Now that we are telling people about Cangrade, we are getting a lot of feedback and opinions. One of the best new terms that I encountered lately and now one of my favorite is “Resume SEO”. SEO (Search Engine Optimization) is a concept developed to promote websites on major search engines (like Google), to optimize the website by including certain keywords and other criteria that Google “likes” so that the site shows up higher on search results. Find out more at https://thedigitalswarm.com/website-theme-and-user-experience/. Moreover, it is also possible to showcase your Google My Business results using a platform developed by Local Viking company (LocalViking.com).
When recruiters and employers get a large number of resumes submitted for every job opening, they are commonly using automated tools to screen for keywords and other criteria, much as a search engine would evaluate a website. When you type in Candidate Grading, for example, there are about 9 million websites that contain these keywords. The reason that you see Cangrade.com (our awesome site) first, is that Google’s advanced algorithms have decided it’s the most relevant result for your search. Employers and recruiters aren’t quite as smart as Google. (Google is really smart.) The technology they are using is much more basic. So for a candidate’s resume to show-up in a search of all the available resumes that an employer or recruiter has on hand all a candidate has to do is customize it with keywords that the employer of recruiter thinks are the most relevant to the job. In other words candidates often optimize their resumes by including key words that they will think the recruiter will “like,” thus pushing the resume to the top of the search results.
To understand what’s going on, and why it’s inaccurate, it’s important to start from the beginning, and to define our terms.
The term “search engine” is often used generically to describe both crawler-based search engines and human-powered directories (which include job boards). These types of search engines gather and present information in radically different ways.
Crawler-Based Search Engines
Crawler-based search engines, such as Google (for the first seven years of operation) create their listings automatically. They “crawl” or “spider” the web, people, in turn, manually sort through the search engine results. If a website changes its web pages (adding and/or removing information), crawler-based search engines are designed to find and record these changes.
A human-powered directory, such as Open Directory, depends on people for its listings. A website owner must submit a short description to the directory or editors, or contributors must write a summary of the site or other applicable content. A search looks for matches only in the descriptions submitted. If a website changes its web pages (adding and/or removing information) it will have no effect on the search engine listing. This type of search engine can provide more targeted results then the Crawler based engine, since the website/information summaries are grouped into categories, and have specific fields that contributors input. Common examples of this type of search engine include Wikipedia.com and job searching websites such as Monster.com.
“Hybrid” Search Engines
In the Internet’s early days, a search engine commonly presented either crawler-based results or human-powered listings. Today, it is extremely common for both types of results to be presented. Usually, a hybrid search engine will favor one type of listing over another. For example, Bing is more likely to present human-powered listings from Wikipedia.org and Yellowpages.com, before Crawler-Based results. Like Bing, Google also tends to prioritize results from human-powered directory information sources such as Wikipedia.org and Yelp.com.
So that’s interesting, but so what?
… Well I’m glad you asked
If I were a job Candidate, my first questions would be:
At what stage of evolution are the Recruiters and Employers in terms of the “search engines” that they use to match the most relevant resume to a job? And how does “resume SEO” actually work?
So, essence, what is the algorithm for passing the first round of screening which is very often done by a computer? What are “search algorithms” and how do I manipulate them?
If I were a Recruiter/Employer, my first questions would be:
How can I improve my resume search algorithm, so that the candidates who try to manipulate it are not successful? How can I find out which candidates are trying to manipulate my search algorithm? And, most importantly – how do I narrow down the number of resumes I get in a cost –effective and accurate way?
All good questions.
And they all have answers that lie in techniques that Internet search engines and the SEO companies which are designed to manipulate them have been using for decades.
However: the answer to these questions is I think deserving of a PhD dissertation… Or at least worthy of a series of blog posts