Twitter
* Custom Search Engines for LinkedIn
* Resources

As the results of the webinar you will get to the next level in your ability to quickly and easily find the right people on LinkedIn, and make the right choices about upgrading your account.The webinar will be of practical use for recruiters, sourcers, sales, marketing people, and anybody else who would like to find business prospects on LinkedIn. Lecture, demonstration, and Q&A will be included in the presentation. You need to have at least some basic search skills for LinkedIn and for Google to benefit.

Date/time: Tuesday, June 8th, 9 am PDT/noon EDT
Length: 90 min
Price: $79
As always, my webinars come with one month of Q&A over email.

Exploring Private Profiles on LinkedIn

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When you use people search on LinkedIn, sometimes you see the “Out of your network” note (as seen on the left here) and at other times you see that the profile is Private. In both cases LinkedIn suggests that you should upgrade your account to see more. Though Glen Cathey posted an excellent exploration of  “private” profiles a while ago, many of us keep thinking that those profiles we find by searching are indeed private, i.e. their owners have changed their settings not to have a public URL (meaning a URL visible to search engines) for their LinkedIn profiles.

The truth is, that private profiles have NO correlation whatsoever with profiles marked “Private” in search results.

I have been exploring out-of-network and “private” profiles lately – and you should, too, before you decide to upgrade your LinkedIn account. I will present a webinar on June 8th, 2010 to show a variety of search techniques for a basic LinkedIn account holder.

Here is an example of a “private” LinkedIn profile found by a friend using LinkedIn people search. Any idea who that might be?

LinkedIn Connections: Statistics

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LinkedIn

Searching on LinkedIn without any keywords will show some statistics about your current connections. I have looked at mine and here are some numbers that I find interesting – and relevant to the profile of my company, Brain Gain Recruiting. I wonder if others with large networks would have similar distribution – if you take a look, let me know.

Location:

  • United States (788069)
  • India (171175)
  • Greater New York City Area (104945)
  • United Kingdom (99636)
  • Canada (60881)
  • San Francisco Bay Area (58014)
  • Greater Chicago Area (43605)
  • Netherlands (40825)
  • Greater Boston Area (40418)
  • Greater Los Angeles Area (37655)

Relationship:

  • 1st Connections (14102)
  • 2nd Connections (3326977)
  • Group Members (1536674)

Industry

  • Information Technology and Services (210563)
  • Marketing and Advertising (124202)
  • Human Resources (97284)
  • Computer Software (80838)
  • Staffing and Recruiting (66489)
  • Management Consulting (59882)
  • Financial Services (56159)
  • Telecommunications (50542)
  • Pharmaceuticals (47373)
  • Internet (40559)

Current Companies

  • IBM (5391)
  • Accenture (4418)
  • Hewlett-Packard (3650)
  • Microsoft (3427)
  • Oracle (3207)
  • Deloitte (2859)
  • SAP (2768)
  • Cisco Systems (2450)
  • Ernst & Young (2283)
  • PricewaterhouseCoopers (2235)
  • Capgemini (2166)

If I change the location to the US only, the list is this:

  • IBM (2197)
  • Microsoft (1873)
  • Cisco Systems (1589)
  • Bank of America (1563)
  • Pfizer (1482)
  • Hewlett-Packard (1434)
  • Accenture (1394)
  • Oracle (1361)
  • JPMorgan Chase (1070)
  • AT&T (1062)
  • Merck (1046)

Past Company

  • IBM (16959)
  • Accenture (12190)
  • Hewlett-Packard (10025)
  • PricewaterhouseCoopers (9009)
  • Ernst & Young (7992)
  • Microsoft (7860)
  • Deloitte (7731)
  • Oracle (7356)
  • AT&T (6199)
  • KPMG (5496)
  • GE (5407)

(These lists very much reflect the type of positions Brain Gain Recruiting hires for: consulting, IT, and finances, with our clients being high up on the list.)

School

  • University of Mumbai (12426)
  • University of Phoenix (11876)
  • Delhi University (9121)
  • University of Pune (7480)
  • New York University (6954)
  • Penn State University (6875)
  • Bangalore University (6052)
  • University of California, Berkeley (6045)
  • University of California, Los Angeles (5970)
  • Michigan State University (5791)
  • Osmania University (5704)

School, for the US only:

  • University of Phoenix (11532)
  • Penn State University (6675)
  • New York University (6394)
  • University of California, Los Angeles (5691)
  • Michigan State University (5626)
  • University of California, Berkeley (5545)
  • Rutgers, The State University of New Jersey-New Brunswick (5509)
  • Boston University (5291)
  • The University of Texas at Austin (5277)
  • University of Michigan (5179)
  • University of Maryland College Park (4960)

Profile Language

  • English (1518738)
  • Spanish (20647)
  • French (13857)
  • Others (7169)
  • German (5772)
  • Portuguese (1945)
  • Italian (1564)

Semantic Search for Recruiters, Sample Approaches

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Semantic search for recruiters, ideally, would let us enter a job description and get lists of matching candidates.

Here are some sample approaches that either have been implemented in new semantic search tools for recruiters or can be accomplished through search engines like Google (learn more by getting my Google DVD).

  • Word proximity. If the work managed in near the word people in a resume, we are most likely looking at a manager’s resume.
  • Abbreviations. It certainly helps to recognize that Sr. is Senior, and PwC is Pricewaterhousecoopers.
  • Synonyms. If you are looking for a Software Engineer, a Software Developer is a match as well.
  • Weighted words. You may want to say that some skills are “must-have”, and some are “nice to have”, or, in a more complex system, give higher “weights” to words that are more important.
  • Keyword clouds. Some keywords may not be in a job description but are common, say, for the industry. Using those terms in a search helps.
  • Ranking. Search engines rank pages higher if the pages are more popular; we need to see pages that are most relevant, and this is not the same.

Would you like to add more? 🙂

Navigating Semantic Search is Featured on SlideShare

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Friends at Monster.com have just forwarded this notification from Slideshare to me:

“Your presentation Navigating Semantic Search is currently being featured on the SlideShare homepage by our editorial team.

We thank you for this terrific presentation, that has been chosen from amongst the thousands that are uploaded to SlideShare everyday.”

Thanks again to Monster.com for giving me the opportunity to share my knowledge with their large audience.

Here is a related blog post at www.monsterthinking.com