Author: Social Nucleus

Why Smart Brands Don’t Outsmart the Machine

 

Meta’s advertising algorithm is no longer a tool you control. It’s a machine you collaborate with.

While many brands still cling to manual controls and outdated hacks, the smartest ecommerce brands are doing the opposite: they’re aligning with Meta’s machine learning priorities. They’re fuelling it, not fighting it.

This guide will show you how to:

• Understand how Meta’s ad delivery algorithm actually works (without the fluff)
• Diagnose how aligned your current setup is
• Fix what’s broken
• Scale smarter

 

1. The Machine’s Brain: Probabilistic Machine Learning & Bayesian Forecasting

 

Meta’s algorithm is powered by probabilistic machine learning. Every impression is a prediction: “What is the probability this user will take a desired action if we show this ad right now?”

That prediction is made using Bayesian forecasting. The algorithm starts with prior knowledge, then continuously updates its beliefs as it receives new signals (like purchases, clicks, scroll depth, time of day, device type, etc).

Key Concepts:

• Probabilistic models deal with uncertainty. They don’t say “this will happen,” they say “this is likely to happen.”
• Bayesian forecasting allows the algorithm to update its predictions as new data becomes available.

Practical Impact:

• Meta is constantly experimenting, even mid-campaign.
• The algorithm balances exploration (testing new options) with exploitation (scaling known winners).

Analogy: Imagine Meta as a casino manager running 5 different slot machines (your 5 ad creatives).
At first, it gives each machine equal play. As soon as one starts paying out more (converting), it shifts more budget there. But it still tests the others just in case. This is known as a “multi-armed bandit” strategy.

 

2. Signals & Priors: The Inputs That Drive Everything

 

Meta’s models rely on two key inputs:

• Signals: Real-time and historical data points about users, context, creative performance, ad account history, and conversion feedback.
• Priors: Baseline assumptions formed from years of training on large datasets — including platform-wide behaviors, advertiser verticals, and seasonal norms.

 

Examples of Signals

• Pixel and Conversions API data
• Add to cart, purchase, and view content events
• Time of day, device type, user interests, engagement metrics

 

Examples of Priors

• Apparel ads usually perform better on weekends
• New creative variants typically take ~50 conversions to stabilize

 

Why This Matters

• You can’t directly “feed” priors — but you can influence how fast they adapt by sending the right signals.
• Poor-quality signals (like click optimizations instead of purchase data) lead the algorithm to optimize for the wrong outcomes.

Takeaway: Bad signals confuse the machine. High-quality conversion feedback teaches it exactly what success looks like.

 

3. How the Algorithm Uses These Inputs to Optimise Delivery

 

User-Level Personalisation: Meta predicts the likelihood of conversion for each individual user based on past behavior current context. Every auction includes:

• Estimated action rate (probability user will convert)
• Advertiser bid or cost goal
• User value score (ad quality, landing page experience)

 

Creative Delivery Optimisation: Meta runs a continuous test on your ads. The best-performing creative gets the lion’s share of spend. This isn’t random — it’s a probabilistic bet based on early signals.

Auction Bidding: Meta doesn’t always select the highest bid. Instead, it uses:

• Total Value = Bid x Estimated Action Rate + User Value This balances cost, predicted results, and experience

 

Creative Delivery Optimisation: Meta runs a continuous test on your ads. The best-performing creative gets the lion’s share of spend. This isn’t random — it’s a probabilistic bet based on early signals.

Budget Pacing: Meta forecasts future performance and adjusts spend throughout the day or campaign lifetime. It paces budget dynamically based on:

• Time-based opportunity predictions
• Remaining budget
• Observed conversion rates

 

4. Why Fighting the Algorithm Fails

 

Common mistakes marketers make:

• Manually adjusting budgets hourly
• Constantly editing campaigns
• Over-targeting (e.g. stacking 10+ interests)

 

These tactics:

• Interrupt the learning phase
• Starve the algorithm of stable data
• Cause volatile performance

 

Analogy: Imagine you’re cooking and keep turning the oven on and off every 3 minutes. The dish never cooks. That’s what resetting Meta’s learning phase does. Instead, you need to:

• Set clear goals (e.g. Purchase, not traffic)
• Trust the system to find the right users
• Let campaigns run long enough to collect meaningful data

 

5. Creative = Fuel. Not Just Fluff.

 

You can’t outbid or out-target the algorithm. But you can out-create your competitors. Meta personalises which creative is shown to which user.

• That means ad creative needs to be diversified
• Different creatives resonate with different people

 

Creative Best Practices:

• Launch 3–5 concepts per week
• Test multiple variations of each concept (5-10) (e.g. hooks, formats, copy angles)
• Use Dynamic Creative or Advantage+ where appropriate

Pro Tip: Don’t spread creatives across different ad sets. Let the algorithm test them side by side in fewer ad sets and auto-optimise.

 

6. Targeting: The Case for Going Broad

 

Broad targeting isn’t lazy. It’s strategic.

Why it works:

• Meta already knows more about users than your interest guesses
• Going broad gives the algorithm room to find high-probability converters
• The algorithm personalises delivery within the audience

 

Analogy: Narrow targeting is like searching for treasure in a sandbox. Broad targeting is like giving the algorithm a metal detector and the whole beach.

 

When to use Lookalikes:

• As a warm start
• Only if you have strong seed audiences

But even then, broad often outperforms in scale phases.

 

7. Learning Phase: Why You Shouldn’t Panic Early

 

The first ~50 conversions in a new ad set are part of Meta’s “learning phase.”

• Performance may be volatile
• The system is actively testing combinations

 

Common errors:

• Pausing ads mid-learning
• Making big budget shifts
• Swapping out creatives too soon

 

Best approach:

• Let the learning phase complete
• Make incremental changes
• Monitor signals, not just results (CTR, CVR, CPA trends)

Pro Tip: Always batch changes to avoid multiple resets.

 

8. The Creative Fuel + Algorithm Freedom Matrix

 

	Low Creative Volume	High Creative Volume
Manual Controls	Red zone (Stuck	Yellow zone (Inconsistent)
Algorithm Alignment	Blue zone (Slow Growth)	Green zone (Scalable)

Green sone = You give the algorithm freedom AND fuel. That’s where scalable growth happens.

 

9. Diagnose Your Account: Use the Calculator

 

Score yourself on:

• Creative Volume
• Signal Quality
• Targeting Style
• Budget & Bid Setup
• Learning Phase Discipline

Use the “Creative Fuel + Algorithm Freedom” Calculator to get your score and recommended fixes.

LINK TO CALCULATOR

Meta’s advertising system just underwent its most significant evolution in years. A new AI-driven engine called Andromeda is now at the heart of Facebook and Instagram ad delivery – and it’s changing the game for eCommerce brands. This visionary technology is more than a tech upgrade; it’s redefining how ads are selected, whom they’re shown to, and how advertisers should strategize.

In this article, we’ll break down what Andromeda is (in simple terms), why it fundamentally changes Meta’s ad delivery, and what it means for your media buying strategy. From creative and targeting to data signals and automation, we’ll explore how you should adapt to thrive in the Andromeda era of advertising.

 

What is Andromeda and How Does It Work?

 

Imagine having to choose the perfect ad for each person from tens of millions of possibilities – in a fraction of a second. That’s the job of Meta’s ad retrieval system, the first stage in showing ads on Facebook or Instagram. Andromeda is Meta’s new AI-powered retrieval engine designed to do this job with supercharged intelligence and speed.

In simple terms, Andromeda is an AI brain that scans a massive pool of ads and picks the most relevant ones for each user. It replaces older, rule-based methods with a powerful machine learning system that can analyze far more factors about the person and the ads than ever before. Andromeda runs on advanced hardware (like NVIDIA’s Grace Hopper chips), which allows it to crunch data faster and handle a huge volume of ads at once.

 

Here’s a basic rundown of how it works:

 

• Retrieval Stage – This is where Andromeda operates. When a user opens Meta’s apps, Andromeda instantly sifts through tens of millions of ad candidates and filters them down to a few thousand that could be relevant for that user. It uses deep neural networks to evaluate who the user is, what they might care about, and what each ad is offering, matching people to ads with uncanny precision.
• Ranking Stage – Next, Meta’s system ranks those few thousand candidates to pick the final ads you actually see, using more refined models to predict what will drive value for both the user and the advertiser. Andromeda makes this stage better by ensuring the candidates it sends forward are much more personalized and high-quality to begin with.
• Continuous Learning – Andromeda continuously learns from user behavior. Every scroll, click, or purchase feeds back into the AI. Over time, it gets smarter at predicting which ads will interest each person, optimizing for things like engagement or conversion. It’s designed to use engagement data across Meta’s platforms to refine its choices, rather than relying on just static audience rules.

 

In short, Andromeda is an AI engine that automatically determines which ads each user should see, far more efficiently and intelligently than the previous system. Meta calls it a “step-function improvement” in delivering value to both advertisers and users. For eCommerce brands, this means the platform can now do much more of the heavy lifting in finding the right customer for your product at the right time.

 

Why Andromeda Fundamentally Changes Meta’s Ad Delivery

 

This isn’t just a behind-the-scenes tech upgrade – Andromeda changes the rules of the game for how ads get delivered on Facebook and Instagram. Here’s why it’s so transformative:

 

• Unprecedented Personalization: Because Andromeda can consider 10,000x more model capacity (i.e. more data points and complex patterns) than before, it can deliver ads with a new level of personalization. It assesses far more factors when deciding which ads to show a user – not just basic demographics or a few interests, but deeper patterns in behavior, interests, and context. This means the ads people see are more likely to be exactly what they’re interested in at that moment, which is a win-win for users and advertisers.
• Better Relevance & Performance: Early results show Andromeda is making Meta’s ad network more efficient and effective. Meta reports a +6% improvement in recall (the system’s accuracy in retrieving relevant ads) and an +8% boost in ad quality scores for the ads selected, in its initial deployment. In practical terms, that translates to better outcomes – and indeed, Meta noted advertisers using these AI-powered systems saw a 22% increase in return on ad spend (ROAS) after turning on AI-driven targeting features. These are significant leaps in performance, implying that Andromeda is finding customers and driving conversions that previously might have been missed.
• Speed and Scale: Meta’s ad system has to work under extreme time pressure – it only has milliseconds to choose an ad. Andromeda’s advanced architecture makes the whole process faster and more scalable. Meta achieved a 3x increase in how many ad selections (inference queries) per second the system can handle. It also dramatically reduces the reliance on manual rules and heuristics that the old system used to need to cope with scale. In other words, the AI can handle a much larger pool of ads and users in real-time without breaking a sweat, which is crucial as Meta’s user base and advertiser base continue to grow.
• Adaptive, Data-Driven Targeting: One of the most game-changing aspects is how Andromeda changes audience targeting. Rather than depending on advertisers to pre-define narrow target audiences, Meta’s AI can learn from a huge pool of engagement data across Facebook and Instagram to find the right people for an ad. It looks at who is engaging with what content and automatically identifies new pockets of users that marketers might not have thought to target. This removes a lot of the guesswork from audience segmentation. The system essentially self-optimizes who sees your ads based on who’s converting or interacting – something that simply wasn’t possible at this scale before. It’s as if the algorithm is constantly doing massive multivariate tests in the background, discovering high-value audiences on its own.
• Integration of AI Across the Funnel: Andromeda doesn’t work alone – it supercharges Meta’s broader Advantage+ suite of AI tools. Advantage+ encompasses features like automated targeting expansion, budget optimization, creative optimization, and more. With Andromeda powering the retrieval step, those tools become more potent. Meta can leverage engagement signals, conversion data, and even creative elements in a cohesive AI-driven pipeline. For example, Andromeda enables predictive targeting to work better, which was a heavy computational task, by efficiently handling the data load.
• All of this fundamentally means the ad delivery is more automated and intelligent from end to end – from selecting the audience to choosing the creative variation to bidding for the impression.

 

For advertisers, these changes mean the platform is doing more of the work that marketers used to do manually. The ads are selected and shown based on granular data patterns and
real-time learning rather than just the targeting rules or bids you set up. Meta itself has indicated this is a “significant upgrade from its traditional ad-matching engine” – it’s a new paradigm.

Importantly, Andromeda’s improvements aren’t just theoretical. They’ve been a key driver of Meta’s recent advertising performance, even contributing to record revenue growth. If you’re an eCommerce brand advertising on Meta, this AI revolution under the hood is likely already affecting your campaigns’ outcomes. The question is: are you set up to take advantage of it?

 

Implications for Media Buying Strategy in the Andromeda Era

 

Creative: Volume and Diversity are the New Kingmakers

 

In the Andromeda era, creative becomes an even more critical lever for success. Why? Because when the AI is finding the right users for your ads, the main thing differentiating one ad from another is the creative itself – the visuals, the copy, the offer.

What’s changed: Meta’s system can now handle an explosion of ad variations. With improvements in retrieval, there’s essentially no penalty for having many creatives in play; in fact, it’s an advantage. Meta is seeing exponential growth in the number of active ads, thanks to tools like generative AI that let advertisers produce variations easily. In one month, over 1 million advertisers created more than 15 million ads using Meta’s new AI tools. And Andromeda is designed to take full advantage of this creative volume, efficiently sifting through all those variants to find which one works best for each audience segment.

Strategy shift: More is now more when it comes to creative. Media buyers and brand owners should invest in greater creative volume and diversity:

 

• Test many variations: Instead of a handful of ads, consider running dozens of creatives (or using Meta’s dynamic creative and Advantage+ creative features to generate combos). Different images, messages, formats, and CTAs give Andromeda a rich palette to choose from for different people. For example, you might supply 20 images and 10 text variations and let the system mix-and-match. The old concern of “too many ads will split my impressions” is less of an issue when the AI is optimizing distribution.
• Keep quality high: Volume doesn’t mean throw spaghetti at the wall blindly. Human creativity and strategy are still paramount – you need to feed the machine with on-brand, compelling ideas. Use the data: if the AI surfaces a winning creative for one audience, learn from that and iterate. Think of Andromeda as a high-powered recommendation engine: it will find which of your creatives work best, but you must continually refresh those inputs with new concepts and high-quality production.
• Leverage AI tools for creative: Meta has introduced new tools (like AI image generation, background removal, text variations, etc.) to help produce more creative assets. Brands should take advantage of these to scale their creative library. Early results show that businesses using Meta’s image-generation for ads saw a 7% increase in conversions – proof that even AI-assisted creatives can perform well. Use these tools to augment your creative team, not replace them. The goal is to efficiently generate variations that the algorithm can test, while maintaining brand standards.
• In summary, treat creative as the primary arena for your experimentation and effort. Andromeda loves lots of creative options – it will sort out which ad resonates with whom, as long as you give it enough to work with. In the past you might have rotated 5 ads; now you might run 50 or more across different formats. The brands that thrive will be those that marry creativity with data, rapidly producing new ideas and letting the AI pinpoint the winners.

 

In summary, treat creative as the primary arena for your experimentation and effort. Andromeda loves lots of creative options – it will sort out which ad resonates with whom, as long as you give it enough to work with. In the past you might have rotated 5 ads; now you might run 50 or more across different formats. The brands that thrive will be those that marry creativity with data, rapidly producing new ideas and letting the AI pinpoint the winners.

 

Targeting: From Micro-Sequences to Broad AI-Powered Audiences

 

Targeting on Meta has traditionally been about defining your audience – by demographics, interests, lookalikes, etc. With Andromeda, much of that targeting work is increasingly handled by the AI itself. This marks a shift from explicitly telling Meta who should see your ads to implicitly letting Meta figure it out based on who engages or converts.

What’s changed: The new retrieval system can utilize predictive targeting and large-scale lookalike modeling far more effectively. It aggregates user behavior signals across the entire platform to find patterns. For example, it might learn that people who watch certain Reels or engage with certain shopping posts are unexpectedly likely to buy your product, even if they don’t fit your “usual” customer profile. Andromeda can discover these correlations because it isn’t constrained by a limited manual audience definition – it looks at all signals to decide relevance. As noted earlier, Meta’s AI can now uncover new audience segments that marketers might have overlooked by analyzing broad engagement data. This means the algorithm is effectively doing continuous audience research and expansion on your behalf.

Strategy shift: Embrace broad targeting and let the algorithm work its magic. Concretely:

 

• Go broad (within reason): Instead of slicing and dicing into many small ad sets for each persona or interest group, consider consolidating. Use broad audiences or very large lookalike audiences as your starting point. You can still exclude obvious mismatches (e.g., if you sell women’s shoes, you might exclude men for certain products), but err on the side of inclusion. The more data Andromeda has (in terms of a large potential audience and lots of signals), the better it can find pockets of converters within that audience.
• Use Advantage+ targeting features: Meta’s Advantage+ tools include options like Advantage+ Audience (Detailed Targeting Expansion), which automatically expands beyond your interest targeting if more conversions can be found, and Advantage+ Shopping campaigns, which largely handle audience finding for you. Lean into these. Meta has reported that when advertisers turned on these AI-driven targeting features in Advantage+ (for example, letting the system expand to find more people similar to converters), they saw significant lifts in performance – notably that 22% increase in ROAS we mentioned came specifically from using AI-driven targeting in creative optimization.
• Don’t over-segment your campaigns: In the past, you might have separated campaigns by devices, ages, or interests to control spend. Now, over-segmentation can actually hurt performance because it deprives the AI of data and scale. With Andromeda, a single campaign with a broad audience might outperform five narrowly targeted campaigns, because the system will automatically prioritize impressions to the sub-groups most likely to convert. We’re essentially moving from manual segmentation to algorithmic segmentation. Trust Meta’s delivery system to find the best segments for you.
• Keep using your first-party data – like Custom Audiences or Lookalikes – but consider them as just a starting point, not the end-all. Uploading a customer list or targeting website visitors is still valuable (it gives the AI a hint about who has interest), but now Meta can go well beyond that and find similar people you didn’t provide in a list. The key is to allow expansion and not restrict to only those lists if scale is a goal.

 

In short, the targeting mindset needs to shift from “Who do I think will buy?” to “Let’s see who the AI finds for me.” This doesn’t mean you stop caring about who your customer is – it means you leverage Meta’s vast data to discover customers you might not have identified on your own. Your role as a media buyer shifts from manually hunting for audiences to setting the parameters and letting the machine hunt, then monitoring and guiding as needed. It’s a bit like moving from being an archer to being a coach for a heat-seeking missile: you set it up, and it finds the target.

 

Signal Quality: Feeding the AI the Best Data

 

When algorithms are driving the optimization, data is the fuel. Andromeda’s effectiveness at finding the right users and optimizing delivery is only as good as the data (signals) it gets about what results you care about and what’s happening off-platform. In the post-iOS14 world of reduced third-party tracking, this has been a challenge – but it also means brands must take charge of their own data to help Meta’s AI succeed.

What’s changed: Meta’s AI is now better at using subtle signals. It doesn’t just rely on a straight line from click to purchase; it can incorporate a variety of engagement signals (like video views, content interactions, profile info, etc.) into its model of who is a good prospect. In fact, a strength of Andromeda is aggregating engagement data across the entire Meta ecosystem to improve ad relevance. However, the ultimate signal for an eCommerce advertiser is usually a conversion (purchase event). Due to privacy changes (like Apple’s iOS 14+ prompting users to opt out of tracking), Meta might not see all conversions unless you have taken steps to send that data back. And the quality of the conversion signal (how accurately and richly it’s recorded) makes a big difference in how well the AI can optimize for your true goals.

 

Strategy shift: Invest in signal quality to “train” Andromeda with the right feedback. Key actions include:

 

• Implement the Conversions API (CAPI): If you haven’t already, integrating Meta’s Conversions API is critical. This allows your server or eCommerce platform to directly send purchase events (and other important actions) to Meta, supplementing or replacing what the pixel might miss. It ensures that even if a user has limited tracking on the browser, Meta still gets the conversion data through your server. The more complete your data, the better the AI can learn who converts and optimize towards those outcomes.
• Optimize for the right events: Choose the conversion event that truly matters (purchase, subscription, etc.) and make sure Meta is optimized for it in your campaign objective. If you have a sales funnel, also feed intermediate signals (Add to Cart, View Content) into Meta – even if you don’t optimize for them directly, they inform the algorithm’s understanding of user intent. And if your sales have varying values, use Value Optimization or pass back the revenue value of each conversion. Andromeda, through the larger ranking system, will try to maximize advertiser value; giving it purchase values helps it aim for higher-quality customers (e.g., those who spend more).
• Ensure event accuracy and enrichment: Work with your dev team to make sure events aren’t duplicated or missing. Include details in the event parameters (like product category or customer segment) if relevant, as this data can potentially feed into better optimization or segmentation by the algorithm. Meta’s system can now handle a lot more complexity, so giving richer data (within allowed privacy limits) can only help. For instance, if you can pass a customer lifetime value or a new vs. returning customer flag in a Custom Audience, do it – these are signals that later could be used in value-based lookalikes or optimized campaigns.
• Prioritize fresh, high-intent data: With Andromeda rapidly adjusting in near real-time, make sure your data feedback loop is fast. If you launch a new product or promotion, ensure the conversions it generates are being reported promptly (via pixel or CAPI) so the algorithm picks up the trend. Also, consider using Meta’s aggregated event measurement to prioritize your highest-value events if you’re limited to a certain number – usually Purchase is #1, but if lead generation or other events matter, rank them so Meta knows what to focus on.
• Maintain privacy compliance while gathering data: As you strengthen signals, do it in a privacy-safe way. Use Meta’s tools for consent and make sure you have user permission where needed to collect data. This not only avoids disruptions but also ensures the data you do collect (albeit less than before 2020) is reliable and can be fully used by Meta’s AI.

 

In essence, Andromeda is like a very hungry, very smart student – it will learn whatever you teach it. The “teaching” happens via the data it receives about what outcomes you value (purchases, for example). High-quality, timely data is the feedback that guides the AI towards your business goals. If you starve it of data or feed it poor-quality info, you can’t expect it to perform miracles. Brands that set up strong data pipelines (think: precise conversion tracking, use of first-party data, leveraging Meta’s APIs) will arm the AI with the intel it needs to find more customers and optimize spend efficiently.

 

Automation: Embracing AI-Driven Campaign Management

 

With Andromeda and the AI wave at Meta, manual campaign tinkering takes a backseat to automation. This doesn’t mean marketers have nothing to do – rather, it shifts the focus to strategic inputs and away from low-level controls. Meta’s goal (and it should be yours too) is to let machine learning maximize performance within the guardrails you set, instead of you micromanaging every aspect.

What’s changed: The success of Andromeda goes hand-in-hand with Meta’s Advantage+ suite, which automates many aspects of campaign management. For example:

 

• Budget optimization: Advantage+ can auto-distribute budget between ads or ad sets to where it’s getting the best results.
• Placement optimization: Instead of manually selecting placements (Facebook Feed vs. Instagram Stories, etc.), you use Advantage+ placements and let Meta decide where the ad is performing best. The AI might find, for instance, that a certain creative performs exceptionally well in Reels and shifts more budget there.
• Dynamic creative and offers: Meta can automatically tweak creative elements (like using different overlays or music on a Reel) or optimize which product from a catalog to show which user.
• Bidding: More advertisers are moving to lowest-cost (automatic) bidding or using value optimization, rather than setting cost caps or bid caps. The reasoning is that the AI can bid more flexibly to capture conversions that are likely to be profitable, whereas a strict cost cap might block those opportunities. In fact, many in the industry are calling this “the death of cost caps and the rise of AI optimization.”

 

With Andromeda supercharging these automations, the overall system can do things like serve more ads to high-value segments in real time (using a concept called model elasticity) and adjust complexity on the fly to meet latency limits. The takeaway: Meta’s ad delivery is now highly automated and adaptive. Trying to override this automation with too many manual rules may actually reduce efficiency.

 

Strategy shift: Lean into Meta’s automation and simplify your campaign structure. Here’s how:

 

• Use Advantage+ Campaigns (especially for eCommerce): If you’re an eCommerce brand, Advantage+ Shopping Campaigns should be a staple in your strategy now. These campaigns automate audience targeting, placements, and even creative mixes to drive sales. Advertisers are finding that these largely out-of-the-box AI campaigns can outperform heavily managed conventional campaigns. Meta has effectively made Advantage+ the default/recommended way to buy ads for conversions because it utilizes all the AI muscle (like Andromeda) under the hood. Start with Advantage+ as your baseline, and layer in manual campaigns only as needed for specific goals (e.g., a retargeting promo).
• Simplify account structure: Consider consolidating campaigns and ad sets. A common approach now is to have a few big campaigns rather than dozens of small ones. For example, one campaign for prospecting (broad audience, using Advantage+), one for retargeting, and maybe one for a specific product launch or test – instead of separate campaigns for every audience or product. Fewer campaigns with larger budgets give the AI more flexibility to allocate spend where it sees the best results. This doesn’t mean you can’t test things – you can test different creatives or messages within these campaigns.
• Automate routine optimizations: Trust features like auto-bid (lowest cost) and Campaign Budget Optimization (CBO). If you’ve been clinging to manual bid strategies or spend caps on each ad set, consider testing life without those constraints. Meta’s automated systems can pace your spend to hit your goals if you give them a chance. The improved delivery engine can find the cheapest conversions available at any given moment. Many brands are finding they achieve a lower cost per acquisition by letting the algorithm roam freely, compared to forcing a cost cap that may make the campaign stall if it can’t find enough people at that exact cost.
• Monitor, don’t micromanage: Your role shifts to watching the algorithms and making higher-level decisions. For instance, rather than manually pausing an ad because its cost per purchase is a bit high today, you might look at the campaign holistically and give the AI time to adjust. Or you might decide, strategically, that the AI is allocating too much budget to a certain region that you want to deprioritize – that’s when you step in and adjust your settings or create a rule. But you’re no longer turning dials daily for slight bid changes or minor audience tweaks; you’re guiding the self-driving car rather than gripping the steering wheel for every turn.
• Take advantage of Meta’s recommendations and A/B testing: The platform will often give you recommendations (like “expand audience” or “use Advantage+ creative” or even budget suggestions). Don’t ignore these outright – they are usually based on broad data of what is working for others. Try them in controlled experiments. Use Meta’s built-in split testing to let the machine prove to you which approach works better. For example, run a split test: Advantage+ campaign vs. your best manual campaign. Chances are the automated one might surprise you with strong results, thanks to Andromeda’s behind-the-scenes optimization.
• Automation of creative analysis: One emerging aspect is using AI to analyze why certain ads work. While Andromeda itself doesn’t tell you the “why,” you can use tools (even third-party or manual analysis of metrics) to discern patterns – e.g., the algorithm keeps favoring ads with a certain message or creative style. Use that insight to inform your creative strategy. This way, you create a feedback loop between human insight and machine optimization.

 

Overall, the philosophy to embrace is “do less, but do it better.” Let Meta’s AI handle the heavy lifting of delivery and optimization. Free up your time from fiddling with granular settings so you can focus on strategy, creative brainstorming, and analyzing the big picture. Automation isn’t taking the art out of media buying; it’s taking the drudgery out, so you can put the art (and science) in the right places.

 

Recommendations: Thriving in the Andromeda-PoweredFuture

 

To wrap up, here are clear, forward-thinking steps and best practices for eCommerce brands and media buyers to excel in this new AI-driven advertising landscape:

 

1. Embrace AI-Native Campaigns: Make Meta’s AI features your default. Start with Advantage+ Shopping campaigns for prospecting, use broad targeting with detailed targeting expansion, and let the algorithm find the best opportunities. Shift budget into these AI-optimized channels and use manual campaigns more sparingly (e.g., for specific promotions or audiences that AI might not handle, like a very niche remarketing list).
2. Scale Your Creative Production: Invest in creating more ad variations than ever before. Assemble a pipeline for constant creative testing – new images, videos, ad copy, and formats. Leverage Meta’s creative tools (e.g., dynamic product ads, Advantage+ creative, AI-generated variations) to feed Andromeda a rich diet of options. The brands that supply abundant and diverse creative will allow the AI to match the right ad to the right person, yielding higher conversion rates. Don’t be afraid to let the machine test things that you’re unsure about – sometimes an off-beat creative will resonate with a certain audience that only the algorithm could identify.
3. Strengthen Your Data Signals: Treat data as a strategic asset. Implement the Conversions API to ensure Meta receives every conversion event (and with correct values). Regularly audit your pixel/events setup for completeness and accuracy. If you have offline conversions (phone sales, in-store purchases) or other meaningful events, integrate them into Meta’s system so Andromeda has the full picture. By improving signal quality, you’re effectively “training” the AI with better examples of success, which will improve its targeting and optimization accuracy.
4. Simplify and Streamline Account Structure: Consolidate campaigns and avoid fragmenting your audiences and budgets. Use Campaign Budget Optimization to let Meta allocate funds dynamically. A leaner account structure aligns with how Andromeda operates – it prefers a big playing field to run in. This also makes management easier on you and reduces the chance of internal competition between your own ad sets. One powerful campaign with 10 great ads can often beat 10 campaigns with 1 ad each, under the new system.
5. Rethink Metrics and Goals: With AI doing more optimization, ensure you’re guiding it with the right objectives. Optimize for what truly drives your business (e.g., purchase value, ROAS, or customer acquisition cost targets). Use Meta’s value-based optimization if possible. Additionally, focus on holistic performance and incrementality. As the algorithm finds conversions you might not have targeted, you want to measure if those are truly new customers or sales you wouldn’t have gotten otherwise. Consider running holdout tests or using Meta’s Conversion Lift studies to verify that the AI is driving incremental growth. Being a thought leader means not just accepting the results at face value but validating and understanding them.
6. Upskill Your Team for the AI Era: The role of a media buyer is evolving. Train yourself or your team on interpreting AI-driven campaign results and feeding the system the best inputs. This might mean learning more about creative strategy and storytelling (to produce better ads), data analytics (to crunch performance and feed insights back in), or even basic machine learning concepts to converse with Meta’s tech reps or understand platform updates. The more you understand how the AI works conceptually, the better you can work with it. For example, knowing that Andromeda values larger datasets might encourage you to pool budgets; understanding it uses engagement signals might prompt you to create content that drives interaction (even if not immediate sales) to help the algorithm learn.
7. Stay Agile and Experiment: The advertising landscape will continue to evolve rapidly. Meta will likely roll out more AI-driven features (perhaps auto-generated content, new optimization goals, etc.). Be prepared to pivot and try new things. Set aside a budget for experimentation with emerging tools – those who get in early often reap outsized benefits before the tactics become commonplace. With Andromeda as the new foundation, expect Meta to introduce even more automation. If you maintain a flexible strategy, you can adapt and incorporate these innovations ahead of competitors.

 

By following these recommendations, you position your brand not only to cope with the changes Andromeda brings, but to ride the wave and outperform. The common thread is clear: let the machines do what they’re good at (data-crunching, pattern recognition at scale, real-time optimization) and free up humans to do what they’re good at (creative strategy, brand building, and critical decision-making).

 

Conclusion

 

Meta’s Andromeda AI ad retrieval system marks a new epoch in digital advertising. It’s ushering in a future where ad delivery is smarter, faster, and more personalized than ever – largely driven by sophisticated algorithms working behind the scenes in milliseconds. For eCommerce marketers and media buyers, this isn’t a distant future; it’s here now, reshaping the performance of campaigns on Facebook and Instagram today.

The opportunity in this moment is massive. Brands that understand the implications of Andromeda’s AI power – and adapt their strategies accordingly – stand to gain a significant edge. By embracing broader targeting, doubling down on creative, improving data feedback, and leaning into automation, you let Meta’s AI become a growth engine for your business. You also future-proof your marketing operation for an increasingly AI-driven world.

Importantly, adopting these changes cements your role as a forward-thinking marketer. Rather than fighting the tide, you’re surfing it. You’re able to focus on strategy and creativity, while trusting the platform’s AI to handle the heavy lifting of optimization. The result is often better performance and insights that you can channel into product development, customer experience, and more – creating a virtuous cycle of improvement.

In a sense, media buying is evolving from manual art to symbiotic art-and-science. Andromeda is the science: billions of calculations to place each ad. Your job is the art: crafting the story and strategy that guide those calculations towards profitable growth.

As a thought leader (and a successful advertiser) in this new era, you’ll recognize that Meta’s Andromeda is not just a tech upgrade – it’s a partner. It’s here to augment your decisions with AI superpowers. Those who embrace this partnership will lead the pack in the future of eCommerce marketing. The ads ecosystem is transforming, and with Andromeda, the sky’s the limit – quite literally, as Meta named it after a galaxy. Now is the time to align your marketing strategy with this stellar new reality and turn the AI revolution into concrete results for your brand.

 

Sources:

 

• Meta Engineering Team – “Meta Andromeda: Next-gen personalized ads retrieval engine” (Dec 2024)
  engineering.fb.com
  engineering.fb.com
• MediaPost – “Meta Discloses ‘Andromeda’ Ad Retrieval” (Dec 2024)
  mediapost.com
  mediapost.com
• MaxBounty Blog – “Meta Releases Performance Results of New Andromeda Ad Tech” (Dec 2024)
  plus.maxbounty.com
  engineering.fb.com
• SwipeInsight – “Meta Introduces Andromeda: Next-Generation AI Retrieval System” (Dec 2024)
  web.swipeinsight.app
  web.swipeinsight.app
• Common Thread Collective – “Meta’s Andromeda: The Biggest Shift in Facebook Ads Since
  iOS14” (Mar 2025) – Podcast insights.

Many brands fall into the trap of relying on repeat purchases from existing customers to keep revenue steady. While customer loyalty is valuable, leaning on it as the main driver of growth is a risky approach. Brands that aren’t first-order profitable—that is, brands that don’t earn enough from a customer’s first purchase to cover their acquisition costs—operate like a “shrinking sponge.” As customers inevitably churn, the brand struggles to keep up with rising acquisition costs and diminishing returns.

To build a resilient, growth-focused brand, first-order profitability should be a core focus. By optimising for new customer profitability, relentlessly improving margins, and leveraging higher spend opportunities, your brand can unlock a sustainable growth engine and avoid the shrinking sponge effect.

 

1. The Risks of Relying on Existing Customers: Why Brands Become a “Shrinking Sponge”

Brands that fail to achieve first-order profitability tend to rely on existing customers to maintain revenue. Over time, this creates a “shrinking sponge” effect, where customer churn makes it harder to sustain consistent results year after year. Customers may not stay forever, and each year, more effort is required to replace churned customers with new ones, often at higher acquisition costs.

The Problem of Churn: Even with strong retention strategies, customer churn is inevitable. Brands that rely too heavily on repeat purchases risk creating a fragile revenue model that struggles to adapt to rising customer acquisition costs and diminishing loyalty over time.

The Impact on Long-Term Growth: Without first-order profitability, your revenue model becomes increasingly unstable, leading to higher dependence on paid acquisition, unpredictable revenue streams, and the risk of declining profitability. In contrast, brands that are profitable on the first order have a resilient foundation that isn’t solely reliant on repeat purchases.

 

2. NCPA: New Customer Profitability Always

New Customer Profitability Always (NCPA) is the principle of ensuring every new customer is acquired at a cost that is covered by the profit from their first purchase. NCPA means you’re generating positive cash flow from day one, without relying on retention to recover costs. This approach empowers brands to scale sustainably, without running the risk of becoming overly dependent on repeat purchases.

Why NCPA Is Crucial:

• Scalability: First-order profitability allows you to confidently acquire new customers, knowing each addition contributes to profit, not just future cost.
• Resilience: Brands with NCPA are less vulnerable to fluctuations in customer loyalty or retention, as each new sale adds value immediately.
• Flexibility for Growth: When each new customer generates positive cash flow, you have greater flexibility to reinvest in growth, marketing, and product development without relying on future purchases to break even.

What You Can Do: Regularly analyse your customer acquisition cost (CAC) against your average order value (AOV). If CAC is higher than AOV, work on strategies to increase immediate profitability, such as bundling products, upselling, or refining your ad targeting.

 

3. Margins Are Your Leverage: Optimise Them Relentlessly

High gross margins are a powerful competitive advantage in eCommerce. With strong margins, brands can absorb rising customer acquisition costs while remaining profitable. As ad costs continue to rise, brands with high margins will have the flexibility to stay competitive and pursue growth, while those with thin margins will struggle.

Why High Margins Matter:

• Ability to Absorb Higher Acquisition Costs: Strong margins provide a buffer that allows brands to sustain higher CAC while still achieving first-order profitability.
• Confidence in Ad Spend: Brands with high margins can invest more confidently in paid media, knowing that they have the margin flexibility to handle variations in acquisition costs.
• Financial Flexibility: High margins create a financial cushion that supports innovation, testing, and scaling, allowing brands to pursue opportunities without compromising profitability.

What You Can Do: Regularly review your cost of goods sold (COGS) and look for ways to improve efficiencies in production, sourcing, or pricing. Every improvement in margin strengthens your ability to scale, maintain profitability, and compete.

 

4. Higher Margins Unlock Higher Spend Tiers on Meta

One of the most significant advantages of high margins is the ability to scale quickly on paid media platforms like Meta (Facebook and Instagram). With high margins, brands can afford to play in higher tiers of ad spend, allowing them to reach larger audiences and accelerate growth.

Why This Matters on Meta:

• Reach Larger Audiences Faster: Higher spend tiers on Meta enable you to expand your reach, targeting more potential customers and increasing brand awareness.
• Accelerate Growth Quickly: With the capacity to absorb higher acquisition costs, brands with strong margins can confidently increase ad spend, driving faster customer acquisition and revenue growth.
• Stay Competitive in Bidding: On Meta, bidding plays a major role in ad delivery. Brands with higher margins can afford more aggressive bidding strategies, ensuring their ads reach target audiences despite rising competition.

Example: A fashion brand with a 70% gross margin can confidently increase its daily ad spend on Meta, knowing that each customer acquisition cost is sustainable within its high margin structure. This enables the brand to grow its customer base faster than competitors with lower margins.

What You Can Do: Focus on maintaining or improving your gross margins. The higher your margins, the more you can invest in higher spend tiers on Meta, outpacing competitors and reaching more of your target audience.

 

5. Leverage for Long-Term Success: How High Margins Help You Win

In eCommerce, leverage is essential for long-term success. Brands with strong margins, NCPA, and disciplined expense management are positioned to thrive in any economic climate. This leverage allows you to scale sustainably, withstand changes in the market, and take advantage of new opportunities as they arise.

Benefits of Leverage:

• Greater Resilience to Market Shifts: Brands with leverage can adapt to changes in acquisition costs, consumer behaviour, and platform dynamics without compromising profitability.
• Sustainable Growth: High-margin brands can grow confidently, knowing they have the financial foundation to support both acquisition and retention.
• Adaptability in Strategy: Leverage provides the flexibility to explore new channels, enter new markets, or launch new product lines without sacrificing profitability.

What You Can Do: Track and optimise your margins, acquisition costs, and overall financial structure. Ensure every business decision builds leverage for the future rather than compromising profitability for short-term gains.

 

Final Thoughts: Avoiding the Shrinking Sponge Trap and Building a Resilient Brand

To create a profitable, sustainable eCommerce brand, first-order profitability is essential. Avoid the “shrinking sponge” trap by ensuring that every new customer contributes immediate value to your business rather than becoming a future liability. Here’s a summary of the key points:

1. Focus on NCPA: Make every customer acquisition profitable from day one, so your brand doesn’t depend on repeat purchases for sustainability.
2. Optimise Margins Relentlessly: High margins give you the leverage to absorb rising ad costs, scale more aggressively, and maintain profitability.
3. Use High Margins to Access Higher Spend Tiers on Meta: With greater margins, you can confidently operate in higher spend tiers, reaching larger audiences and accelerating growth.
4. Leverage for Long-Term Success: Ensure your business model builds leverage, enabling sustainable growth and adaptability over the long term.

At Social Nucleus, we’re dedicated to helping brands drive sustainable, profitable growth. If you’re ready to take your brand to the next level, reach out to us today to learn how we can help you optimise margins, achieve NCPA, and unlock the full potential of your paid media strategy.

In today’s competitive eCommerce landscape, building a profitable brand that generates consistent cash flow requires more than just clever marketing. Successful brands are designed with a specific “genetic code” that enables them to thrive, scale sustainably, and create free cash flow. These brands don’t just grow; they flourish in any economic environment, thanks to a set of strategic traits.

At Social Nucleus, we’ve distilled these critical elements into a blueprint for building a cash-flowing eCommerce brand. By focusing on these eight essential traits, you can position your business for long-term, sustainable growth that goes beyond short-term wins.

 

1. High Gross Margins: Setting the Foundation for Profitability

Gross margin is the lifeblood of profitability. Aiming for 70% or higher in gross margin ensures that every sale contributes meaningfully to covering costs, generating profit, and funding growth. This includes everything from production costs to return rates, creating a solid foundation for reinvestment and stability.

What You Can Do: Calculate your current gross margin, factoring in all costs involved in getting your product to customers. If your margins fall short, explore opportunities to renegotiate with suppliers, reduce production expenses, or consider strategic pricing adjustments.

 

2. Lean Operational Expenditures (OpEx): Keeping Costs Under Control

Efficient operations allow brands to scale profitably. For the most resilient eCommerce brands, operational expenses (OpEx) remain under 15% of revenue. Lean operations mean more revenue flows directly into profit, which provides the business with flexibility and resilience in the face of change.

What You Can Do: Regularly audit your operational expenses. Identify areas where you can streamline, automate, or even cut costs. Focus on core activities that drive value, and avoid unnecessary overhead that can weigh down profitability.

 

3. Flexible Supplier Terms: Freeing Up Cash Flow with Strategic Agreements

Cash flow management is crucial for scaling. Negotiating favourable payment terms with suppliers—like net on delivery (N.O.D.) or extended payment timelines—can create a “negative cash conversion cycle.” This means you receive products, sell them, and collect revenue before payment is due to your supplier, giving you a powerful advantage in terms of cash flow.

What You Can Do: Approach suppliers to explore terms that allow delayed payments, ideally after the products have sold. This approach reduces pressure on your cash reserves, making it easier to grow and invest in new opportunities without needing outside capital.

 

4. First-Order Profitability: Acquiring Customers Profitably from Day One

One of the biggest indicators of long-term success is achieving profitability on the first order. When you’re acquiring new customers at a cost that is fully covered by the profit from their initial purchase, you’re creating a sustainable model that doesn’t solely rely on repeat purchases to recoup acquisition costs.

What You Can Do: Evaluate your average order value (AOV) in relation to your customer acquisition cost (CAC). If CAC exceeds AOV, consider strategies to increase immediate profitability, such as bundling products, offering upsells, or refining your ad targeting to capture high-value buyers.

 

5. Increasing Customer Lifetime Value (LTV): Unlocking Long-Term Revenue Potential

While first-order profitability is a powerful growth driver, enhancing customer lifetime value (LTV) amplifies long-term profitability. Ideally, brands should aim for a 30% increase in LTV within 60 days and a 100% increase within a year. This ongoing value from each customer supports scalable growth without needing constant new acquisitions.

What You Can Do: Create strategies to encourage repeat purchases and increase LTV, such as loyalty programs, personalised email marketing, or exclusive offers for returning customers. Track these metrics carefully to measure the impact on profitability over time.

 

6. Strong Organic Demand: Reducing Reliance on Paid Ads

Paid advertising is essential, but over-reliance on it can create dependency and strain profitability. The most successful brands generate a substantial portion of their traffic organically, aiming for at least 50% organic traffic. Organic demand lowers acquisition costs, improves margins, and builds a more loyal, engaged customer base.

What You Can Do: Invest in non-paid traffic sources like SEO, content marketing, and social media engagement. Cultivating a strong organic presence can significantly reduce acquisition costs and create a lasting brand reputation.

 

7. Revenue Peaks: Leveraging Seasonal Moments and Product Launches

Brands that know how to strategically create demand spikes enjoy revenue peaks that fuel growth without sustained ad spend increases. The best eCommerce brands achieve at least four revenue peaks per year, leveraging moments like seasonal launches, product drops, or special sales events. These peaks boost sales, acquire customers efficiently, and add excitement to the brand.

What You Can Do: Map out a yearly calendar of high-impact revenue events, aligning with holidays, product launches, or exclusive promotions. Create targeted campaigns that build anticipation and drive urgency to maximise these periods.

 

8. Large Total Addressable Market (TAM): Ensuring Scalability

A large total addressable market (TAM) allows brands to scale without quickly reaching audience saturation. Having a broad TAM means that there’s significant potential for growth, providing long-term scalability and reducing the risk of stagnation. Categories with wide appeal, like basics or wellness products, often benefit from this advantage.

What You Can Do: Analyse the size and characteristics of your target market. If your TAM is limited, consider expanding your product range or exploring adjacent demographics to widen your customer base and support future growth.

 

Combining These Traits to Build a Cash-Flowing Brand

A thriving eCommerce brand doesn’t need to have every one of these traits, but a successful combination of several can create a robust model for generating free cash flow and sustainable growth. Here are a few examples of how these traits might work together:

• A Health Supplement Brand: With high gross margins, favourable supplier terms, and a subscription model that boosts LTV, health supplements often operate with high cash flow potential. A large TAM and strong organic demand due to health trends create additional opportunities for sustained growth.
• An Influencer-Led Apparel Brand: Apparel brands that leverage an influencer’s organic audience can generate significant demand without excessive paid spend. Frequent product drops, high margins, and regular revenue peaks allow these brands to maintain high engagement and profitability.

 

Building a Cash-Flowing Brand: Key Takeaways

Creating a cash-flowing eCommerce brand requires intentionality and strategic focus. By prioritising attributes like high gross margins, lean OpEx, and first-order profitability, brands can create a foundation for sustained growth and resilience. Whether you’re just starting or looking to scale an established brand, these traits offer a blueprint for turning your business into a cash-flowing success story.

At Social Nucleus, we specialise in helping brands unlock their growth potential by implementing strategies that drive sustainable, profitable growth. If you’re ready to elevate your eCommerce brand, reach out to us today to learn how our expertise can help you achieve your goals.

In the competitive eCommerce market, growth isn’t just about seeing immediate returns on individual ads. It’s about long-term sustainability, being first-order profitable, and trusting the process for incremental gains. With a focus on backend metrics and consistent creative testing, brands can build a foundation for scalable, lasting success. Here, we’re sharing insights that have driven millions in revenue for our clients, crafted to help UK brands grow strategically.

 

1. Defining Key Metrics: Backend Blended Data for Sustainable, Incremental Growth

Tracking clicks and on-platform metrics only scratches the surface. For growth that’s genuinely profitable and sustainable, you need a clear view of backend blended data that shows causality, not just attribution. By focusing on first-order profitability, customer acquisition cost (CAC), and blended return on ad spend (ROAS) across all channels, you’ll be able to grow without chasing vanity metrics.

• Set Key Blended Metrics That Reflect Real Growth: Blended ROAS and CAC give you a clearer picture of profitability, accounting for all channels rather than attributing value to single touchpoints. One UK fashion client switched to a blended ROAS approach, which helped them double profitability by reallocating spend to channels with real incremental impact.
• Trust the Machine: Valuing Incremental Scale over Platform ROAS: Relying solely on in-platform ROAS is limiting; it may show immediate success but often skews toward retargeting or high-frequency ads, which don’t generate new customers. Instead, focus on incremental growth by observing your backend metrics and trusting the machine to scale based on true value. This shift in focus allows you to put spend where it actually drives first-time customers, not just recycled buyers.
• Prioritise First-Order Profitability: Achieving first-order profitability ensures that each new customer is acquired at a cost that immediately covers their purchase. This approach builds a stable foundation for growth, rather than relying on hopes of long-term retention or future purchases. Action Point: Set benchmarks for first-order profitability by analysing backend blended CAC, adjusting spend to ensure your acquisition cost aligns with immediate revenue.

Why This Works: Focusing on backend blended data and first-order profitability means you’re building a sustainable growth model that’s resilient, not reliant on short-term tactics.

 

2. High-Impact Creative Testing: Win by Testing Emotional Drivers at Scale

In the UK, where consumers are highly savvy and values-driven, brands must tap into emotional buying triggers and test them at scale. Success in eCommerce advertising isn’t about creating one perfect ad; it’s about testing volumes of creative to find the emotional drivers that resonate with your audience.

• Develop Campaigns with Emotional Drivers in Mind: Consumers buy for emotional reasons, so build campaigns that connect with these drivers. For example, a recent wellness client focused on themes of “self-care” and “rejuvenation,” which led to a 45% increase in engagement when emphasised in creative assets.
• Test Creatives at High Volume: The more creative variations you test, the better your chances of finding what resonates. For one fashion brand, rotating through a large volume of creatives that focused on themes of individuality and self-expression increased conversions by 60%.
• Customise and Refine for Platform Nuances: Tailoring creatives to the specific strengths of each platform maximises reach. Use fast, visually captivating ads for Instagram Stories, storytelling formats for Facebook carousel ads, and longer, dynamic videos for YouTube.

Pro Tip: Refresh your creatives every 2-3 weeks. UK audiences see thousands of ads daily, and a constant flow of new creatives will keep engagement high and avoid ad fatigue.

 

3. Scaling Intelligently: Prioritising Incremental Scale over Short-Term ROAS

Scaling in eCommerce isn’t about chasing the highest ROAS on individual ads; it’s about achieving incremental growth that’s sustainable. Incremental scale, built on backend blended metrics, ensures you’re growing with purpose and profitability in mind. Here’s how to build a scalable approach:

• Shift Your Focus from In-Platform ROAS to Incremental Scale: While platform ROAS can show short-term success, it often highlights low-hanging fruit—like retargeting or overly niche targeting—rather than bringing in new, high-value customers. Incremental growth means focusing on attracting new customers at scale, not just retargeting those already in your funnel. For a high-end apparel client, shifting focus from platform-specific ROAS to backend blended metrics provided a more accurate measure of profitability, helping them optimise budget for maximum first-order profit.
• Be Intentional with Spend Allocation: Spend should be distributed across channels based on backend performance, not platform ROAS alone. By testing incremental budget increases and adjusting based on blended ROAS, you can pinpoint high-ROI channels without being misled by platform attribution.
• Prioritise Channels that Drive First-Order Profit: Sustainable growth relies on ensuring every pound spent drives immediate profitability, not future promises. For one UK wellness client, this approach yielded a 30% increase in first-order profit by focusing spend on channels with high backend ROAS, rather than those with the highest platform-attributed conversions.

Insider Insight: Scaling isn’t about chasing short-term ROAS. Incremental scale, backed by backend metrics, is the only way to drive growth that’s both profitable and sustainable.

 

4. Cost Control Campaigns: Optimising Ad Spend with Strategic Cost Management

Rather than relying on automated rules for optimisation, cost control campaigns offer a robust approach to managing ad spend and maximising ROAS. This approach lets you control your cost structure, allowing for targeted growth without budget overruns.

• Structure Campaigns to Cap Costs by Objective: Cost control campaigns allow you to set hard limits on spend, ensuring that ads are only served when they align with your profitability targets. This approach allows for greater predictability in ad spend and keeps costs aligned with business goals.
• Use Cost Caps for First-Order Profitability Goals: Set cost caps that align with first-order profitability to ensure each sale covers acquisition costs. For a beauty client, implementing cost caps resulted in a 25% improvement in incremental profitability by ensuring each campaign drove sales at an acceptable acquisition cost.
• Control Bids Based on Backend ROAS Insights: Adjust bids to optimise campaigns based on backend ROAS rather than relying on platform automation. Monitoring backend data regularly allows you to make bid adjustments that keep campaigns aligned with your profitability targets.

Pro Tip: By implementing cost control campaigns, you gain more control over your budget and can ensure spend is directly linked to incremental growth, without relying on platform-specific automation.

 

5. Customer Experience: Ensuring Profitable Long-Term Relationships

Exceptional customer experience doesn’t just convert clicks into sales; it converts sales into lifelong relationships. Brands that build a seamless, value-driven customer journey are better positioned for profitable, sustainable growth. Here’s how to elevate your customer experience:

• Optimise for Mobile and Speed: With most UK eCommerce sales happening on mobile, a fast, seamless mobile experience is essential. A fashion client improved mobile load times, leading to a 20% conversion increase.
• Streamline Checkout to Minimise Abandoned Carts: A simpler checkout reduces cart abandonment and boosts conversion rates. For one client, reducing form fields and offering guest checkout increased conversions by 15%.
• Personalise Post-Purchase Engagement for Repeat Purchases: Keep customers engaged with tailored follow-up emails recommending complementary products or offering discounts for future orders.

Bonus Tip: A loyalty programme can further increase retention, building value over time and reducing the pressure on paid acquisition.

 

The Bottom Line: Building a Profitable, Sustainable Brand

In the eCommerce space, the path to success involves focusing on backend blended data, high-volume creative testing, incremental scaling, and an exceptional customer experience. These aren’t just theoretical strategies; they’re proven approaches that have driven sustainable growth for our clients.

If you’re ready to scale profitably and sustainably, reach out to us today. Our team of experts is here to help you implement these strategies and turn your brand into a long-term success story in the UK market.

For eCommerce brands, creative testing campaigns on Meta (Facebook and Instagram) have long been a staple for identifying high-performing ads. But here’s the reality: running separate creative testing campaigns often results in a massive waste of budget, time, and resources. At Social Nucleus, we believe there’s a more efficient way to optimise ad performance—one that lets Meta’s algorithm do the heavy lifting without sinking money into forced testing.

By rethinking your approach to ad testing, you can avoid burning cash on creatives that don’t deliver and instead rely on Meta’s advanced machine learning to point you toward winning ads quickly and cost-effectively. Here’s our approach to making your ad spend work harder and go further.

 

1. The Costly Trap of Traditional Creative Testing Campaigns

Creative testing campaigns might seem like an essential part of every eCommerce ad strategy, but they come with serious downsides. Forcing Meta to spend on unproven creatives in a separate testing environment often ends up wasting budget and delivering unreliable insights. You’re paying for Meta to spend on ads it would naturally deprioritise, leading to skewed data and reduced profitability.

Why Forcing Spend on Ads Doesn’t Work:

• Expensive with Minimal Return: Separate creative testing campaigns require a large budget allocation, especially when pushing spend to ads that might not perform. This approach often results in lost revenue, as it diverts budget from ads that could be scaled profitably.
• Unreliable Data: Ads that succeed in a testing campaign don’t always perform at scale. By the time you move these “winners” into real campaigns, Meta’s algorithm may suppress them or shift spend to other ads, revealing the flaws in forced testing.

The Solution: Instead of separate creative testing campaigns, allow Meta’s machine learning to indicate which ads are likely to succeed by monitoring natural spending patterns.

 

2. Rethink Ad Success: Why Meta’s Spending Patterns Are the Best Performance Indicator

Meta’s machine learning model doesn’t just look at clicks or purchases; it makes decisions based on a sophisticated understanding of user engagement. Using probabilistic forecasting, Meta’s algorithm relies on a range of early signals to predict ad performance, meaning it can quickly identify the most promising ads. In short, if Meta’s algorithm chooses to allocate budget to a creative, that’s usually the strongest indicator that the ad has real potential.

Why Meta’s Natural Spend Is the Ultimate Test:

• Data-Driven Decision-Making: Meta’s algorithm assesses each ad’s performance potential based on a range of signals, such as early engagement rates and past performance. By using probabilistic forecasting, Meta can quickly determine whether an ad is worth spending on, even without extensive testing data.
• Instant Feedback: When Meta spends on an ad, it’s a strong indication of quality. If it doesn’t spend, that’s often a sign that the ad isn’t likely to perform. Trusting this natural selection process saves you money and avoids the trap of trying to “force” an ad to perform.

What You Can Do: Monitor Meta’s spending patterns rather than running forced testing. Ads that receive spend in real campaigns indicate promise; those that don’t likely won’t improve with extra push.

 

3. Why Manual Bidding Outshines Traditional Testing

A more efficient alternative to creative testing campaigns is to eliminate them entirely and rely on manual bidding instead. Launching ads with manual bids allows Meta to allocate spend based on performance potential from the start. This way, you let Meta’s algorithm decide which ads deserve budget, reducing wasted spend on low-potential creatives.

Benefits of Using Manual Bids:

• Efficient Spend Allocation: Manual bidding enables Meta’s algorithm to naturally pick the best ads, directing spend only to those with high performance potential. You’re not wasting budget forcing ads to spend in an artificial testing environment.
• Faster Identification of Winners: By launching directly into real campaigns with manual bids, you allow Meta to quickly identify the ads that will deliver results, skipping the need for prolonged testing.

Actionable Step: Start new ads with manual bidding, and let Meta’s algorithm make spending decisions. Ads that receive budget under this approach are likely to perform well, allowing you to scale the best creatives with confidence.

 

4. The Power of Micro-Engagements: How Meta Predicts Ad Performance

One common misconception in ad testing is that Meta needs lots of clicks or conversions to determine an ad’s effectiveness. In reality, Meta’s machine learning model relies on micro-engagements—small signals of interest, such as scroll stops, short video views, and pauses—to assess ad quality. These micro-engagements offer early, valuable insights into how an ad might perform at scale.

Key Micro-Engagements Meta Tracks:

• Scroll Pauses: When users pause on your ad, Meta records this as a sign of potential interest.
• 3-Second Video Views: If viewers engage for the first few seconds, Meta interprets this as a positive indicator, often predicting higher engagement rates.
• Click-Through Rate (CTR): While not definitive, CTR helps Meta gauge user interest and optimise delivery based on early interactions.

What You Can Do: Recognise the importance of these micro-engagements as early indicators of ad quality. Meta uses them to predict success without needing extensive data, which can save you from spending excessively on traditional testing.

 

5. Avoid the “Statistical Significance” Trap

Many brands aim for statistical significance when testing ads, but on Meta, waiting for statistical significance is both costly and largely unnecessary. Meta’s algorithm uses a predictive model that doesn’t require thousands of clicks or purchases to make decisions, making traditional statistical methods impractical and expensive.

Why You Don’t Need Statistical Significance:

• High Cost, Low Reward: Achieving statistical significance for every ad test would require a large testing budget, which isn’t feasible for most eCommerce brands.
• Irrelevant in Meta’s Model: Meta’s machine learning doesn’t depend on statistical significance. Instead, it relies on probabilistic forecasting to predict an ad’s performance using smaller, faster insights.

The Solution: Don’t let “statistical significance” dictate your ad strategy. Meta’s algorithm is designed to make data-driven predictions without the need for large sample sizes, so you can avoid costly testing campaigns.

 

6. High Margins Give You Access to Higher Spend Tiers on Meta

One of the biggest advantages of strong gross margins is the ability to compete in higher spending tiers on Meta, which can significantly accelerate growth. Brands with higher margins can afford to increase their ad spend, reaching a broader audience and scaling faster than competitors with thinner margins.

How High Margins Enable Faster Growth on Meta:

• Greater Reach Potential: Higher margins allow brands to confidently spend more, reaching larger audiences and boosting brand awareness.
• Competitive Advantage in Bidding: Strong margins enable more aggressive bidding strategies, which means your ads can secure better placements even in competitive markets.
• Sustained Profitability: As acquisition costs increase, brands with high margins can maintain profitability even at higher spending levels, making scaling more sustainable.

Actionable Step: Focus on improving your margins so you can confidently scale ad spend. By creating room for growth through strong margins, you’ll have a competitive edge on Meta, where ad costs continue to rise.

 

Final Thoughts: Trust Meta’s Algorithm to Optimise Your Ad Spend

Ultimately, Meta’s algorithm is built to help you achieve efficient ad performance by leveraging its vast data and machine learning capabilities. By moving away from traditional creative testing campaigns and embracing a strategy that lets Meta’s algorithm guide your ad spend, you can achieve higher efficiency, conserve budget, and focus on scaling what truly works.

Key Takeaways:

1. Skip Separate Creative Testing Campaigns: Allow Meta’s algorithm to dictate which ads perform by monitoring spend in real campaigns.
2. Value Micro-Engagements as Early Signals: Small interactions like video views and scroll pauses can provide valuable insights into ad potential.
3. Avoid Costly Statistical Significance Requirements: Meta’s probabilistic forecasting model eliminates the need for large data samples to pick winning ads.
4. Leverage High Margins to Compete in Higher Spend Tiers: Strong margins give you the flexibility to bid more aggressively and accelerate growth.

At Social Nucleus, we’re experts in making Meta’s algorithm work for your brand. Get in touch with us today to learn how we can help you streamline your ad strategy, eliminate budget waste, and scale your business sustainably.

In the fast-paced world of digital advertising, controlling costs while driving conversions is a balancing act. Meta’s bid caps and cost caps are invaluable tools for advertisers who want to optimise their campaigns, ensuring efficient spend without sacrificing performance. The key to their success lies in Meta’s advanced forecasting system, which uses predictive data to guide ad performance. This white paper delves into how Meta’s forecasting powers these caps, making them work more effectively for advertisers.

Meta’s Forecasting: The Foundation of Bid Caps & Cost Caps

At the heart of Meta’s predictive power are two core metrics:

• Expected Click-Through Rate (eCTR): Meta uses early user engagement data (clicks) to predict how often an ad will be clicked. Since clicks happen frequently and early in a campaign, Meta quickly develops a reliable eCTR.
• Expected Conversion Rate (eCVR): The second, more nuanced metric, eCVR, is Meta’s prediction of how many users who click an ad will go on to convert. Because conversions require more data than clicks, Meta’s eCVR predictions take longer to refine but are crucial for estimating the eventual Cost Per Acquisition (CPA).

Formula:
Amount Spent * eCVR = Expected CPA
This simple formula drives both cost and bid caps, allowing Meta to determine how much it should bid to ensure profitability within your target CPA.

Deep Dive into eCVR: The Key to Optimising Performance

eCVR (Expected Conversion Rate) is a fundamental piece of Meta’s forecasting model. While eCTR can be quickly assessed through clicks, eCVR requires a more detailed analysis of post- click behaviour—how likely users are to make a purchase or complete a goal once they engage with your ad.

• How it works: Meta needs a critical volume of clicks to predict conversion likelihood. It uses data from interactions such as time spent on the landing page, adding products to a cart, or completing forms to gauge the likelihood of conversion.
• Impact on CPA: A lower eCVR can lead to higher CPA predictions, causing Meta to adjust bids downward to avoid inefficiencies. Conversely, a higher eCVR means Meta will confidently bid more, knowing that the chances of conversion are higher.

Why eCVR is crucial for advertisers:

If your conversion rate prediction is off, it can significantly affect the performance and profitability of your campaign. Therefore, optimising for both click-through rate and post-click conversion is critical to success. If an ad performs well in engagement but falls short in driving sales, Meta will adjust bids to prevent overspending.

The Role of Engagement Signals in Meta’s Forecasting

Meta’s ability to predict the success of your ad is powered by a vast dataset of engagement signals. These signals include user interactions like clicks, shares, comments, and video views. Meta draws on its extensive historical data, also known as priors, to predict how your current engagement will lead to conversions.

Examples of signals that fuel forecasts:

• If an ad receives a high number of shares, Meta knows this is a strong sign of purchase intent.
• If users watch a video ad for a long time, Meta recognises the high interest and increases bids accordingly.

By combining real-time user engagement with its enormous backlog of data, Meta’s forecasting becomes highly accurate, ensuring your ads are positioned to succeed without unnecessary overspending.

Ad Set-Level Metrics and Adjustments

• Meta’s forecasts are dynamic. As your ad gathers more data, the system continuously updates predictions at the ad set level. If the actual conversion rate deviates significantly from the expected eCVR, Meta adapts its bidding strategy to reflect this new data.

For example:

• If an ad gains lots of clicks but fails to convert, Meta will eventually throttle back bids to avoid wasting budget. This prevents campaigns from spending inefficiently on engagements that don’t lead to sales.

Why this matters:

Dynamic forecasting ensures that even if an ad overperforms or underperforms in the early stages, Meta’s system will gradually adjust its bidding strategy to optimise for better efficiency and profitability over time.

How Meta’s Forecasting Makes Bid & Cost Caps Effective

Meta’s forecasting system allows cost caps and bid caps to function seamlessly by ensuring that each dollar spent aligns with expected performance. These caps provide critical cost control while Meta’s system continually fine-tunes bidding decisions based on predicted outcomes.

Here’s how this benefits advertisers:

1. Maximising Efficiency: Meta predicts where bids will drive conversions, ensuring that you’re not wasting budget on ineffective placements.

2. Cost Control: Forecasts allow Meta to maintain your CPA target within budget by continuously adjusting bid amounts.

3. Scaling Confidence: Accurate forecasts enable you to scale campaigns without fear of overspending, knowing Meta’s system is constantly working to optimise costs and bids.

Conclusion: Unlocking the Power of Meta’s Forecasting

Meta’s forecasting system is the engine behind the success of bid caps and cost caps. By leveraging real-time engagement data and vast historical insights, Meta helps advertisers maintain control over their spending while optimising for the highest possible performance. Whether you’re scaling a campaign or tightening cost control, understanding how Meta’s forecasting works is essential for getting the most out of your ad spend.

If you’re ready to explore how Meta’s forecasting can help you drive efficient growth, reach out for a consultation. We can help you harness these powerful tools to optimise your ad campaigns for profitability and scale.

This white paper breaks down how Meta’s bid caps and cost caps work, emphasising the role of forecasting and the impact of metrics like eCVR on ad performance. It serves as a comprehensive guide for advertisers looking to understand and leverage Meta’s advanced ad system for more effective campaigns.

As a savvy marketer, you understand that maximising returns while controlling costs is critical to running successful Meta (Facebook and Instagram) ad campaigns. One of the most effective tools in your arsenal for achieving this balance is cost controls, which include mechanisms like bid caps and cost caps. These tools allow you to manage your ad spend more strategically, ensuring you stay within budget while optimising performance.

In this white paper, we’ll explore why cost controls are essential for your Meta ad campaigns, how they help optimise budget allocation, and when and how to use them effectively.

What Are Cost Controls?

Cost controls in Meta advertising refer to settings that enable you to limit the amount Meta can bid for each impression or regulate the overall cost per action (CPA) in your campaigns. The two primary types of cost controls are:

• Bid Caps: These set a maximum bid for your ads, ensuring Meta doesn’t bid more than the specified amount in auctions for impressions.
• Cost Caps: These allow you to set a target cost per action (CPA), like a purchase or lead, and Meta’s algorithm adjusts bids to keep the average cost within your desired range.

By using these tools, you gain better control over your ad spend, allowing for flexibility in how you scale and manage your campaigns.

Why Cost Controls Matter

Meta’s advertising platform operates on a real-time auction system, where the highest bidder typically secures the best ad placements. Without cost controls, you leave Meta in charge of how much to bid on each impression, potentially leading to overspending. Cost controls provide the following key benefits:

1. Optimising Return on Ad Spend (ROAS)

Using cost caps ensures that Meta’s algorithm adjusts your bids to keep acquisition costs within a specific range. This enables you to optimise for the best Return on Ad Spend (ROAS) by maintaining acquisition costs that don’t erode profitability, ultimately maximising your profits.

2. Improved Budget Allocation

Cost controls allow for more efficient allocation of your ad budget. For example, a bid cap ensures that Meta doesn’t overspend on impressions that don’t align with your profitability goals. This means your budget is spent on impressions that are more likely to convert at a price point that works for your business.

Example:

If you set a bid cap of £50, Meta’s algorithm will never bid more than that for an impression. This ensures that your ads are served only when they have a strong chance of driving a conversion at your desired cost. Without this control, you could pay £60 or more for the same impression, reducing your overall effectiveness.

3. Controlling Customer Acquisition Cost (CAC)

With cost caps, you can directly control your Customer Acquisition Cost (CAC) by setting a maximum amount you’re willing to spend to acquire a new customer. This prevents overspending on impressions that don’t align with your CAC targets.

Example:

If your target CAC is £70, Meta’s algorithm might go beyond that without a cost cap, leading to unsustainable acquisition costs. By setting a cost cap, you ensure that Meta’s bids stay within a range that keeps your campaigns profitable.

4. Increasing upside potential and minimising downside risk

• Advertising is an auction, and we’re selling to real people. Performance fluctuates depending on real time events, and many different factors. By utilising cost controls, we can massively increase the upside potential of our campaigns, by setting an extremely high campaign budget, on good days in the market Meta will spend the entire budget at our target CAC. On the flip side, on quieter days in the market, public holidays/busy times etc, Meta will simply pull back spend, and remain efficient. Using highest volume/lowest cost, Meta will spend the entire daily budget on bad days giving us hugely unprofitable days. 1 inefficient day can wildly change our P&L for the whole month.

When and How to Use Cost Controls

While cost controls can provide excellent results, they must be used strategically to unlock their full potential. Below are some best practices for implementing cost controls effectively:

1. Scaling Campaigns

When scaling a campaign, controlling acquisition costs becomes critical. Cost caps help you increase your budget without disproportionately inflating your costs, ensuring that scaling doesn’t come at the expense of profitability.

2. Long-Term Profitability

For brands focused on sustainable growth, keeping acquisition costs under control is essential. Cost caps allow you to maintain profitability over time by ensuring that conversion costs stay within a manageable range.

3. Combining Bid Caps and Cost Caps

One challenge marketers face is balancing auto-bid campaigns with cost-controlled campaigns. If most of your budget is allocated to auto-bid campaigns, your bid cap campaigns may not receive enough spend. To avoid this, allocate at least 50% of your budget to campaigns using cost controls, ensuring they receive sufficient delivery.

Example:

If your total ad budget is £10,000, ensure that at least £5,000 is dedicated to campaigns with bid or cost caps. This allows Meta’s system to optimise your spend for lower-cost impressions, giving you better results for your money.

Pitfalls to Avoid with Cost Controls

While cost controls are highly effective, they can become counterproductive if not used carefully. Here are some common pitfalls to avoid:

1. Setting Caps Too Low

If your bid caps or cost caps are set too low, your ads may not get enough visibility, leading to limited impressions and poor delivery. Ensure your caps are realistic, aligned with historical performance data and industry benchmarks.

2. Over-relying on Caps During High-Competition Periods

In periods of high competition, such as during major sales events or holidays, strict cost controls may prevent you from competing effectively. Consider adjusting your caps during these periods to ensure you can still win prime placements while maintaining overall profitability.

3. Giving in too early

The most common mistake people make when using cost controls is giving in too early, a few days of £0 spend and moving back to lowest cost campaigns. We have to play around with the cap for some time before we find the sweet spot of profitable spend.

Conclusion: The Strategic Power of Cost Controls

Cost controls are a vital tool for ensuring your Meta ad campaigns run efficiently while driving profitable results. Whether you’re scaling your campaigns, optimising ROAS, or controlling CAC, cost controls allow you to manage your budget effectively and maximise the impact of your ad spend.

If you’d like to explore how to implement cost controls for your Meta campaigns or need help optimising your ad strategy, we’re here to assist. Get in touch, and we’ll work with you to drive sustainable growth and maximise your marketing budget.

At Social Nucleus, we are dedicated to helping direct-to-consumer (DTC) ecommerce brands scale in a sustainable and profitable way. Through optimisation models, we have successfully driven incremental growth for our clients, particularly through Meta’s advertising platforms. In this edition of our newsletter, we will dive into why we prioritise the 7-day click optimisation window, avoid using the 7-day click and 1-day view model, and in which situations a 1-day click optimisation might be appropriate. Our goal is simple: ensure every click adds measurable value.

The Importance of Incremental Growth in DTC Advertising

For ecommerce brands, the objective is clear: growth. But sustainable, long-term growth depends on incremental improvements, small but consistent gains that accumulate into significant results over time. Incremental growth ensures that every advertising effort contributes measurably to business objectives, making scaling both profitable and
sustainable.

Choosing the right optimisation model is central to achieving this. At Social Nucleus, we primarily use the 7-day click window to focus on high-intent, incremental actions. In specific cases, we may consider a 1-day click optimisation model. Let’s explore why these choices matter.

Why We Use 7-Day Click as Our Primary Optimisation Model

The 7-day click optimisation model focuses on high-intent actions that reflect consumers who are more likely to convert. Here’s why it is the best fit for scaling DTC brands:

1. Aligns with Consumer Behaviour

The 7-day click window is well-suited to typical ecommerce buying behaviour, where consumers often take time to consider their options before making a purchase. Whether comparing prices or considering alternatives, many customers don’t make immediate decisions. Allowing for up to seven days from the initial click captures higher-intent buyers who may take longer to convert but are still valuable.

2. Greater Control Over Incrementality

The 7-day click model provides clearer attribution, focusing on actions that reflect meaningful engagement. By only tracking clicks, we can measure the incremental impact of our efforts. This ensures that conversions are more likely driven by advertising and not merely passive views, giving us confidence that our spend is contributing to growth.

3. Cleaner, Actionable Data

With the 7-day click model, we avoid passive data points like view-through conversions. The data we gather is cleaner and more actionable, allowing us to optimise for high-quality clicks that drive revenue. By focusing on active engagement, we ensure that Meta’s machine learning algorithms optimise for users who are likely to convert, improving long-term campaign performance.

When using the view window, this skews ad account data to create cases like capo/ other big spenders where the ad account ROAS is better then blended ROAS which creates confusion. Using click only attribution ensures that the ad account aligns more with backend numbers.

Why We Avoid the 7-Day Click and 1-Day View Model

The 7-day click and 1-day view model can be tempting because it tracks both clicks and views, but it comes with notable drawbacks:

1. Lower Incremental Value

This model includes passive views, which can dilute the incremental value of each conversion. Users who simply see an ad may not be actively considering a purchase. By including these passive engagements, the model makes it harder to accurately assess the real value your ads provide.

2. Overestimation of Impact

The 1-day view component can inflate your performance metrics, as conversions might be falsely attributed to users who saw an ad but were already planning to purchase. This leads to an overestimation of impact, making it harder to optimise for true, measurable growth.

3. Focus on Intent

We prioritise high-intent clicks over passive views. While awareness is important, it’s not the goal when optimising for conversion-driven campaigns. We want every click to reflect a strong likelihood of conversion, and the 7-day click and 1-day view model doesn’t align with this goal.

Not only does the 1 day view window mislead us as media buyers, but it also makes the machine lazy and directs it to start optimising for view through conversions, rather than meaningful click conversions.

When 1-Day Click Might Be Appropriate

Though we typically rely on the 7-day click model, there are instances where a 1-day click optimisation window might make sense. This model is suitable when purchase decisions are made quickly, such as with low-cost or impulse-buy products.

Use Cases for 1-Day Click:

• Impulse Purchases: If your brand sells items like fashion accessories or consumables, consumers are more likely to make a decision immediately after seeing an ad. A 1-day click model captures these rapid conversions and ensures we’re optimising for quick, intent-driven actions.
• Frequent Purchases: For brands with products that have a shorter purchase cycle or lower price point, the 1-day click model may help focus on immediate buyers rather than those who need more time to consider their options.

However, for higher-cost products or those with longer consideration periods, the 7-day click model is generally more appropriate. It allows for a fuller picture of customer intent and behaviour, ensuring that we capture those conversions that take a few days to finalise.

Making the Machine Work Hard for Every Click

At the heart of our optimisation strategy is the belief that every click should count. We strive to make the machine learning algorithms that power advertising platforms “work hard” to ensure each click reflects high-intent behaviour. By focusing on models like 7-day click, we maximise efficiency, ensuring that ad spend is tied to incremental growth.

When Meta’s algorithms work harder to identify and target high-value users, we reduce waste on low-intent clicks and passive impressions. This leads to more effective ad spend, higher ROI, and sustainable growth for your brand.

Final Thoughts

As your ecommerce advertising partner, our mission is to align our strategies with your business’s growth goals. By selecting the right optimisation models and focusing on incrementality, we help your brand scale without wasting spend on low-value clicks or passive
engagements. While the 7-day click window is our primary approach, we’re flexible and adjust strategies to meet the unique needs of each client.

It’s not about casting the widest net but about focusing on quality, ensuring that every click we drive contributes to incremental growth and profitability.

Here’s to your continued success and growth. As always, we’re here to optimise, scale, and help you thrive.

In today’s highly competitive ecommerce landscape, your website is one of the most critical tools for turning visitors into paying customers. Optimising your site for conversions not only boosts revenue but also improves the return on your marketing investments. This white paper outlines essential best practices that will help enhance your website’s ability to convert traffic into sales.

1. Simplify Navigation and Structure

A well-organised, easy-to-navigate website is crucial for creating a seamless user experience. Reducing friction for users leads to higher conversion rates.

• Surface Primary Links: Ensure key product categories and links are easily accessible in your navigation bar. Avoid the use of hamburger menus on desktop versions to make navigation more intuitive.
• Use Solid Colours for Navigation: A solid colour background for your navigation bar helps improve contrast and visibility, particularly on mobile devices, where clarity is essential.

A clean, straightforward structure ensures visitors can quickly find what they’re looking for, improving the likelihood of converting them into customers.

2. Optimise Product Pages for Conversions

Your product pages are the heart of your website when it comes to driving sales. A well-optimised product page encourages users to take action and add items to their cart.

• High-Quality Images: Use detailed product images, showcasing multiple angles and user-generated content (UGC) where possible to build trust.
• Social Proof: Display product reviews, “Best Seller” badges, and ratings prominently under product titles to enhance credibility and trustworthiness.
• Clear CTAs: Make “Add to Cart” buttons large, highly visible, and in vibrant colours to catch the user’s attention and prompt action.

By presenting products in an appealing and transparent way, you can better guide visitors towards making a purchase.

3. Leverage Incentives and Urgency

Creating urgency and offering incentives are proven methods to encourage quicker buying decisions.

• Free Shipping Progress Bars: Include a progress bar that shows customers how close they are to qualifying for free shipping, motivating them to add more items to their cart.
• Low Stock Alerts: Show low stock alerts to create a sense of urgency, encouraging users to complete their purchases before the product sells out.

Tactics like these help reduce hesitation and speed up the customer decision-making process.

4. Streamline the Checkout Process

Cart abandonment is a common challenge for ecommerce sites, often caused by complex or confusing checkout processes. Simplifying checkout can dramatically improve your conversion rates.

• Security Icons: Display security icons, such as a lock symbol, near the “Checkout” button to reassure users that their payment information is safe.
• Streamline Payment Options: Offer a few clear and simple payment options rather than overwhelming users with too many choices, which can lead to decision paralysis.

A faster, more straightforward checkout process minimises drop-offs and increases the likelihood of completed purchases.

5. Harness User-Generated Content (UGC)

People trust other customers’ experiences more than polished marketing materials. Leveraging UGC helps build credibility and trust.

• Swipeable UGC Carousels: Display user-generated content in swipeable carousels, featuring customer photos and usernames to highlight authenticity.
• Product Reviews with Photos: Combine customer reviews with real-life photos of your products being used to demonstrate their value in a practical context.

UGC provides social proof, which is a powerful tool in nudging potential customers towards a purchase.

6. Improve Technical Performance for Faster Loading Times

Website speed is critical for both user experience and SEO performance. A slow site can frustrate visitors and lead to higher bounce rates, affecting conversions.

• Optimise Image Sizes: Compress large image files and use the correct formats to ensure faster page loading times without sacrificing image quality.
• Minify HTML, CSS, and JavaScript: Reducing the size of these files helps improve load speed and site performance, which is crucial for keeping visitors engaged.

Fast, responsive websites lead to better user satisfaction, higher engagement, and increased conversions.

7. Test and Iterate for Continuous Improvement

To ensure your site is continually optimised for conversions, regular testing and analysis are essential.

• A/B Testing: Run A/B tests on key elements such as product pages, navigation, and checkout processes to identify which designs and layouts convert better.
• Monitor Site Metrics: Keep a close eye on metrics like bounce rates, conversion rates, and average time spent on the site to identify areas for improvement and adjust your strategies accordingly.

Testing allows you to continuously refine and optimise your website based on data-driven insights.

Conclusion

Optimising your ecommerce website for conversions requires a focus on both user experience and technical performance. By following the best practices outlined above, you can transform your site into a more effective sales tool, turning visitors into loyal customers and maximising the return on your marketing investments.

If you have any questions about implementing these strategies or would like to discuss how to optimise your website for better conversions, don’t hesitate to reach out.