By Erol Gün, senior manager of businesses intelligence at Joblift
Most people have looked for a job on the Internet at some point, either by perusing job boards or using a job search engine like Joblift. Most people know that the purpose of a job search engine is to aggregate openings from job boards and partner companies.
What may be less commonly known is that the instant a search is conducted, a huge amount of data is generated, with the potential to aid significant profit and growth. This is where Machine Learning (ML) and predictive analysis comes into play: exploring data generated from job searches can reveal causal relationships, refine job-searching, and help your company get to the next level. Understanding how ML can be leveraged at each stage of the job search is important in tapping its full potential.
To fully capitalize on the potential of ML in job searching it is important to understand the needs of job seekers by identifying their user personas. Ideally, most job boards and search engines classify their users into one of three groups:
Location seekers: Users in this group are generally looking for manual labor or part-time jobs in specific locations.
Title seekers: Those generally searching for specific positions—typically white-collar—regardless of the location, and often willing to relocate.
Expert seekers: Those with a very firm idea of where they want to work, and which type of position they want (generally mid-to-senior white-collar executive).
Once users are divided into one of the persona groups, machine learning (ML) technology can help in two important ways: boosting the relevance of search results and more efficiently managing inventories.
One of the most useful features of ML is its ability to reveal relationships between similar data points. For example, a “location seeker” could benefit from guidance in filtering out all the possibilities to allow them to focus on things like sector to get the most relevant search results. As the user clicks on different job postings, a ML algorithm can assist by evaluating the user’s profile and determining which sectors might be interesting for the user.
Market Basket Analysis, in this instance, helps by clarifying how and why relationships exist between sectors. ML’s contribution in this context yields higher conversion rates (CVR) for companies and useful guidance for users by offering more precise results.
“Title seekers”, however, provide a different valuable data point: search keywords. By monitoring frequently-used search terms, and inventories from partners, ML technologies can help companies to compare their supply and demand levels for job listings.
ML techniques can also be applied in case a company is not attracting enough users to job listings (demand deficit) or the users bounce out due to a lack of inventory (supply deficit). Since both job postings and search keyword phrases are text-based data points, ML can easily extract useful keyword clusters by compartmentalizing words in search keyword phrases and/or items in job postings via POS speech tagging, filtering out irrelevant terms, lemmatizing, digitizing similar keywords, and detecting synonyms.
Once the keyword data has been refined, analysis of search components like sector or job title can be calibrated to scan and compare incoming requests for jobs (demand) with the current inventory of jobs (supply). The resulting data can then be repurposed to create marketing campaigns to satisfy any deficit in demand by attracting new users or recommending potential partners who could supplement the inventory.
ML techniques also pose considerable benefits to performance analysis, which is an integral part of the post-transactional phase. As visitors navigate their way around a website and leave traces of commonly repeated search patterns, ML models can identify and recommend cities to users that are aligned with their user personas.
For example, if a job seeker mostly looks at finance-related job postings, ML models could then recommend employment opportunities in finance hubs like Paris, London, or Frankfurt. When adopting such a strategy for one city proves to be successful, applying it to other similar cities, in theory, will prove to be equally successful and result in profit from the spillover effect.
However, despite the fact that ML poses immense benefits in terms of transactional and performance data, as a colleague of mine at Joblift noted, “Machine learning is like fishing, you can’t deny the possibility of returning empty-handed.”
Indeed, running and facilitating ML models necessitates well-tuned feature engineering, clean data, and an ability to clearly identify problems. But doing all this doesn’t necessarily guarantee statistically meaningful results. ML is an endless cycle of modeling, testing, and fine-tuning.
The data that is procured in the modeling phase is instrumental in informing the testing phase, the output of which is an unparalleled ability to explain cause-effect relationships—a process that holds great potential for moving your company one step forward.