If you want a deep understanding of how the Meta Algorithm really works… This guide is for you.
Modern advertising runs on algorithms that learn from data and predict outcomes probabilistically. Nowhere is this more evident than in Meta’s advertising platform, which heavily leverages probabilistic machine learning and Bayesian forecasting to deliver ads.
For e-commerce brands, this means Meta’s system is constantly adjusting bids, pacing budgets, personalising delivery, and choosing creatives based on statistical predictions of what will drive sales. In this playbook, we’ll unpack how Meta’s ad algorithm works under the hood – in relatively accessible terms – and compare it to Google Ads’ approach for e-commerce optimisation. You’ll also learn why trying to “outsmart” these algorithms is usually a futile exercise, and how to instead align your marketing strategy with the machine for better results.
1. How Meta’s Ad Algorithm Leverages Probabilistic Learning
Meta’s advertising algorithm is fundamentally built on predicting probabilities. Rather than using fixed rules or simple heuristics, it employs large-scale machine learning models to estimate the likelihood that a given user will take a desired action (click, add to cart, purchase, etc.) upon seeing an ad.
These models are probabilistic – they output a probability or expected value for each possible ad impression. Meta uses techniques akin to Bayesian forecasting, which means the system starts with prior assumptions and continuously updates its predictions as new data arrives (impressions, clicks, conversions). In essence, the algorithm is always asking:
“Given what we’ve seen so far, what is the probability user X will convert if we show ad Y now?”
And it adjusts campaign delivery based on those evolving probabilities.
- Bayesian Update of Beliefs: Meta’s system begins with initial beliefs (“priors”) about performance and refines them as results come in. For example, if you launch multiple new ads with no history, the platform initially treats them roughly equally. As soon as data starts coming in, the algorithm updates its beliefs about which ad is better, favoring the one with higher observed conversion rates. This Bayesian-style paradigm means past performance informs future predictions – the more quickly a pattern emerges, the faster the system shifts spend toward the likely winner.
- Probabilistic, Not Deterministic: Importantly, these decisions aren’t absolute. Meta’s algorithm doesn’t guarantee a certain ad will always win – it works in probabilities. Think of it like a digital “scientist” running constant experiments: it might occasionally still show a lower-performing ad to some users (exploration) to verify if conditions have changed, but most of the time it will exploit the current best prediction. This balance of exploration vs. exploitation is a hallmark of probabilistic machine learning. It helps the system avoid tunnel vision and continuously test alternatives, which is crucial in e-commerce where creative fatigue and shifting consumer behavior can change which ad is most effective.
Analogy You can imagine Meta’s algorithm as a seasoned casino manager running a multi-armed slot machine test. Each “arm” is an ad in your ad set. Initially, the manager gives each slot machine a fair chance (not knowing which might pay out). Very quickly, she observes one machine paying out more (one ad driving more conversions) – so she reassigns more players to that machine. She’ll still occasionally send a player to the other machines to see if something’s changed, but overall most people get directed to the “luckiest” machine. Over time, she’s effectively running a Bayesian experiment to maximise total winnings.
2. Impacts on Key Aspects of Ad Delivery
Meta’s probabilistic, Bayesian-driven approach manifests in four key areas of ad delivery that are especially relevant for e-commerce marketers: user-level personalisation, creative delivery optimisation, auction bidding, and budget pacing. Let’s break down each:
2.1 User-Level Personalisation
One of Meta’s biggest strengths is its ability to personalise which ad is shown to which user. Under the hood, Meta’s models consider thousands of signals about each user and context to decide the best ad to serve. These signals include a person’s on-platform behavior (pages followed, past ad clicks, content viewed), off-platform activity (Facebook Pixel or Conversions API data like products viewed on your website), demographic info, device type, time of day, and more. The algorithm crunches all this data to predict the probability that the user will take the advertiser’s desired action, such as making a purchase.
What this means in practice is highly granular personalisation. Every impression is auctioned with user-level relevance in mind. If you’re an e-commerce brand selling shoes and a particular user has shown interest in similar products recently, the system will recognise that signal and is more likely to show your ad to that user (assuming your campaign objective is, say, conversions). Conversely, if another user historically never engages with apparel ads, Meta’s model will de-prioritise showing your shoe ad to them due to a low predicted conversion probability. This all happens automatically via machine learning – advertisers no longer have to manually segment every tiny audience, because the algorithm does it in real-time.
Furthermore, Meta has moved toward advanced modeling techniques like sequence learning to improve personalisation. Traditionally, their recommendation models (DLRM – Deep Learning Recommendation Models) used many hand-engineered features about user behavior. Now, newer approaches ingest the sequence of a person’s actions to glean patterns (similar to how a Netflix recommendation might consider). For marketers, the takeaway is that Meta’s personalisation isn’t static or simplistic – it’s a dynamic, ever-learning system that “reads” user behavior sequences to infer intent, delivering ads for products a person is most likely to care about at that moment.
Why this matters for e-commerce: If you’re targeting broad audiences (which Meta encourages), the algorithm will find the pockets of users within that broad pool who are most likely to convert. A middle-aged man and a college student might both be in your broad audience, but they won’t be served the same products or creatives. The system might show the man a premium leather loafer and the student a trendy sneaker, if past data suggests those are the most relevant for each. This user-level matching is powered by probabilistic predictions – essentially micro-forecasts for each user – that maximise the chance of conversion for every single impression.
Why this matters for e-commerce: If you’re targeting broad audiences (which Meta encourages), the algorithm will find the pockets of users within that broad pool who are most likely to convert. A middle-aged man and a college student might both be in your broad audience, but they won’t be served the same products or creatives. The system might show the man a premium leather loafer and the student a trendy sneaker, if past data suggests those are the most relevant for each. This user-level matching is powered by probabilistic predictions – essentially micro-forecasts for each user – that maximise the chance of conversion for every single impression.
2.2 Creative Delivery Optimisation
Have you ever noticed that out of a batch of ads in a Meta campaign, one or two ads quickly get the majority of the spend? This is creative optimisation in action – and it’s driven by the same Bayesian logic. Meta’s algorithm treats each creative as an “option” and initially distributes impressions relatively evenly when all ads are new. Very soon, it measures which ads are getting more clicks or conversions and starts shifting impressions toward the better performers. It’s essentially running a multi-armed bandit test on your creatives. Instead of waiting for a full manual A/B test to conclude, the system continuously updates its beliefs about each ad’s conversion rate and allocates budget accordingly to maximise total conversions or revenue.
From a Bayesian perspective, each ad has a “prior” – an initial assumption of performance – that gets refined with each new impression outcome. Let’s say Ad A got two sales out of the first 50 impressions and Ad B got zero. The algorithm sees Ad A is statistically more likely to drive a sale, so it will start favoring Ad A with more impressions. Ad B isn’t necessarily dead; the system might still show it occasionally (especially to user segments where it might do better, e.g. maybe Ad B’s style appeals to a different demographic) to gather more evidence. But unless Ad B starts catching up in performance, it will continue to get sidelined. The result: a small number of ads get most of the spend – and that’s by design. Meta has learned that this uneven allocation actually yields better overall results than equal rotation, because pushing spend to the predicted winners drives more conversions in aggregate.
For e-commerce brands, this underscores the importance of feeding the algorithm good creative options. The system will rapidly figure out which product image or video resonates best. If you use Meta’s Dynamic Creative or Advantage+ catalog ads, the algorithm even takes on the job of mixing and matching creative elements (image, text, call-to-action) to find top-performing combinations for each user segment – again using probabilistic experimentation. Every creative variation is essentially a hypothesis, and Meta’s AI is constantly testing those hypotheses. This is far more efficient than manual testing, especially post-iOS14 when individual-level tracking is harder and the algorithm must do more of the heavy lifting with aggregated signals.
Pro Tip: Don’t fight the algorithm by forcing equal spend on each creative (for example, by putting each ad in a separate ad set with its own budget). This often leads to worse results, because you’re removing the algorithm’s ability to allocate spend to the eventual winner. It’s usually better to let several creatives start in one ad set and allow Meta’s probabilistic engine to work out the winner. If you need to ensure a fair test (for instance, for internal learning), use Meta’s built-in split test tool – otherwise, trust that the algorithm’s always-on Bayesian test will find the best creative for the objective.
2.3 Auction Bidding and Value Prediction
Every ad impression on Meta is awarded via an auction. However, it’s not as simple as highest bid wins – Meta wants to balance advertiser results with user experience. To do this, it calculates a “Total Value” score for each active ad competing for an impression. This score is a combination of your bid, the platform’s estimated action rate for that impression, and a user value/quality component. In formula form (simplified):
Total Value = Advertiser Bid × Estimated Action Rate + User Value.
Illustration: Meta’s auction formula combines your bid, the estimated action rate (how likely the user is to take your desired action), and user value (a quality/relevance metric) to determine which ad wins the impression. The ad with the highest total value wins the auction and is shown to the user. In practice, this means an advertiser with a lower monetary bid can win if their ad is much more likely to get a positive result from that user or if their ad is higher quality. Meta’s machine learning models predict each person’s likelihood of taking the advertiser’s desired action (e.g., making a purchase) based on a huge range of factors about the person and the context. This predicted probability is the Estimated Action Rate. The model considers things like “Has this person shown interest in similar products or ads before? Is the ad creative itself compelling to similar users? What time of day is it? What device are they on?” – all of these are signals that feed into the prediction.
From a probabilistic standpoint, you can see the Bayesian logic here as well. The system has prior knowledge (maybe this user has a history of buying fitness products, so a fitness apparel ad has a higher baseline probability) and real-time signals (the ad content, the current context) that combine into a probability of conversion. If your ad’s predicted conversion chance for that user is, say, 5%, and you bid $10 for a conversion, your effective value is $0.50. Another advertiser might bid $5 but have a 20% predicted conversion chance (perhaps the ad is extremely relevant to that user), giving an effective value of $1.00 – that advertiser would win the auction despite a lower bid. This is essentially a form of stochastic optimisation where Meta tries to maximise the total value (which correlates with meaningful outcomes).
For e-commerce campaigns using conversion objectives, the “bid” is often implicitly your target cost per action or value. If you use strategies like Lowest Cost (no explicit bid cap), Meta’s algorithm will try to get the most conversions for your budget, effectively entering auctions where your predicted conversion value exceeds the cost. If you use a Cost Cap or Target ROAS, the system will be more selective, only bidding in auctions likely to meet those constraints. In all cases, the heavy lifting of predicting conversion value in each auction is done by ML models, not by static rules. Meta even updates these predictions continuously as it learns more – for example, if the model’s priors expected a 3% conversion rate but early results show 1%, the bid calculations will adjust dynamically. Pacing (next section) further moderates this by considering how much budget is left and time remaining.
The User Value part in the formula is Meta’s way of ensuring ads don’t win purely on bid and probability if they might degrade user experience. This includes quality metrics like negative feedback (hides, reports), positive interactions, and landing page experience. It’s another probabilistic estimate – essentially a penalty or boost factor. As an e-commerce advertiser, you mostly control this by making relevant, non-spammy creative and having a fast, pleasant website. A high predicted conversion rate won’t help if your ad triggers a poor user value score; the auction might favor a competitor with slightly lower predicted conversion but a better user experience. The takeaway is that Meta’s auction is a sophisticated balance of bid and predicted relevance, all powered by probabilistic models evaluating numerous signals simultaneously.
2.4 Budget Pacing and Delivery Over Time
Budget pacing is how Meta’s system spreads your ad spend throughout your campaign’s duration (day or lifetime) to maximise results. If the algorithm spent your entire daily budget in the first hour of the day, you might miss better opportunities later. Conversely, if it spends too slowly, you might undershoot your potential conversions. Meta uses forecasting to pace budgets optimally. This involves predicting how many opportunities are likely to come later in the day or campaign and adjusting delivery speed in real-time.
The pacing mechanism can be thought of as a feedback control system that uses probabilistic input (expected value of future auctions). Meta has even patented techniques for pacing that likely involve continuously estimating the probability of getting desired actions in upcoming auctions and modulating spend accordingly. For example, one patent describes learning a model offline that predicts the chance of a click or conversion for each ad impression, then grouping and scheduling ads to meet both performance and budget constraints. In simpler terms, the algorithm might determine in the morning that conversions are usually cheaper in the evening (perhaps more users convert after work for your product), so it will hold back some budget for later. If by midday it sees conversion volume is lower than expected, it may loosen the reins to spend a bit faster (to avoid under-delivery). This is all automatic and continually updated as the day progresses.
Meta’s own documentation explains that pacing helps account for auction variability so you can meet your cost goals even when market conditions change. There are actually two levels of pacing: budget pacing (spending the budget evenly over time) and bid pacing (adjusting bids up or down from the average to hit cost targets). Bid pacing means if the algorithm notices you’re tracking below your cost per acquisition (CPA) goal (maybe you’re getting cheaper conversions than expected), it might increase bids to capture more conversions until costs align with the target. Conversely, if costs are coming in high, it will bid down or hold back to stay on target. This is an inherently Bayesian move – it’s updating its prior plan based on observed data in real time.
For e-commerce advertisers, effective pacing is crucial during things like sales events or seasonal swings. Meta’s system will automatically pace faster when it sees conversions flowing (say, during Black Friday it might spend budgets earlier in the day if midnight shopping surges show lots of cheap conversions) and slow down if conversions wane.
The key point is you don’t have to manually throttle or release budget; the algorithm’s probabilistic forecasting handles it. However, providing it the correct signals (for example, using a Lifetime budget with an end date if you want it to consider the whole campaign window, or setting a realistic daily budget that matches how much you can spend effectively) will help the pacing system make better decisions. If you set a very low budget and your bid strategy is target-based, the system might become too constrained and not learn properly. On the other hand, a reasonable budget with the flexibility of pacing will let the algorithm find the sweet spots throughout the day to get the best bang for your buck.
3. The Role of “Signals” and “Priors” in Decision-Making
Two words often thrown around with these algorithms are “signals” and “priors.” Let’s clarify what they mean in Meta’s advertising context, and how they contribute to the algorithm’s effectiveness.
- Signals: These are the data points or inputs that inform the algorithm’s predictions. Meta’s models take into account a rich tapestry of signals about users, ads, and context. As mentioned, signals range from user demographics and interests to real-time contextual info like device type or time of day. An important category for e-commerce is conversion signals – e.g. the pixel events on your site. Meta’s algorithm learns from events like Add to Cart, Purchase, ViewContent, etc. The stronger and more plentiful your signals, the better the model can get. For instance, if your pixel or Conversions API is feeding back every purchase with value, the algorithm gets smarter at predicting who is likely to make high-value purchases (helping if you use value optimisation). Signals also include creative elements (text, image contents), engagement feedback, and broader platform data (like overall ad performance trends). In short, signals are the evidence the algorithm uses to update its beliefs.
- Priors: In machine learning, a “prior” is an initial assumption before seeing specific data. Meta’s system has priors at multiple levels. At the start of a new campaign or with a new ad, without any direct history, the system will rely on prior knowledge to guide initial delivery. This prior could be learned from similar advertisers or ads in the past, or general consumer behavior. For example, Meta might know that on average an ad in the apparel category gets a 1% conversion rate for a purchase objective – that could serve as a rough prior for a brand new apparel ad. As real data for your specific ad set comes in, the prior is overridden by actual performance (this transition from prior to posterior belief is the essence of Bayesian updating). We saw earlier how with no data, Meta shows ads more evenly, effectively indicating it assumed all ads equal at first. That “no info” prior leads to equal serving. But note, Meta doesn’t start completely blind either; it has years of aggregate data that inform the modeling. Even the very first impression your brand-new ad gets is guided by learned patterns – the system knows which users might be worth testing first (those who fit your target and have a history of converting in similar scenarios).
Consider “priors” also in budgeting and bidding. If you set a target CPA, the system might initially bid in a way expecting to hit that CPA based on prior campaigns’ learnings. If it’s a new pixel with no history, it may bid conservatively until it gains confidence. Meta hasn’t publicly detailed all the priors, but we do have a clue from Google’s side: Google’s Smart Bidding, for instance, will use data from similar auctions outside your campaign to build an initial model when your own data is sparse. It’s reasonable to assume Meta’s ad delivery does something analogous – leveraging its immense pool of data to avoid starting from scratch. This is beneficial for advertisers because it means the algorithm isn’t guessing wildly in the beginning; it has a baseline from historical data.
To illustrate, say you launch a new e-commerce campaign optimising for purchases. In the early phase (what Meta calls the “Learning Phase”), the algorithm is testing a lot of possibilities, essentially validating or adjusting its priors. It might have assumed a 2% conversion rate, but after a couple of days with 10,000 impressions, it sees it’s actually 0.5%. It will update the models so that going forward it bids and delivers with 0.5% in mind. The learning phase typically stabilises after ~50 optimisation events (e.g. 50 purchases for a purchase-optimised campaign), after which the algorithm has enough evidence to reliably predict performance. That “50 conversion” rule of thumb is essentially the amount of data needed to confidently move from prior to data-driven posterior in Meta’s models.
Key Point: As an advertiser, you can’t directly set or change the algorithm’s priors, but you can influence how quickly it learns and how well it uses signals. Providing high-quality, timely data (signals) is your job. For example, implement the Conversion API to send purchase events that might be missed due to cookie restrictions – this ensures the algorithm has the evidence to update its internal models. Likewise, avoid resetting campaigns too often. If you start over, you’re forcing the system to relearn from a fresh prior. It’s like hitting the reset button on a seasoned salesperson’s memory – suddenly they forget what they learned about your customers. Instead, when possible, incrementally build on past data (e.g. use campaign budget optimisation or expand an existing ad set) so that the priors carrying over are already informed by your own performance history.
In summary, “signals” are the ongoing clues the algorithm uses, and “priors” are its initial expectations. Meta’s AI combines both to make decisions. The better the signals you feed, the faster and more accurately its prior assumptions get tuned to your reality.
4. Meta vs. Google Ads: A Comparison of ML Approaches in E-commerce
Both Meta and Google Ads heavily utilise probabilistic machine learning and even Bayesian techniques to optimise campaigns – but they do so in contexts that differ. Here’s how the two stack up, particularly for e-commerce:
4.1 Auction and Bidding
- Meta: Uses the formula discussed (Bid × Estimated Action Rate + User Value). The Estimated Action Rate is predicted via ML for each impression. Meta’s system optimises who sees the ad as much as it optimises how much to bid, since in Meta’s auction the platform decides the best match of ad to user. Advertisers can set bids or goals, but the platform will modulate delivery to hit those goals via pacing algorithms.
- Google: In Google Ads (especially Search and Shopping), the advertiser’s bid (or Smart Bidding strategy) plays a direct role each auction. Google’s Smart Bidding employs machine learning to set bids in real time for each query and user. It similarly predicts the conversion probability and value for each auction, and then calculates the optimal bid to either maximise conversions or achieve a target like CPA or ROAS. Notably, Google has confirmed it uses Bayesian learning to continuously improve its bidding models as more conversion data comes in. This means Google’s algorithms update their “beliefs” about conversion rates at granular levels (e.g. by query, by audience segment) as your campaign accumulates data. Both platforms therefore share the approach of constantly updating predictions rather than using fixed assumptions.
- In Practice: For an e-commerce retailer, Meta’s approach might feel more like audience optimisation while Google’s feels like keyword/value optimisation, but underneath, both are doing probabilistic predictions. On Meta you might let a broad audience run and count on the algorithm to find buyers; on Google you might let broad match keywords run and count on Smart Bidding to find converting search queries. The strategies converge in philosophy: give the machine flexibility and let it learn whom to show ads to or what bid to apply, respectively.
4.2 Personalisation & Targeting
- Meta: Personalisation is user-centric. Meta has deep profiles and behavior history on users (within the limits of privacy policies), and it utilises that to show the right ad to the right person. It doesn’t have intent signals like search queries, so it leans on interest, demographic and lookalike-based signals. Meta’s algorithm can dynamically personalise which product from your catalog to show which user (using something like Advantage+ catalog ads that show different items to different users based on likelihood to purchase). All this is again ML-driven – essentially predicting “User U is likely to buy Product P if shown”.
- Google: Google’s personalisation in Search is more limited to query intent (someone searches for “running shoes men’s size 10” – that’s explicit intent). However, Google also uses audience signals increasingly. In Display, YouTube, and Performance Max campaigns, Google leverages user data (like past browsing, Google account data, etc.) to predict who is a good prospect for an ad. For example, Performance Max for e-commerce can use your product feed and then automatically find likely buyers across Search, YouTube, Gmail, etc. by analysing tons of signals. Google has a trove of contextual and user signals: device, browser, time, location, language, and even things like whether the user is on a WiFi or what app they’re using – these are all factored into its ML models. And with techniques like cross-signal analysis, Google looks at combinations of signals (maybe the combo of “evening + mobile + remarketing list + query contains ‘sale’” is especially high converting) to inform bidding.
- The Difference: Meta’s advantage is in rich social-behavioral data and cross-site tracking (though curtailed by privacy changes, it still has a lot of first-party data within its apps and any data you feed via pixels). Google’s advantage is direct intent (search queries) and a wide ecosystem (they see you on search, YouTube, Android apps, etc.). For e-commerce, Google Shopping ads (now often wrapped into Performance Max) use product feed info and search intent – a different approach than Meta’s feed ads in the social timeline. But both have converged on using machine learning to match audiences to ads.
Concretely, a Meta broad targeting conversion campaign and a Google Maximise Conversion Value campaign are both “black boxes” to some extent – you input creatives and goals, and the algorithm finds where to show them and at what cost. The main difference a marketer will notice is Google’s need for keyword structure is diminishing (with broad match and automation) while Meta’s need for audience specification has diminished (with broad targeting). Both ask you to trust their AI to find the right user. And both use signals and historical data (priors) to do so – e.g., Google will look at your account’s past conversion trends and related queries even on day one; Meta will look at things like your pixel’s recent events or similar advertisers’ performance as it kicks off learning.
4.3 Creative Optimisation
- Meta: We covered how Meta auto-optimises creatives within an ad set. You can also use Dynamic Creative, where you upload components and Meta assembles them. Meta’s strength is in visual ad optimisation – it can figure out which image or video grabs attention and leads to action among different audiences. The Bayesian paradigm is evident in Meta’s creative testing; it will allocate traffic to creatives proportionally to their success probability and rapidly prune losers.
- Google: Google has Responsive Search Ads (RSA) for Search and Responsive Display Ads, where you also provide components (headlines, descriptions, images) and Google’s ML picks the best combinations for each auction. This is a similar concept. Over time, the system learns which combinations yield the highest click-through and conversion rates for each query or audience. Google’s approach here is also probabilistic – it tries various combinations and favors the ones statistically performing better. In video (YouTube), Google offers video ad sequencing and optimisation as well. So both platforms use ML to handle multivariate creative tests at scale, something impossible to do manually at the granularity they do.
E-commerce specifics: Meta’s Advantage+ Shopping campaigns automatically test different creatives (like different product sets or messages) and target audiences in one combined campaign, heavily relying on AI to allocate budget to the best combos. Google’s Performance Max similarly will test different creative assets (images, text, video) across channels and learn which asset drives the most sales for which audience. In short, both Meta and Google have embraced automated creative optimisation using bandit-like approaches – a win for marketers who feed the machine good assets, since it reduces the need to micromanage every ad.
4.4 Budget Pacing
- Meta: As discussed, uses pacing to achieve your cost goals over time, including both day pacing and lifetime pacing. Meta’s pacing is adaptive and takes into account predicted opportunities later. If you have a lifetime budget for a week-long campaign, Meta might spend less today if it predicts the weekend will have better inventory, and vice versa.
- Google: Google Ads has a concept of daily budget pacing. They allow up to 2x daily budget to be spent in a single day (to account for variability) but will average out to your monthly limit (30.4 × daily). The pacing is mostly on autopilot – Google will attempt to spread your spend throughout each day, but tends to be more aggressive early in the day until the budget hits, then it stops (for Search). Google doesn’t explicitly promise to optimise the intra-day distribution for performance (other than standard vs accelerated delivery, which nowadays is only standard). However, with portfolio bid strategies and seasonality adjustments, Google’s system can ramp spend up or down based on expected conversion rates. For example, Smart Bidding may spend your budget faster if it sees cheap conversions available (similar to Meta bid pacing logic). But in general, Google’s pacing is a bit more rigid at the budget level (you often see spend max out a budget and then ads stop until next day), whereas Meta, especially with lifetime budgets or cost caps, does more nuanced pacing throughout the day.
- Overall: Neither platform requires you to babysit spend hour-by-hour. Both will adjust automatically to some extent. On Meta, it’s particularly seamless if you give a lifetime budget and let it optimise delivery over the campaign duration. On Google, you set daily budgets and the system ensures you roughly spend that (with some performance optimisation via bidding). If we look under the hood, both likely use predictive control systems (possibly even PID controllers or similar algorithms as hinted by patents) to ensure delivery meets targets without dramatic overshoot or undershoot. For an e-commerce marketer, the practical guidance is: set budgets that reflect what you’re willing to invest, use pacing options wisely (lifetime vs daily), and let the system handle distribution. If you see consistent underpacing, it’s usually a signal your bid targets are too restrictive or your budget is very high relative to reachable conversions – not that you need to manual spike spend at 6pm, etc.
4.5 Data and Learning
Both Meta and Google emphasise feeding their algorithms with data. Signals are gold on both platforms. Google’s Smart Bidding uses dozens of signals at auction-time (device, location, interface language, remarketing lists, browser, operating system, and so on), and the interactions between those signals. Meta uses its own rich signals (user behavior, ad engagement, etc.). Both use historical data (priors) to jumpstart new campaigns. Google will look at similar queries or industry conversion rates to inform new campaign bidding. Meta will use lookalike modeling and prior campaign data. They also both have a “learning phase” concept – Google typically says a Smart Bidding strategy may take a couple of weeks (or ~50 conversions) to fully learn; Meta explicitly cites ~50 conversion events in a week per ad set for learning. During learning, performance might be volatile as the algorithms explore different possibilities.
For e-commerce optimisation, both systems excel when you give them the purchase feedback loop. That means setting up conversion tracking for purchases (with values) is critical on both. If either platform has to optimise for proxy metrics (clicks or pageviews) due to missing conversion data, the ML won’t focus on the ultimate sale outcome and will be less effective. It’s also why both Meta and Google encourage using broad targeting or broad match and relying on their algorithm – they each have more data than any individual marketer could analyse, and they use it to find customers likely to convert.
One difference: Google can leverage its understanding of intent (e.g. what people search for) and context in a way Meta cannot. For instance, if someone searches “buy iPhone 13 online”, Google knows this user intent at that moment is high, and any e-commerce advertiser selling iPhones would want to be in that auction. Meta doesn’t have that kind of explicit intent signal; it must infer interest from behavior. On the flip side, Meta might catch someone earlier in the funnel scrolling Instagram, who didn’t search anything but has shown interest in photography and gadgets – Meta’s ML might predict they’re a good candidate for an iPhone ad even without a search query.
In summary, Google’s machine learning shines at capturing existing demand, whereas Meta’s shines at creating or identifying latent demand. But both tasks involve complex predictive modeling with Bayesian updates as new data rolls in.
4.6 Bottom Line – Don’t Pick Sides, Use Both Wisely
For an e-commerce brand, Meta and Google Ads are complementary. It’s less about who has the “smarter” algorithm and more about understanding each system’s strengths. Meta’s probabilistic algorithm finds people where they spend time socially and inspires them to buy; Google’s finds them when they actively seek or show intent. Both are incredibly sophisticated, using Bayesian ML techniques to self-optimise campaigns in ways that manual tweaking simply can’t match. The best results often come when you feed both platforms as much conversion data as possible, embrace their automation, and then let them do what they’re designed to do – find you the most customers for the lowest cost.
5. Why You Can’t Outsmart the Machine (and Shouldn’t Try)
It’s a natural instinct for marketers to want to control and “beat” the algorithm – after all, in earlier days of digital ads, clever hacks and manual optimisations could yield big advantages. But today’s Meta (and Google) algorithms have become so advanced and dynamic that trying to outsmart them is usually ineffective, if not counterproductive. Here’s why:
- Constantly Evolving System: Meta’s algorithm is not a static formula – it’s a learning system that adapts to user behavior and retrains models regularly. If you discover a small trick today, the algorithm might adjust tomorrow. “Chasing the algorithm is a fool’s errand,” as one expert put it. By the time you exploit some perceived preference of the algorithm, that preference might change or your meddling might throw off the system’s learning.
- Complexity Beyond Human Scale: The algorithm evaluates far more factors simultaneously than any human could. For example, it might consider a combination of 100+ signals for each impression. As a marketer, you might think “the algorithm seems to favor video ads” (an observation from one slice of data), but internally the system is likely considering when video works and for whom, and it might switch to favoring a static image for a different cohort an hour later. Outsmarting something that is essentially recalculating a multi-dimensional equation in real-time is not feasible without access to the same data. Meta’s own engineers often can’t fully explain every outcome because of the complexity – it’s an interplay of many models and signals.
- It Optimises for the Objective You Give It: Some advertisers attempt to game the system by setting misleading objectives or by manual bid fiddling. But Meta’s algorithm takes your chosen objective (say, conversions) very seriously and optimises toward it. If you try to outsmart it by picking a different objective (hoping for cheaper traffic, for instance) and then manually converting that traffic, you usually end up with lower quality results. The system, had it been trusted with the actual goal, would likely have found better prospects. Similarly, manual bidding or budgeting tricks (like toggling campaigns on and off to catch specific hours, or duplicating ad sets to force spend) can confuse the learning process that was trying to gather stable data. The machine is literally built to allocate budget in the best way possible; assume any simple hack you think of, the engineering teams have likely accounted for or the algorithm will adjust to.
- Opportunity Cost of Micromanagement: Every minute you spend trying to “beat” the algorithm on delivery is a minute not spent on things that truly move the needle – your creative, your offer, your landing experience. Advertisers often find that when they relinquish some control (like using broad targeting, or allowing auto-bidding) and focus instead on creating better ads or improving their site’s conversion rate, the performance improves. This is not a coincidence. Thanks to the complexity of these ML algorithms, advertisers don’t need to manually consider each factor anymore – they set their goal and let the AI do the heavy lifting. Your energy is better used feeding the AI good inputs (e.g. eye-catching product images, a proper conversion tracking setup, persuasive ad copy) rather than trying to manipulate the AI’s outputs.
- The Algorithm “sees” much more than you do: We might look at our campaign and see aggregate metrics. The algorithm is looking at per-user probabilities. It might do things that seem odd to us (like spending most of budget on one ad, or serving a country we didn’t expect in a worldwide campaign) because it has evidence at a granular level that that’s beneficial. If you intervene without that insight, you often break the very mechanism that was finding efficiency. For example, you might think an ad isn’t getting enough spend and try to force it, but the algorithm withheld spend for a reason (users didn’t like it as much, it predicted poor outcomes). By forcing it, you’ll likely pay more for less results. In essence, trying to outsmart it often means fighting against the grain of the system, and the system is designed to maximise performance metrics – so you end up hurting your own performance.
All this said, “don’t outsmart the algorithm” doesn’t mean “do nothing and accept whatever happens.” It means collaborate with the algorithm rather than compete with it. Use your human intuition and creativity where the machine has no leverage: understanding your customers emotionally, crafting a brand story, coming up with a novel angle in your ad creative. The machine will then take those inputs and ensure they get delivered efficiently to the right people. When you find something that works (e.g. a particular ad resonates), scale it within the system’s framework (increase budget, expand audience) instead of trying to trick the system with workarounds.
6. Aligning with the Algorithm: Best Practices for Marketers
If outsmarting the algorithm is off the table, the path to success is aligning your strategy with the algorithm’s way of working. In practice, this means designing your campaigns and content to play to the strengths of Meta’s (and similarly Google’s) ML systems. Here are actionable best practices:
a. Provide Rich, Relevant Signals:
As discussed, signals are fuel for the machine. Ensure you have Meta Pixel (or Conversions API) set up to track meaningful e-commerce events (Product views, Add-To-Carts, Purchases with value, etc.). Use the highest-value conversion event that makes sense (e.g., Purchases rather than just Landing Page Views) so the algorithm optimises for what really drives your business. If you have offline conversions (like in-store sales from Facebook ads), feed that data back in. The more complete the dataset the AI has, the better it can allocate your budget to users likely to convert. Poor or missing tracking is like flying blind – the algorithm will optimise for the wrong thing. For example, if you only optimise for “Add to Cart” because purchase tracking isn’t set, you might get tons of adds-to-cart but fewer actual sales, because the algorithm isn’t being told to focus on the sale. In short, feed the machine the right success criteria.
b. Embrace Broad Targeting:
Meta’s algorithm excels when it has a large pool to find the best users from. If you narrow your audience too much (say, interest targeting 25–35-year-old cyclists only), you might exclude segments that would convert well. Often, a broad audience will outperform a very granular manual audience because the algorithm can sift through the broad audience for the gems. This doesn’t mean never use targeting – but use it judiciously. Provide broad boundaries (like countries or basic age ranges if needed, and perhaps an interest or two if it’s obviously relevant), but don’t stack dozens of interests or behaviors assuming you know exactly who will buy. Many advertisers have found success by simply targeting broad (no interests at all, just a conversion objective) and letting Meta’s ML find the customers. This works especially well if you have prior seed data (like a custom audience or pixel data) that Meta can use to start. Remember, your best customers may not fit the neat profile you expect – let the algorithm discover them.
c. Let the Algorithm Control Budget Allocation:
Features like Campaign Budget Optimisation (CBO) are designed to automatically shift budget between ad sets based on performance. Use them. If you have multiple audiences or creatives, put them under one campaign with CBO, rather than splitting into many campaigns with fixed budgets. CBO will allocate more to the ad set that’s giving better results, which is basically algorithmic budget pacing at the campaign level.
This prevents scenarios where one ad set is starving for budget while another is overspending with poor returns. Additionally, avoid duplicating campaigns/ad sets just to force more spend; this typically just splits the learning and can cause audience overlap issues. Consolidation is your friend – it gives the algorithm more data in one place to learn from. Meta’s own reps often advise consolidating to fewer ad sets/campaigns for this reason.
d. Respect the Learning Phase:
Whenever you launch a new campaign or make a significant change, the algorithm needs a learning period. During this time (the first ~50 conversion events), try to avoid major edits or resets. Don’t panic if performance fluctuates in those early days – that’s the model trying to find its footing. If you constantly reset (by swapping ads, changing targeting, pausing/restarting), you may never exit the learning phase properly. It’s like pulling a cake in and out of the oven repeatedly – it never gets a chance to bake evenly.
Instead, make needed changes in batches (if you have to add 3 new ads, add them all at once rather than one new ad every day) so that the learning restarts as few times as possible. Once an ad set is out of learning and performing steadily, avoid frequent tinkering. Minor tweaks (e.g., adjusting budget by less than 20%) usually don’t reset learning, but major ones do. Plan your strategy, set it, then give the algorithm some runway to optimise.
e. Focus on Creative Excellence and Testing:
Since the algorithm handles delivery, your biggest manual lever is the ad creative and messaging. Invest time in making compelling ads – high-quality images/video, clear and persuasive copy, strong calls to action. Then use the algorithm to test them (as described in creative optimisation). You can also use structured tests: for example, run a short experimental campaign using Meta’s split test feature to compare two drastically different strategies (different target or bidding approach) while holding others constant, if you want insights. But day-to-day, your creative refresh cycle will likely have the most impact. A good practice for e-commerce is to refresh creatives regularly (before ad fatigue sets in) but without tossing out your winners too soon. Leverage dynamic product ads if you have a catalog – they allow the system to show the most relevant product to each user (like showing the exact product a user viewed but didn’t purchase, a classic retargeting win). When creative fatigue is suspected (performance dips after an ad has been shown a lot), introduce new creatives to give the algorithm fresh options.
f. Leverage Automated Products (Advantage+ and Performance Max):
Meta’s Advantage+ shopping campaigns and Google’s Performance Max campaigns are built to simplify alignment with the algorithm. They intentionally remove many manual controls, forcing you to rely on the AI. While this can feel uncomfortable, they often drive strong results. For instance, Advantage+ shopping uses machine learning to find highest value customers across Meta’s apps with minimal manual setup. Many e-commerce advertisers report it finds conversions they couldn’t via manual campaign structures. The key is to supply enough creative variants and a healthy budget, then monitor results. These “black box” solutions work best when you feed them with your best data and creative, and let them run. When using them, supplement them with solid tracking and perhaps some guardrails (e.g., location targeting if you only sell in certain regions, or a sensible budget cap). But overall, if you choose to use these, truly hand over the keys – don’t fight the automation by trying to force additional layers on top.
g. Optimise Your Website and Funnel:
This might not sound like part of aligning with the ad algorithm, but it is. Remember, the algorithm optimises for the outcome on your site (if you set it to purchases). If your site is slow or checkout is cumbersome, fewer people convert – the algorithm then “thinks” those users weren’t good prospects and might misallocate or simply struggle to find converters. By improving conversion rates on your site, you’re effectively making the algorithm’s job easier; suddenly more of those clicks turn into sales, and the algorithm gets clearer signals about what a good prospect looks like. In Bayesian terms, you improve the likelihood function – the data becomes more separable between converters and non-converters. So, ensure your landing pages are relevant to the ads, your mobile site is fast, and your checkout process is smooth. This will feed back into better ad performance without you changing anything on the ad side.
h. Use Learnings Across Platforms:
While Meta and Google differ, lessons from one can often apply to the other. If Meta’s algorithm finds that a certain demographic or creative angle works for your product, you can create content targeting that angle on Google too (and vice versa). Both systems will reward relevance. Just be careful not to confuse correlation with causation – use these insights as ideas for creative and strategy, not as absolute targeting levers. Let each platform’s algorithm validate the idea on its own terms.
Conclusion
Today’s advertising algorithms, be it Meta’s or Google’s, are incredibly powerful allies for marketers – if we let them be. By harnessing probabilistic machine learning and Bayesian forecasting, Meta’s ad delivery system optimises campaigns in ways that manually adjusting levers simply cannot match, especially at scale. It personalises ad delivery to each user, chooses the right creative for the right moment, bids the right price in each auction, and paces your budgets to hit goals, all by continuously learning from data. Google’s systems operate on similar principles, finding the right keywords, audiences, and bids to drive conversions for your e-commerce store. Rather than trying to game these algorithms, the winning approach is to work with them: feed them rich data, give them freedom to learn, and focus your efforts on what humans do best (creative storytelling, strategy, and understanding your customer).
In the end, the “machine” isn’t your adversary – it’s your most efficient employee, crunching numbers and making micro-decisions at a scale no team of humans could. Much like you wouldn’t hover over a skilled employee’s shoulder all day and micromanage their every move, you shouldn’t micromanage the algorithm. Set clear goals, provide guidance and resources, then trust its expertise and check in on the results to inform your next strategy.
Marketers who adopt this mindset often see improved performance and free themselves to think bigger-picture. Use the information in this playbook to educate your team and stakeholders: success in modern e-commerce advertising is a blend of human creativity and machine intelligence. By aligning with Meta’s Bayesian-brained algorithm (and Google’s), you position your brand to achieve greater efficiency, scale, and ultimately, revenue growth in the digital marketplace.
