Social media algorithms are evolving quickly, transforming how brands must approach their marketing and content strategies. While precise audience and creative targeting has historically driven paid media performance, platform algorithms are now encouraging a different approach – one that asks us to loosen the reins. In this article, I’ll explore how brands can navigate this new era of digital media planning and content production, offering practical insights and important considerations to help develop robust, future-ready strategies.

The New Rules of Algorithmic Engagement 

The platforms are sharing a message: trust the algorithms, reduce control and reliance on multiple campaign set up and targeting types. This might seem counterintuitive, but consumer interests are increasingly polarised and complex, my Instagram and your Instagram will look completely different. The sophisticated AI-driven algorithms of these platforms are suggesting (and proving) that they can be more adept at finding the right audiences for brand and business goals.

We’re seeing this shift materialise quickly in new platform tools. Google’s Performance Max (PMAX) and Meta’s Advantage+ products are examples of where the paid media industry is headed. These tools essentially ask marketers to step back from granular control, instead providing the algorithms with assets, conversion goals, and the freedom to optimise across placements and audiences automatically.

I’ve had personal experience working with brands who have implemented these capabilities, and will caveat that in some instances this is not always providing better results than previous campaigns. I’ve seen variation across different sectors, and the algorithms can favour a certain type of creative more than others, as it’s driving better results in platform. This is great for in-platform performance, but it may limit other important variables to your business, such as the types of product ranges or messages shown to the audience. This could mean higher-value products aren’t as visible in your advertising, important business metrics (that the platforms can’t see) aren’t being met, or strategic audiences aren’t being reached. In summary, don’t rush into changing your setup; test, learn and evolve, keeping your business priorities and parameters front of mind.

Impact On Consumer Expectation 

What I find quite fascinating (albeit a bit terrifying) about this evolution, is how deeply embedded algorithmic personalisation has become in consumer’s unconscious expectations. I know this from personal experience; recently, my boyfriend started watching sports videos on our Smart TV, which was connected to my YouTube account. What followed was surprisingly annoying – my perfectly curated feed was filled with content I had absolutely no interest in watching. This made it hard for me to decide what I wanted to watch, and ultimately, I ended up feeling lost on the platform. The algorithm was no longer lulling me into videos that it knew only I would like, and therefore I found it hard to stay engaged.

This experience brought to the forefront for me that consumers have grown to expect – and rely on – highly personalised content feeds. The algorithms have become so good at serving relevant content that any deviation from our personal interests feels like an unwelcome interruption. This in itself highlights the importance of brands focusing on the authenticity, relevance and quality of their creative and content. 

The Power of Creator Content and UGC 

The type of content we’re feeding into social media systems matters more than ever before. User-generated content (UGC) and creator collaborations have emerged as powerful tools, not just for organic reach but also for paid media success. This is because these formats often mirror the authentic content people naturally engage with on these platforms. Creators and influencers have been around for a long time, this isn’t a new concept, but increasingly it’s their content that is flooding social feeds. That is because this is what consumers want to engage with more and therefore what the algorithms continue to prioritise.

This is where collaboration between paid and organic becomes crucial. The most successful brands are those that bring these teams together. Content that performs well organically will often make for good paid media assets, whilst paid amplification can help scale your best organic content to a wider relevant audience.

Key Principles

There are two key principles I’ve taken away whilst observing the shift in both product from partners and content from brands: First, platforms are suggesting loosened targeting parameters, allowing their algorithms to determine which users are most likely to engage with your content based on your desired outcomes. Instead of manually selecting audience segments and creative, the recommendation is to let AI better find the right audience for your brand, product and outcomes.

Second, good content and creative variation has become crucial. The algorithms need a diverse range of high quality creative assets to effectively learn and optimise performance. This means giving the AI enough good content and data to understand what resonates best for the outcome you’re looking to achieve. Brands that continue with creative that isn’t fit for platform purpose, are likely to see continued diminishing performance, as we’re only going to see the importance of content continue to grow.

Considerations For Success 

Embrace & Test New Paid Media Products: Start by testing the platforms’ ability to find your audience. Focus on broadening your audience parameters in paid media; if you’re in a position where you’ve over-relied on targeting, you could start by taking a step-by-step testing approach. More targeted audiences could also be driving up your costs, so taking a step back to critically think about how you could change your audience and targeting strategy is a good place to start – remembering these products are still relatively nascent and therefore you should proceed with taking a low-risk testing approach.

Creative Diversity: Invest in producing multiple variations of your creative assets. This isn’t about quantity over quality – it’s about giving the algorithms enough data points to optimise effectively. Consider incorporating UGC and creator content into your paid media mix, or re-working old content to reduce cost and creative load. Always keep authenticity and relevance front of mind when producing this type of content. 

Content Quality at Scale: Develop systems and processes that allow you to produce varied, high-quality content consistently and affordably. This might mean exploring new production methods, or partnerships with creators who can produce authentic content at scale.

Cross-Team Collaboration: Break down the silos between organic and paid teams. Establish regular touchpoints where teams can share insights about top-performing organic content, emerging trends, and audience engagement patterns. Create workflows that allow successful organic content to be quickly adapted for paid campaigns, and ensure paid media learnings inform organic content strategy. This collaboration will inevitably lead to more efficient use of resources, and better overall performance across both channels if implemented correctly.

Continuous Learning: Monitor performance data to understand which creative elements resonate best, then use these insights to inform future content development. Pay particular attention to how organic content performs before deciding what to amplify through paid channels.

The Balance of AI and Business Intelligence 

Whilst embracing algorithmic targeting will become increasingly important, it’s equally important to maintain strategic oversight and human intelligence. This is about finding a sweet spot between algorithmic efficiency and business insight.

Your business knowledge remains invaluable here. You understand which products drive the most volume, which customers have the highest value, and which strategic priorities need to be balanced. This insight should inform how you set boundaries for targeting and creative within the platforms.

The key is to create a set up where algorithms can optimise within parameters that align with your business goals. For example; you might allow more “algorithmic” targeting strategies across certain campaigns or products, whilst also maintaining campaigns where you have more manual control and prioritise your efforts towards audiences, creatives or products because you know this brings back value to your business that the platform can’t yet see. Regular test and learn will help refine this balance, and you must focus on leveraging AI capabilities, measurement, business expertise and human intuition effectively.

The Modern Brand Method 

The reality is, we’ve heard platforms advocate for algorithmic optimisation for a while. But each day the algorithms become more sophisticated. Brands that will thrive are those that can adapt their approach whilst maintaining strategic oversight. This means moving away from the traditional ‘control everything’ mindset toward a more fluid, collaborative approach.

Success will depend on mastering three balances: giving algorithms room to optimise whilst maintaining oversight & business intelligence, scaling creative production whilst preserving authenticity, and blending organic and paid strategies into a unified approach. It’s about creating an ecosystem where AI, human insight, and creativity work together – fundamentally rethinking how we approach modern marketing.