The role of digitization in recruitment
The coronavirus pandemic accelerated the role of digitization in business. Practically overnight, we all had to work from home and find new ways to stay productive. As a result, the use of online applications increased enormously. It can be said that due to the coronavirus pandemic, the potential of digitization is finally being exploited.
We also see an increased use of digitization in the recruitment industry. More than ever, the recruitment sector recognizes the need for automation when it comes to staying in touch with clients and potential candidates. The same goes for the importance of well-written job postings that are easy to find in order to address the right candidates.
Accelerated digitization within the recruitment industry
For recruiters, the coronavirus pandemic means that they are not able to visit clients as much as they used to, and that job interviews with candidates are now mainly done online. This can be challenging. There is much less or no in-person interaction, which can increase the time needed for the recruitment process and might decrease the efficiency. However, digitization also offers recruiters a number of interesting advantages:
Before the coronavirus pandemic, the shortage of talent was one of the biggest challenges recruiters had to deal with. Now that more and more people have lost their jobs, that is no longer the case.
What recruiters are now faced with are sectors where there is suddenly a high demand for staff. How can recruiters process large numbers of online assessments and job interviews as quickly and effectively as possible? The use of new, automated recruitment techniques is exactly what they need.
Job interviews are an important but time-consuming part of the recruitment process. With face-to-face applications no longer possible, recruiters quickly discovered that there are plenty of useful tools for online interviews, including some that even allow recruiters to pick up on applicants’ non-verbal signals, which is an important aspect of interviewing.
Using online application tools saves both recruiters and candidates a lot of valuable time. For example, candidates save time and money on travel, while recruiters notice that their time-to-hire is shortened.
More attention for well-written job postings
Recruiters have known for some time that well-written, easy-to-find online job ads are essential for an effective recruitment process. Nevertheless, it was a challenge for many to fit this into their daily work at the office. Now that recruiters have more time—because, among other things, there is less distraction from colleagues and fewer ad-hoc requests when working from home—recruiters are paying more attention to writing advertisements that appeal to their target candidates. In addition to the fact that recruiters are focusing more on writing good vacancy texts, today’s technology can also be a huge help. Textmetrics offers tools that help you make sure that your recruitment texts are well-written, easy to find and fully targeted at the right audience.
Are you ready to implement AI in your recruitment department? Textmetrics is here to help! Get in touch to find out more.
The future of AI in the recruitment industry
Many companies struggle to find the right candidates. For certain jobs, there just aren’t enough qualified people out there, and it seems to be difficult to find good candidates unless they are actively looking for a new job. Some companies may also have difficulty appealing to the right audience with their vacancies and some vacancies might not be easy to find online. It also proves to be a challenge not to exclude certain candidates, despite the good intentions recruiters have.
In the ongoing battle for talent, artificial intelligence (AI) plays an important role. More and more companies discover that AI can make the recruitment process smarter and more efficient. But what are the benefits of implementing AI in the recruitment industry? And how exactly does it work? We are happy to tell you more and give you some insights on how AI can be used to find the best candidates.
AI in the recruitment industry
There are various ways in which you can use AI in the recruitment industry.
To select the right candidate
You can use AI to predict if a candidate would be a good fit for the job you are offering. For this, you need the data of candidates, and as a company, you must determine which data you select. Based on this data, an algorithm tries to calculate which factors are predictors of success for the job you are offering and which candidates score well on those factors, making them suitable candidates for the job.
To find candidates who are not actively looking for a new job
AI analyses data sources like LinkedIn to find out how long potential candidates have worked at their current job, when they were last promoted, and how well the company they work for is doing. Based on this information, you get a list of candidates who are most likely to be interested in a job at your company, after which you can actively approach them.
For writing better vacancies
AI is also a very good tool for improving the content of your job vacancies, so you get more responses. You use an algorithm to write vacancies that appeal to the right audience, do not contain unnecessary or confusing jargon and do not exclude certain audiences, such as people with low literacy or candidates of a certain gender. In addition, AI helps you write vacancies that are easier to find on Google, so they reach more candidates.
Four advantages of AI in the recruitment industry
There are several benefits of using AI in the recruitment process:
It contributes to the productivity of the recruitment department
Writing content for vacancies, recruiting candidates, selecting candidates and job interviews; these tasks all ask a lot of your time. And there is so much more to do in a recruitment department. AI can take a lot of work off your hands. It takes less time to write vacancies, as does recruiting and screening candidates. You will receive more valuable responses to the vacancies you post and you will know more quickly whether a candidate has what it takes or not. This increases the productivity of the department and leaves more time for other important activities.
It increases diversity within the organisation
Many companies want a diverse workforce but find this difficult to achieve. AI can help increase the diversity and inclusiveness of your organisation. It all starts with the vacancies. Without realising it, many vacancies exclude certain groups of people. Think of people with low literacy and people of a certain gender. Algorithms can analyse whether vacancies appeal to all audiences. Even during the selection of candidates, it sometimes proves difficult for recruiters to assess candidates objectively and without prejudice. That human prejudice, which recruiters certainly do not purposely have, can be drastically reduced with the help of AI. When recruiters assess candidates based on data, emotions no longer have an influence.
A better match between the candidate and the organisation
AI can identify factors for success for a certain position and use that to screen candidates. By analysing data on candidates, AI can determine which candidates score well on those factors and will therefore be a good fit for the job. You determine the success factors based on current, successful employees. Candidates who have the same characteristics are likely to perform just as well in the same position. The better candidates score on these factors, the better the match between the candidate and your organisation.
More value from available data
By using AI, there are more options for screening candidates. You can analyse more data on the candidate than the information provided by the candidate. Nowadays, you can find a lot about your candidates online. Just think of social media, such as LinkedIn profiles. There is a wealth of extra data on each candidate that you can analyse. This gives you even more information to determine whether that person is a good fit or not.
AI is the future
At Textmetrics, we believe that more and more companies understand that AI can make their recruitment process smarter and more efficient. And that a future without AI in the recruitment industry is actually unthinkable. That’s why we offer AI-driven tools that help you write vacancies that appeal to the right audience and don’t exclude groups of people.
Are you ready to implement AI in your recruitment department? Textmetrics is here to help! Get in touch to find out more.
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Everything you need to know
Computers don’t speak languages the way humans do. They communicate in machine code or machine language, while we speak English, Dutch, French or some other human language. Most of us don’t understand the millions of zeros and ones computers communicate in. And in turn, computers don’t understand human language unless they are programmed to do so. That’s where natural language processing (NLP) comes in.
What is natural language processing?
Natural language processing is a form of artificial intelligence (AI) that gives computers the ability to read, understand and interpret human language. It helps computers measure sentiment and determine which parts of human language are important. For computers, this is an extremely difficult thing to do because of the large amount of unstructured data, the lack of formal rules and the absence of real-world context or intent.
In recent years, AI has evolved rapidly, and with that, NLP got more sophisticated, too. Many of us already use NLP daily without realizing it. You’ve probably used at least one of the following tools:
- Spell checker.
- Spam filters.
- Voice text messaging.
Five basic NLP tasks
As we mentioned before, human language is extremely complex and diverse. That’s why natural language processing includes many techniques to interpret it, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. There are five basic NLP tasks that you might recognize from school.
Part of speech tagging
One of the tasks of NLP is speech tagging. For every sentence, the part of speech for each word is determined. Part of speech is a category of words that have similar grammatical properties. For example, the word book is a noun in the sentence the book on the table, but it’s a verb in the sentence to book a flight. And a word like set can even be a noun, verb or an adjective.
There is a large number of words that can serve as multiple parts of speech, which makes it challenging for a machine to assign them the correct tags.
Lemmatization concerns removing inflectional endings only and reducing a word to its base form, which is also known as a “lemma”. Past tenses are changed into present and synonyms are unified. For example, the past tense ran is changed to run and the synonym best is unified into good.
Lemmatization uses a different approach than stemming to reach the root form of a word. For example, the lemma of caring is care, not car as it is with stemming.
The tokenization task cuts a text into smaller pieces called tokens. This process segments a chunk of continuous text into separate sentences and words, while at the same time removing certain characters, like punctuation. For example, this sentence split up into smaller tokens would look like this:
For example this sentence split up into smaller tokens would look like this
That pretty much looks the same, right? That’s because languages like English often separate words with a blank space, but not all languages do. In those languages, tokenization is a significant undertaking that requires deep knowledge of the vocabulary.
In English, too, blank spaces may break up words that actually should be considered one token. Think of city names like Los Angeles or San Francisco or the phrase “New York-based”.
Disambiguation is a task that has to do with the meaning of the words we use in human language. Some words have more than one meaning, and while reading, we select the meaning that makes the most sense in the given context. For example, the word bat can refer to the animal that flies around at night or the wooden or metal club that is used in baseball. And a bank can be a place where you go to open a current account or a piece of land alongside a body of water where you go fishing.
Humans communicate based on meaning and context. Semantics help computers identify the structure of sentences and the most relevant elements of a text in order to understand the topic that is being discussed. For example, if a text contains words like election, democrat and republican or budget, taxes and inflation, the computer understands that the topics discussed are American politics and economics.
Examples of natural language processing in practice
In recent years, because of the availability of big data, powerful computing and enhanced algorithms, natural language processing has been rapidly advancing and transforming businesses. It’s now widely used across an array of industries. We have listed some interesting examples below:
- NLP is widely used in the translation industry. Many localization companies use machine translation to help their translators work more efficiently. When the text is already largely translated by machine, it saves them valuable time and the number of words they can translate daily increases.
- Search engines use natural language processing to come up with relevant search results based on similar search behavior or user intent. By using NLP, the average person finds what they’re looking for.
- NLP is also used for email filters. The spam filter has been around for quite some time now, but Gmail’s email classification is one of the newer NLP applications. Based on the content of the emails that come in, Gmail now also recognizes to which of the three categories (primary, social or promotions) the emails belong. This helps users determine which emails are important and need a quick response, and which emails they probably want to delete.
- We also see the use of natural language processing in healthcare. It can be used for streamlining patient information or for apps that convert sign language into text. The latter enables deaf people to communicate with people who don’t know how to use sign language.
- NLP is even being used in the aircraft maintenance industry. It helps mechanics find useful information from aircraft manuals that have hundreds of pages, and it helps find meaning in the descriptions of problems reported by pilots or others working in the industry.
Ways we use NLP at Textmetrics
What the examples above show is that there are numerous ways that NLP can improve how your company operates. That’s because human interaction is the driving force of most businesses. When you’re not too familiar with AI and NLP, though, it can be quite challenging to do it right. And having employees manually analyze all of the content that your company produces is almost impossible.
At Textmetrics, we offer a number of tools that use natural language processing to help organizations analyze their content and provide suggestions for improvements.
- A spell checker enables everyone in your organization to create grammatically correct and error-free content.
- A tool to determine the language level of the content you’ve created. This is based on the European Language Framework.
- A tool to flag words that are gender-biased, providing suggestions and possible replacements based on the target audience you’re creating the content for.
- An algorithm-based program based on the needs of your organization to help you standardize your communication according to your corporate identity.
Are you curious to know more about these tools, or do you want to find out if they could be of use in your organization? Please let us know. Textmetrics is here to help!