Embracing the Algorithm: The Power of Advanced Targeting in Recruitment Marketing

From personalised recommendations to programmatic advertising, social media algorithms have transformed recruitment marketing forever. Gone are the days of manual targeting and archaic ad placements with low-quality creative. In today’s era of speed, scale, and precision, spreadsheets and intuition simply can’t compete with advanced social recruiting technology.

Manual Targeting VS Advanced Targeting in Recruitment

At Adway, we've been obsessed with talent targeting on social media for 15+ years. From the first retargeting pixels to today’s AI-powered social engines, I’ve watched and hopefully contributed to the evolution up close. Join me for 4 minutes of raw reflections on why manual targeting in recruitment is dead, and what it takes to target quality candidates today.

(Click on the image to play!)

5 Things to Stop Doing:

1. Relying on Basic Demographic Targeting: Simply targeting candidates based on age, location, or job title is no longer effective or even allowed. This approach overlooks the nuanced behaviours and preferences that modern algorithms can effectively identify.
   
   
2. Using Interest Stacking and Lookalike Audiences Exclusively: These methods were once gold standards but have become less reliable, especially after privacy updates like iOS 14. Over-reliance on these tactics can limit your reach and effectiveness.
   
   
3. Ignoring Ad Quality: In the past, throwing together an ad quickly might have been enough, but today’s algorithms penalise low-quality ads. Poor ad quality results in lower visibility and higher costs.
   
   
4. Focusing Solely on Manual Bid Strategies: Manually setting bids for ad placements is outdated. Automated bidding strategies, optimised by AI, often yield better results with less effort.
   
   
5. Expecting Long-Term Success from One Ad: The days of finding one magic ad and scaling it endlessly are over. Ads need to be continually tested and refreshed to maintain performance.
   
   

6 Things to Start Doing:

1. Leveraging Behavioural Data: Focus on understanding the behaviours and preferences of your target audience. This includes analysing engagement patterns, content interaction, and even seemingly trivial actions that algorithms can interpret to predict job fit.
   
   
2. Emphasising Creative Testing: Run a high volume of creatives each month, experimenting with formats like video, images, and testimonials. Let the algorithm identify what resonates most with your audience – and make sure to blend job ads with employer branding ads for balance.
   
   
3. Prioritising Ad Quality: Invest time in creating high-quality ads that are visually appealing and relevant. Use compelling copy, strong visuals, and a seamless user experience to boost your ad quality score.
   
   
4. Adopting Automated Bidding Strategies: Allow AI to optimise your bids for you. Automated strategies adjust in real-time based on performance data, often leading to better outcomes than manual bidding.
   
   
5. Building and Optimising Social Talent Pools/Retargeting audiences: Continuously update and engage with your Social Talent Pools. Use APIs and XML feeds to integrate seamlessly with your ATS, keeping your candidate database dynamic and in sync with your audience on Social.
   
   
6. Minimising Clicks and Maximising Conversions: Streamline the application process by reducing the number of clicks required for candidates to apply. Leveraging tools like META's Easy Apply (or Instant Forms) minimises friction in the candidate experience, keeping them engaged and reducing drop-off rates. This approach not only enhances the candidate journey but also improves the efficiency of AI-driven optimisation, ensuring that your ads reach the most qualified candidates. Ultimately, this is how you boost the quality of hire — by making sure the right candidates convert instead of dropping off.

The Smart Ways AI Predicts Your Next Top Hire

A fascinating example of this is the "Curly Fry Conundrum," as explained by Jennifer Golbeck. Imagine this: a candidate who likes a photo of curly fries might also be someone who enjoys problem-solving or has a keen interest in tech-related humor. At first glance, these connections seem random, but social media algorithms are incredibly adept at finding patterns that humans might miss.

These algorithms can correlate such seemingly unrelated actions with attributes that could make someone a great match for a particular job. For instance, someone who frequently engages with content about AI, follows tech influencers, and shares articles on leadership might be an ideal candidate for a "Senior Sales Manager in Tech" role. This ability to uncover hidden insights from user behaviour has transformed how we think about recruitment marketing. It’s impossible to beat advanced algorithms with manual inputs.

How Facebook’s Ads Algorithm Works

Facebook’s ad algorithm has evolved into a highly sophisticated system that prioritises user experience while maximising value for advertisers. It’s not just about who bids the most; it’s about who can create the most relevant and engaging ads for the right audience.

Key Components of the Ad Auction

1. Advertiser Bid: The bid is the amount an advertiser is willing to pay for a specific action, such as a click or conversion. While the bid is important, it’s not the sole determinant of ad success.
   
   
2. Ad Quality Score: This score reflects the overall quality of the ad. Factors like the ad’s content, the user experience on the landing page, and feedback from users (such as whether they hide or report the ad) contribute to this score. Higher quality ads are more likely to win the auction and be shown to users, even if they don’t have the highest bid.
   
   
3. Estimated Action Rate (EAR): EAR is Facebook’s prediction of how likely a user is to engage with or take the desired action on an ad. This prediction is based on machine learning models that analyse historical data, user behaviour, and real-time interactions. While the exact workings of EAR are somewhat opaque, its role is crucial in ensuring that ads are relevant and effective.

These three factors combine to create a “total value score” for each ad in the auction. The ad with the highest score wins the auction and gets shown to the user. This system ensures that users see the most relevant and engaging content, which benefits both the users and the advertisers by enhancing ad performance and reducing costs.

Machine Learning and Ad Delivery

Facebook’s ad delivery system is powered by advanced machine learning models that continually refine their predictions based on user interactions. As more people engage with an ad—whether by clicking, sharing, or applying for a job—the models become better at predicting the ad’s future performance. This dynamic learning process helps ensure that ads remain relevant and effective, even as user preferences and behaviours evolve.

The Shift to Creative Theory and the Importance of Compelling Creatives

In the early days of Facebook advertising, much of the focus was on finding the right audience through precise targeting—a concept often referred to as Pocket Theory. However, as Facebook’s algorithm has evolved, there’s been a significant shift toward what is now known as Creative Theory. This approach emphasises the importance of the ad creatives themselves over the intricacies of audience targeting.

Today, success in Facebook advertising is driven by the quality of the content you create. The algorithm has become increasingly content-oriented, meaning that well-crafted, engaging ads are more likely to succeed, regardless of the specific audience targeting settings. By experimenting with different ad formats—videos, images, testimonials—advertisers can leverage Facebook’s machine learning to identify which creatives resonate most with their audience.

This shift underscores the importance of continuous creative testing. Understanding your audience’s preferences and behaviors is more critical than ever. By continually refining and experimenting with ad creatives, advertisers can significantly enhance their campaigns' performance and reach.

Debbie explains how Artificial Intelligence drives smarter and more efficient targeting

In this video, our data engineer Debbie unpacks how AI, LLMs and Machine Learning power Adway’s Social Recruiting Technology. You’ll learn how these technologies optimise targeting and even predict recruitment outcomes in real time.

If you want a clear, practical look at how advanced AI is transforming recruitment marketing, this one’s for you:

Learning from the Frontlines: Insights from $20M in Ad Spend 

Another layer of insight comes from a recent online post by an experienced advertiser who has managed over $20M in Facebook ad spend since 2018. The evolution in ad strategies over the past few years offers valuable lessons for anyone involved in recruitment marketing.

From "Hacking the Ads Manager" to Content-Driven Success. Back in 2018, the focus was on "hacking the ads manager." Advertisers could scale businesses rapidly by exploiting targeting techniques like interest stacking and lookalike audiences. This was the golden age of digital advertising, where finding the right audience felt like cracking a code.

However, the landscape began to shift, particularly after the infamous iOS 14 update in 2021, which severely disrupted ad performance across the board. This update limited the tracking capabilities of apps and, by extension, the effectiveness of many targeting strategies that advertisers had relied on for years. It marked the end of an era where targeting hacks reigned supreme and forced advertisers to rethink their approach.

By 2022, the emphasis had moved away from intricate targeting tactics to a content-first approach. The advertiser in the online post highlights how their strategy shifted to testing a large volume of ad creatives—sometimes 50 to 100 different ads in a single month—to identify what resonates most with their audience. This change reflects a broader industry trend where the algorithm has become more content-oriented, rewarding those who invest time and effort into crafting high-quality, engaging ads.

The Power of Social Talent Pools

As the recruitment landscape becomes more complex, the concept of Social Talent Pools has emerged as a powerful tool. These pools leverage behavioural data from various platforms to identify and engage candidates. Most social media channels allow building retargeting audience based on engagement on your ads, visitors to your career page or landing pages. You can also sync your CV-database or talent pools in your Applicant Tracking Systems (ATS) through APIs or XML feeds. If you thereafter also make sure to categorise the Social Talent Pools by job category and location, you create a powerful source of audience that are highly engaged. 

This process doesn’t just pull candidates into the ATS; it also pushes out opportunities to candidates who might not be actively seeking them but fit the ideal profile. This continuous cycle of engagement keeps the talent pool fresh and relevant, making it easier for the algorithms to convert highly qualified candidates into your job openings. And because these pools are built on behaviour and engagement, they reduce human bias and champion inclusivity from the very first touchpoint.

Embracing the Future of Recruitment Marketing

The world of recruitment marketing is changing exponentially, driven by advances in AI, machine learning, and social media algorithms. These changes present both challenges and opportunities. For recruiters, the key is to embrace these new tools and techniques, leveraging them to boost quality of hire. The future belongs to those who harness automation not just to scale, but to stay precise, inclusive, and candidate-centric.

Why Recruitment Belongs Where Talent Spends Time: TikTok & Snapchat

Join Victor, COO of Adway, as he unveils how to leverage TikTok and Snapchat to transform your recruitment strategy. With examples of our most engaging ads, see firsthand how a multi-channel approach can amplify your reach and significantly boost your quality of hire. Learn about our predictive analytics and automation that ensure you're not just present but dominating every relevant social platform.

Ready to Take Social Media Targeting to the Next Level?

Let's boost the quality of your hires with smarter social media targeting. Book a demo today and see how Adway helps you reach, attract, and convert the right talent — faster.

Sources

• Jennifer Golbeck's "Curly Fry Conundrum" Talk: A detailed discussion on how seemingly trivial social media actions can reveal deep insights into user behavior. YouTube: The Curly Fry Conundrum
• Reddit Post on Facebook Ad Spend: A first-hand account of lessons learned from managing over $20M in Facebook ad spend since 2018. Reddit: Lessons from $20M in Facebook Ad Spend
• Facebook Ads Manager Help: An official guide to understanding how Facebook's ad auction works and how ad delivery is optimized. Facebook: How Ads Work
• Abdul Mateen's Guide on Facebook's Ads Algorithm: An exploration into the complexities of Facebook's ad auction system and the importance of ad quality and relevance. LinkedIn: How Facebook Ads Algorithm Actually Works
• Facebook Business on Targeting: Insights into Facebook’s ad targeting capabilities and how audience selection impacts ad performance. Facebook Business: Advanced Targeting