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While many brands still cling to manual controls and outdated hacks, the smartest ecommerce brands are doing the opposite: they’re aligning with Meta’s machine learning priorities.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.