Lens/Match™

The industry’s most effective person-to-job and job-to-person matching application uses its understanding of candidates’ skills, experiences, and career trajectories to deliver statistically predicted matches and robust search tools.

What We Deliver
Additional Details
Key Benefits
What Makes Us
Different

Lens/Match™ delivers intuitively relevant results by undertaking a detailed analysis of each job seeker and of each job. Unlike other search solutions, Lens/Match goes well beyond keyword matching or O*NET code correspondence. Instead, Lens/Match predicts which job seekers are best suited for a position by looking for those whose skills base, work experiences, career path, certifications, and educational credentials most closely resemble those who have placed successfully into similar jobs across millions of prior observations. It is a powerful idea: the most relevant job or job seeker isn’t necessarily the one that lists the right keyword a dozen times, rather it’s the job that people following similar career paths have placed into or the person with similar career trajectory and qualifications to those that have placed before.

Lens/Match™ predicts which job seekers are best suited for a position by looking for those whose skills base, work experiences, career path, certifications, and educational credentials most closely resemble those who have placed successfully into similar jobs across millions of prior observations.

Lens/Match offers four ways to find the most relevant jobs for a candidate and the most viable resumes for a job:

Smartlist™ – Lens/Match uses neural-network predictive technologies to rank the candidates most compatible with a job description – based on a full evaluation of skills, experiences, and career path.
Contextual Searching – Going far beyond keyword searching, Lens/Match enhances the relevance of search parameters by examining them in the context of a resume. For example, users can limit a search to candidates who have used a specific skill in their last two jobs, or who have worked for a particular employer during the last three years, or who earned a certain degree or higher, or who live within 20 miles of a particular street address or postal code.
Resume Cloning™ – Sometimes, a recruiter or employer will find their ideal candidate. When that happens, Lens/Match will “clone” the candidate to find others with similar attributes in terms of skills, knowledge and experience. The only requirement for this function is an exemplar resume in any standard format.
Skills Identification – Lens/Match allows users to search for skills, even those a candidate may not have included in her resume. This is possible because Lens/Match harnesses the power of its companion parsing engine, Lens/Xray™, to derive and infer a candidate’s unstated skills based on the qualifications that are described in her resume and the application’s prior observation of millions of similar candidates.

Experiences in Context – Lens/Match evaluates experiences based on their context. Japanese spoken in a job is far more valuable than Japanese learned in a course. Lens/Match notices the difference and weighs experiences based on context.
Skills Standardization – Lens/Match translates skills to a standard, multi-tiered hierarchical catalog.
Core Competency Analysis – If a candidate is a VP of Sales and Marketing, is her job in sales or in marketing? Lens/Match uses experience and skills clustering to assess and categorize each candidate’s experiences.
Title and Skills Normalization – Two candidates might have the same job title but be at completely different stages in their career. Lens/Match normalizes titles and skills based on millions of observed career progressions. Career Path Assessment – Compares the candidate’s career path with those who have held similar positions in our 1,000,000+ job transition Knowledge Mine™.

Relevant Results – Our relevance scoring technology focuses attention on the most suitable candidates for a job by rank-ordering applications and by putting next-generation search tools at your fingertips.
Leverage Your Databank – Employers and recruiters invest considerably in resume acquisition yet common tools don’t provide significant ability to go back and search through candidate or job databases. That means that once a candidate is out of contention for the job to which he applied, his resume effectively disappears into an impenetrable stack. Lens/Match features robust search tools that optimize your candidate or job databanks by giving you the ability to return repeatedly to find the best matches for a job or candidate, including passive candidates and non-obvious work opportunities.
Widen the Field – Our advanced search technology makes each received resume that much more valuable by combing through both current and archived applications for different positions, significantly increasing the size of the candidate pool for any given job.
Multiple Search Options – Lens/Match gives you control over how you search for candidates. Search based on a job posting or customize your search within targeted geographies, industries, educational levels or work experience ranges. Or look for search terms within specific parts of a resume. It's up to you.
Provides Insight into Source Productivity – You pay a lot to maintain each of your application sources. Are they delivering? Because Lens/Match measures candidate quality quantitatively, it can be an important tool in analyzing the cost efficiency of each recruiting channel.
Ease of Deployment – It’s quick and easy to embed Lens/Match in any of your applications or it can be provided by Burning Glass on a hosted basis. Our API makes it easy to get the application up and running. Lens/Match is compatible with a wide variety of operating systems, including NT, Windows 2000, Unix, Linux, and HP-UX. Its architecture is extremely scalable; whether you want it to run on desktop computers, large servers, or multi-processor systems, Lens/Match will work for you.

Burning Glass	Others
Lens/Match matches people and jobs based upon real world patterns of placement. Rather than relying exclusively on keywords or profile data, Lens/Match suggests matches that fundamentally make sense based upon statistical correlations between job requirements and job seeker attributes learned from millions of observations. As a result, Lens/Match understands what kinds of people have placed into similar jobs in the past and can search for people with similar career paths, work experience, skills, etc. and can predict what kinds of jobs a candidate is likely to place into next based upon the actual placements of similar job seekers it has seen.	No other match technology makes use of actual career patterns because no other technology considers people and jobs as more than a series of words or attributes. Semantic search engines examine the correspondence between words while profile technologies examine the correspondence between form-entered data. By contrast, Lens/Match recognizes that matches need to make intuitive sense based upon the specific experiences, skills, qualifications, and career trajectory of each individual.
Lens/Match recognizes that a person is more than just a list of skills. The context in which those skills or experiences were accrued makes all the difference. For example, Lens/Match has learned from real world observation that on-the-job experiences are more valuable to employers and that recent experiences carry more weight in determining placement success. In addition, Lens/Match goes well beyond keywords to understand the most important concepts within someone’s experience base. For example, an administrative assistant and a senior engineer may both list Microsoft Word among their skills but, for the admin, it represents a core skill whereas, for the engineer, it is mostly incidental to a set of technical skills; Lens/Match knows that showing the engineer a job as a secretary using Microsoft Word is not likely to represent a good fit.	Others solutions can find an “admin” even when the posting is looking for a “secretary,” but because they are generic document matching technologies they can’t assess how many years experience a person has or fit with the candidate’s objective or whether their career path to date is similar to that of others who have been successful for this job. When they score matches for a CEO position they do not differentiate between someone who is a CEO and someone else who is the Executive Assistant to the CEO. They also can’t differentiate recent skills from decayed skills or provide context on which skills were acquired on the job vs. in a course. And such engines don’t have the ability to evaluate whether this job is a logical progression for a specific candidate (based on their career history).

Burning Glass

Others

Lens/Match matches people and jobs based upon real world patterns of placement. Rather than relying exclusively on keywords or profile data, Lens/Match suggests matches that fundamentally make sense based upon statistical correlations between job requirements and job seeker attributes learned from millions of observations. As a result, Lens/Match understands what kinds of people have placed into similar jobs in the past and can search for people with similar career paths, work experience, skills, etc. and can predict what kinds of jobs a candidate is likely to place into next based upon the actual placements of similar job seekers it has seen.

No other match technology makes use of actual career patterns because no other technology considers people and jobs as more than a series of words or attributes. Semantic search engines examine the correspondence between words while profile technologies examine the correspondence between form-entered data. By contrast, Lens/Match recognizes that matches need to make intuitive sense based upon the specific experiences, skills, qualifications, and career trajectory of each individual.

Lens/Match recognizes that a person is more than just a list of skills. The context in which those skills or experiences were accrued makes all the difference. For example, Lens/Match has learned from real world observation that on-the-job experiences are more valuable to employers and that recent experiences carry more weight in determining placement success. In addition, Lens/Match goes well beyond keywords to understand the most important concepts within someone’s experience base. For example, an administrative assistant and a senior engineer may both list Microsoft Word among their skills but, for the admin, it represents a core skill whereas, for the engineer, it is mostly incidental to a set of technical skills; Lens/Match knows that showing the engineer a job as a secretary using Microsoft Word is not likely to represent a good fit.

Others solutions can find an “admin” even when the posting is looking for a “secretary,” but because they are generic document matching technologies they can’t assess how many years experience a person has or fit with the candidate’s objective or whether their career path to date is similar to that of others who have been successful for this job. When they score matches for a CEO position they do not differentiate between someone who is a CEO and someone else who is the Executive Assistant to the CEO. They also can’t differentiate recent skills from decayed skills or provide context on which skills were acquired on the job vs. in a course. And such engines don’t have the ability to evaluate whether this job is a logical progression for a specific candidate (based on their career history).