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Employee Retention and Salary Prediction

Today, the job market is very expansive; to get even your foot at the door - you will be face a myriad of competition. Hence, it is very important for any young stalwart to get acclimatized with the job market and especially develop their networking skills. Fortunately, the Internet and the explosion of Social Media has brought the world much closer.

So one can utilize their language and communication skills to reach out employers on various platforms such as Instagram, Facebook, Twitter, linkedIn, Glassdoor etc. But most high-value employees have a strict command of their language and a professional acumen when it comes to use it for either posting or messaging. Our goal is to create a model that will extricate the high-value employees & employers by analyzing their textual and lingual syntax even if they are using it in an informal setting.



Hiring Agency and Job Search Portal



One of our client was a job portal and another one was a hiring agencies based in India, both of them wanted a system to identify valued users for increased reputation and better employment rates. The project is extended towards an overarching system of analysis - for social media and it’s market relations as whole. This in-depth model will create a resilient model that will expedite the process of hiring, and also give the clients to analyze click-to-action rates as well. The conversion rate, will be also boosted because of the additional stats that will be given by our clients to their corporate clients. This creates a robust ecosystem attuned to a higher professionalism with better corporate and employee responsibility.



A complex and revolutionary model like this comes with it’s fair set of challenges that comes with it. The important and the most pertinent challenges are listed below:

  • Involvement of the Employees - Since, the data that’s being gathered is rather confidential in nature, many wouldn’t attempt to take part in it. Hence, we should provide support for their anonymity.

  • Concerns of Partiality - A peculiar model like this; especially focusing on human etiquette, will need affirmation that it is impartial and objective. This has to be delivered.

  • Data Security - The data provided by the employees and the employer has to be safeguarded and must be private.

  • Emotional & Phrasal Analysis - Due to the informal and ironic nature of most social media communication, the analysis must be tuned to fit these trend-based language orientations as well.



Solution summary:

  • A sample set is created after gathering data (email and chat text) from consenting professionals employed by a Multinational Companies who provide selective data for the enrichment of the model.

  • After this sample set is gathered all the stats pertaining to it is compiled and processed.

  • A comparative analysis between market trends and professional conduct is measured and analyzed to arrive at a conclusion.

With all these concerns in mind, we decided to draft strategies to tackle the aforementioned challenges and
even forthcoming ones :

  • The sample set is compiled in a limit-based, anonymous manner that only states the arbitrary details and never the personal ones.

  • This data set is then used to generate stats; which are conditions that could be compared easily with the data gathered from employers sources and groundwork data.

  • Utilizing the results of these comparisons, we then can create various models which can predict everything from like/dislike counts to personal intents of statuses and tweets.



This model, with its advanced analytical methods can be bring profound changes to the job market. By analyzing these peripheral behavioral tendencies of people who use social media, we can paint a clear picture of who is a worthy candidate for a job and also identify high-profile users for future networking.

Easy lingual identifiers are also discerned and can be integrated to any system for analysis. The rate of correlation between the data and the perused income group is very strong even with minimal samples.

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