Quality over Quantity: how Machine Learning is solving recruiters’ biggest problem

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83% of HR professionals say they have had difficulty recruiting suitable candidates in the past 12 months, according to new study from the Society for Human Resources Management. HR professionals struggle getting enough applications and when they are getting them, the quality is not there. How to deal with this issue of quantity and get more quality applications?

New and evolving machine learning techniques offer a great solution for this problem. Here is how:

A/B tests 
By testing two groups of users against each other, machine learning can organize search results based on expected user behavior. This filtering of search results will lead to more qualified candidates finding results they are suited to.

CV parsing
Machine learning uses historical data in a job seeker’s CV or resume to suggest where a candidate’s skill set will best fit suitable positions. This way, candidates who may not have entered a specific keyword will be shown results with similar job titles that suit their skills but may have been overlooked.

Predict next steps 
Machine learning can monitor user behavior to make predictions about their next steps career-wise. For example, users that find job postings through Facebook can be seen to have different behavior patterns than job seekers who use Google due to different targeting campaigns.

Location-relevant results
Robotics can make certain assumptions based on job seekers’ desired location – e.g. users looking for positions in New York’s trendy Williamsburg would be shown more creative positions, given the strength of this sector in the location.

Conversion tags
Similar jobs can be marked or tagged based on keywords – e.g. skills needed, location, and industry. This ensures that once a user makes a search, the results shown do not contain only the items that include the provided keyword, but also jobs which can be grouped under the same function (i.e. business, IT, etc.), or under the same industry (i.e. engineering, logistics, etc.) and so-on.

Joblift tests groups of users against each other to analyze patterns in how job seekers search for vacancies online and adapts search results accordingly. This means that only qualified candidates will find the roles that will suit them best and in turn ensures only the best CV’s make their way to the hiring company.

The possibilities that machine learning are revolutionizing the recruiting industry and is showing direct impact on recruiters’ success.

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Lukas Erlebach CEO, Joblift

Lukas Erlebach is Co-Founder& CEO of Berlin-based Joblift. Previously he held an executive position at Zalando SE and was P&L responsible for Zalando's biggest market Germany for 3 years. He has prior 5 years of strategic top-management consulting experience at The Boston Consulting Group with a focus on industrial and Private-Equity clients acting pan-European with stints in the US and Asia. Lukas earned his post-graduate degree at London Business School and his Master's from ESADE Business School and the Vienna University of Economics and Business Administration.


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