Create account if you don’t have any account:-

You will get this view of dashboard – click on the campaign group

• Campaign groups allow you to group related campaigns together to manage them at scale.
• You can create an unlimited number of campaign groups to organize your campaigns.
• Each campaign group can hold a maximum of 2,000 campaigns. Once a campaign has been added to a campaign group, it can’t be moved.

Click on create campaign TAB

After getting New Campaign group – you can edit the name of the campaign and click on next

Fill all the details step by step in the dashboard

Objective:- Set your specific objective from the available options for your campaign based on your goals. This objective selection gives you an option “the action you want your customers to take when they see your ads”

Audience:- with this option you can select locations (Country/CITY) of your targeted customers. Here LINKEDIN have some great features like you can narrow down your targeted audience by targeting the specific company, demography, education, job experience and interests.

Ad format:- This option allows you to choose “How you want to show your ads to the audience”. For example – Single image AD, Multiple (Carousel) images AD, Video Ads, Message Ads, or Conversation Ads

Placement:- This option allows you to choose the option to place your ADs in the LInkedIN newtwork only or you may allow LINKEDIN to run your ads on LinkedIn and their partner apps and websites.

Budget & Schedule:- This is the most important section where you need to invest sometime to understand all the options and set the budget accrodingly.

Here you will get three options of BUDGET

1. Daily budget

2. Lifetime budget

3. Both daily and lifetime budget

After putting your budget you will get an option to schedule your ads

1. Run campaign continuously

2. You can set a start and end date

Conversion tracking (optional):- This option is optionals which provides you additional reporting and analysis whihc will hel you to better understand the actions people are taking on your website after viewing or clicking on your ad.

After completing the above steps you will get below image dashboard. If you don’t want to create any other ad campaign in that particular campaign group then click on next – you will get the Review option of your Ads, if you want to change anything go through those options if not then simply Launch the campaign.

After clicking on the next step – you will get the option to select the images or videos based on your AD format selection.

After that choose your payment option make the payment and simply launch the campaign. You will get this image “You’ve successfully launched your campaign. New ads are under review and will serve pending approval. You will receive an email within 24 to 48 hours if your ad has been rejected.”

The post How to Set up a LINKEDIN Ads Campaign? appeared first on Artificial Intelligence.

LinkedIn has been at the forefront of AI for years, and it employs it in a variety of ways that users may not be aware of.

LinkedIn is one of the most popular recruiting platforms available today. Recruiters from all around the world use LinkedIn every day to find and filter applicants for specific job openings.

LinkedIn is well-known for being one of the software behemoths that has pushed the limits of machine learning research and development. LinkedIn has been constantly exploring cutting-edge machine learning techniques in order to make artificial intelligence (AI) a first-class member of the LinkedIn experience, in contrast to cultivating one of the world’s wealthiest datasets.

Here are a few examples of how LinkedIn uses Artificial intelligence:

LinkedIn makes substantial use of data analytics and artificial intelligence to enhance the experiences of its users and customers. Members are exposed to it on a daily basis, whether they are receiving employment recommendations, suggestions for connecting with others, or reading helpful stuff in their feed. AI assists enterprise customers, such as recruiters, in finding new talent pools and receiving insightful data. AI systems have had a significant impact on both sides of the platforms, adding significant value.

According to a report, there was a 30% increase in job applications after the improvement of the personalization tool, which recommends jobs that members might be interested in. In general, job applications overall have grown more than 40% year-over-year, based on a variety of AI-driven optimizations that have been made to both sides of the member-recruiter ecosystem. The email responses to cold emails from recruiters have increased by 45% while at the same time cutting down on the notifications needed to send to the job seekers that have received such messages. Even better, the total number of continuing, two-way conversations started in LinkedIn Recruiter has doubled. Every message a recruiter sends is twice as likely to turn into a hire after the most powerful LinkedIn AI tools are implemented. AI has also improved article recommendations in the feed by 10-20% based on click-through rate. With all of these amazing statistics, the platform has reached 660 million users and engagement has increased 50% year over year. The value capture consists of increasing the number of users, specifically paid users and the number of enterprise subscriptions from companies.

LinkedIn also uses AI to help businesses overcome human biases that stifle equality. These AI features are included in LinkedIn’s Talent Insight product, which is geared at recruiters and focuses on diversity and inclusion.

LinkedIn can monitor what occurs in the recruitment process in terms of gender, providing firms with data and insights into how their job postings and InMail are functioning. In addition, the top search results in LinkedIn Recruiter will be re-ranked to be more representative.

Companies can observe the gender breakdown in each step of the hiring process according to LinkedIn’s reporting data on gender in the hiring process. They can also have a better understanding of the overall talent environment, such as how skill sets change over time and how to discover talent pools. Companies can also evaluate their gender breakdown to that of their counterparts in the sector to see how they might recruit from a more diverse pool.

AI also works in the background, protecting its members from hazardous information, routing connections to maintain a fast site performance experience, and ensuring that the notifications given to the users are informative but not obtrusive.

The post LINKEDIN USES ARTIFICIAL INTELLIGENCE IN THESE INCREDIBLE WAYS appeared first on Artificial Intelligence.

When it comes to job hunting, there is no other place than the largest professional and employment-oriented service platform LinkedIn. With hosting over 20 million active job postings, the largest hiring marketplace has been continuously developing its platform with the help of using intelligent models to optimise various processes such as job postings, job recommendations, handling abusive contents and much more.

To the latest, the developers at LinkedIn recently unveiled a new deep learning model known as Job2Questions. Using it, recruiters can ask screening questions online to filter qualified candidates easily. The professional social networking platform has many conventional on-demand services and state-of-the-art machine learning models that help millennials and professionals in the global workforce. 

The developers stated that the primary goal is to match jobs with qualified applicants and improve hiring efficiency while reducing the requirement of manually screening each applicant. 

The Deep Learning Model

In order to solve the issue of generating screening questions, the researchers at LinkedIn developed a two-stage deep learning model known as Job2Questions. In this method, a deep learning model is applied to detect intent from the text description and rank the detected intents by their importance based on other contextual features.

Job2Questions is a Screening Question Generation (SQG) model that automatically generates structured SQs for a given job posting. According to the developers, this AI product is new compared to the earlier products and has no historical data. The researchers employed deep transfer learning to train complex models with limited training data.

Most of the time, interviewing applicants for a job profile is costly and inefficient. A traditional hiring method follows a number of steps such as screening the applicants in the pool by their profile and conducting additional phone screenings before sending out interview invites. This process is not only time consuming but also ends up in finding out such applicants who have missing basic qualifications such as work authorisation/visa, minimum years of experience, or degree requirements.

This is where Job2Questions comes into play. This new model addresses such issues, ease the manual workload by estimating person-job fit automatically. It is a Screening Question (SQ) -based online screening product for LinkedIn, which works by asking job-specific questions to applicants and assessing them using the answers they provide when applying for the job.

Challenges

The researchers mentioned two significant product challenges for designing a successful SQ-based online screening product:-

• The product should provide an easy way to add SQs to jobs, since asking the recruiters to reformulate their job postings into SQs manually is a waste of time and human resources.
• SQs should help recruiters identify qualified applicants quickly. A job requirement may have many different expressions, using unstructured text questions to present SQs will be tough for recruiters or AI models to interpret the intent of the SQ.

Benefits

This new AI model has a number of benefits, as mentioned below:

• Job2Questions model helps recruiters to find qualified applicants, and job seekers to find openings they are qualified for
• Job2Questions model is cost-effective and addresses hiring inefficiency
• The model has been created to solve hiring issues while making it easy
• Job2Questions eases the manual workload by estimating person-job fit automatically

Contributions To This Research 

• According to the researchers, this is the first work on the Screening Question Generation (SQG) task, which generates structured screening questions to help assess job applicants
• The researchers proposed and deployed the first SQG model, Job2Questions in production to help millions of jobs finding qualified applicants and assist hundreds of millions of members in identifying qualified jobs
• During an offline evaluation, the proposed Job2Questions model improved the AUROC of both template classification and question ranking by 178% and 27.4%, respectively
• Job2Questions significantly improved the online SQ suggestion quality by +53.10% acceptance rate and +22.17% job coverage

Wrapping Up

According to the developers, the online A/B testing showed +53.10% screening question suggestion acceptance rate, +22.17% job coverage, +190% recruiter applicant interaction, and +11 Net Promoter Score.

In one of our articles, we discussed how the recommendation system of LinkedIn is generating the perfect job match. Amid the pandemic, the platform has also announced to offer free job postings to accelerate hiring for critical roles during the COVID-19 crisis. This initiative has been launched to promote urgent jobs and recruiting resources to organisations in critical need such as healthcare, supermarket, warehousing and freight delivery, including disaster relief nonprofits to help them find talent quickly.

The post HOW LINKEDIN IS USING DEEP LEARNING TO INCREASE HIRING EFFICIENCY AMID RECESSION appeared first on Artificial Intelligence.

Zoom has nixed a feature that came under fire for “undisclosed data mining” of users’ names and email addresses, used to match them with their LinkedIn profiles.

The feature, the LinkedIn Sales Navigator, is a LinkedIn service used for sales prospecting. When users enter a web conference meeting, the tool automatically sent their user names and email addresses to an Zoom internal company system. This system would then match this data to their LinkedIn profiles, according to a New York Times investigation.

Per The New York Times, the tool also automatically allowed other meeting participants to covertly access this LinkedIn profile data, without Zoom asking for users’ permission or notifying them. That means if a user is in a Zoom meeting – even if they aren’t using their real names – other participants could collect information about their real names, locations, employer names and job titles.

The tool was removed on Thursday as part of several sweeping changes Zoom made in response to snowballing security and privacy concerns. Zoom founder Eric Yuan said in a Wednesday post responding to the concerns that Zoom will freeze the development of its features and instead focusing on security and privacy issues.

“Over the next 90 days, we are committed to dedicating the resources needed to better identify, address and fix issues proactively,” said  Yuan. “We are also committed to being transparent throughout this process. We want to do what it takes to maintain your trust.”

With more employees working from home over the past few weeks due to the coronavirus pandemic, Zoom has ballooned in popularity to include 200 million daily meeting participants in March. To put that into context, the maximum number of daily meeting participants on Zoom in December was 10 million.

But questions over what data Zoom collects – and how it is secured – have also increased. On the privacy front, Zoom this week removed a feature in its iOS web conferencing app that was sharing analytics data with Facebook, after a report revealing the practice sparked outrage. According to the Motherboard report last week that originally disclosed the privacy issue, the transferred information included data on when a user opened the app, a user’s time zone, device OS, device model and carrier, screen size, processor cores and disk space.

The issue left the public — including New York attorney general, Letitia James — demanding more information about how Zoom secures user data. Some have even prohibited use of the video-conferencing app — including, according to Reuters, Elon Musk’s SpaceX rocket company, which cited “significant privacy and security concerns.”

Yuan said Wednesday, in response to these privacy concerns, that Zoom will prepare a transparency report detailing information related to data, records or content. In addition, he said, Zoom has now updated its privacy policy “to be more clear and transparent” around what data is collected and how it is used. The policy now explicitly clarifies that Zoom does not sell users’ data and will not going forward.

On the security side of things, Zoom has now patched several recently-disclosed vulnerabilities – including two zero-day flaws uncovered this week in the conferencing platform’s macOS client version, and a UNC path injection vulnerability in the Zoom Windows client, which could enable attackers to steal Windows credentials of users.

Moving forward, Yuan said Zoom would be “enhancing” its current bug-bounty program, and creating white-box penetration tests to “further identify and address issues.”

“Transparency has always been a core part of our culture,” said Yuan. “I am committed to being open and honest with you about areas where we are strengthening our platform and areas where users can take steps of their own to best use and protect themselves on the platform.”

The post Zoom Removes Data-Mining LinkedIn Feature appeared first on Artificial Intelligence.