Meta Andromeda Signals: What to Feed the Algorithm Now That Targeting Is Dead
TL;DR
With targeting precision fading, Meta Andromeda runs on signal quality. Here is exactly what data to feed it: events, value, and identifiers, ranked by impact.
Once a client accepts that manual targeting is not the lever it used to be, the next question is always the same: "so what do we actually control?" The honest answer is signal - what you tell Andromeda about who converts and how valuable they are. I am Naman Khetawat, and here is the exact hierarchy of signal we prioritise when we take over a Meta account in 2026, and the specific implementation details that separate accounts that get this right from ones that only think they do.
The citable answer: Meta Andromeda's retrieval quality depends on the signal you feed it, ranked by impact: server-side purchase events with hashed customer identifiers first, value-based optimisation data second, and engagement/view-content events third - accounts with event match quality above 7/10 consistently retrieve and convert better than identical accounts with weak signal. Here is what that means for setup.
Signal Is the New Targeting
Under the old model, you told Meta who to show ads to. Under Andromeda, you tell Meta what a good outcome looks like, and it finds who to show ads to on its own, at a scale and precision no manual targeting rule could match. That shift means the quality of what you feed it - not your audience-building skill - is now the primary lever available to you. This is a genuinely uncomfortable adjustment for media buyers who built their expertise around targeting craft, since a large part of what used to be the skill has been absorbed into the platform's own optimisation layer.
The Signal Priority Stack
| Priority | Signal type | Why it ranks here |
|---|---|---|
| 1 | Server-side purchase events (Conversions API) with hashed email/phone | Highest-value, most reliable event; identifiers let Meta match to real people despite browser blocking |
| 2 | Purchase value passed with each event | Lets Andromeda optimise toward high-value customers, not just any conversion |
| 3 | Add-to-cart / initiate-checkout events | Useful mid-funnel signal, but should never substitute for purchase data |
| 4 | View-content / engagement events | Weakest signal; useful for audience seeding, not core optimisation |
Event Match Quality Is the Metric to Watch
Meta scores how confidently it can match your server-side events to a real user profile, on a scale visible in Events Manager. We treat anything below 6/10 as an active problem, because it means a meaningful share of your purchase events are not being matched to a person Andromeda can learn from - effectively wasted signal. Getting this score above 7-8 usually requires passing more than just email: phone number, first/last name, and city/zip, all hashed, meaningfully improve match rate over email alone.
This is the single most common gap we find auditing new Meta ads accounts - businesses that set up Conversions API once, years ago, and never revisited whether it is passing complete identifier data. The setup was often done correctly at the time, but as checkout flows change, plugins update, or new fields get added to a customer database, the original implementation quietly drifts out of alignment with what's actually available to pass, and nobody notices because the connection itself still shows as "active."
Value-Based Optimisation, Not Just Conversion Counting
Passing purchase value with every event lets Andromeda optimise toward customers who spend more, not just anyone who converts. Without value data, the system treats a ₹300 order and a ₹15,000 order identically, which means it has no reason to prioritise finding more of the second type. Brands with a wide range of order values see the largest gains from turning this on, because it lets the retrieval engine chase the customers that actually move revenue.
This matters even for brands that assume their order values are relatively uniform - a closer look at order data often reveals more variance than expected, especially once bundles, upsells, and multi-item orders are factored in. Even a moderate spread in order value is usually enough to see a measurable improvement once value-based optimisation is switched on correctly.
Don't Substitute Weak Signal for Strong Signal
A common mistake is optimising toward add-to-cart or view-content events because purchase volume feels too low to optimise against directly. This can work short-term for very new accounts, but it should be treated as a temporary bridge, not a destination - the moment purchase volume supports it, optimisation should move back to purchase events, because weaker signal always retrieves a lower-quality audience than genuine purchase intent does.
How to Audit Your Own Event Match Quality
| Step | What to check | Where to look |
|---|---|---|
| 1 | Current event match quality score | Events Manager → Data Sources → your pixel/dataset |
| 2 | Which identifier fields are actually being sent | Test Events tool, checking the raw payload of a recent purchase event |
| 3 | Whether purchase value is included in the payload | Same Test Events check, looking for the value/currency parameters |
| 4 | Consistency across devices/checkout paths | Test a purchase on both desktop and mobile to confirm identical data quality |
This audit takes under an hour for someone with Events Manager access, and it's worth doing quarterly rather than assuming a setup that worked at launch is still working correctly a year later. Checkout flows change, third-party apps get added or removed, and any of these can silently break identifier passing without triggering an obvious error anywhere in the dashboard.
A Worked Example: Fixing a Degraded Signal Setup
When we find an account with unexpectedly low event match quality despite Conversions API technically being connected, the fix usually follows the same sequence. First, we pull a sample of recent purchase events from the Test Events tool to see exactly what identifier fields are present - this alone usually reveals the gap within minutes, whether it's missing phone numbers, unhashed data, or inconsistent formatting across devices. Second, we work with whoever manages the storefront (often a developer or the ecommerce platform's support team) to ensure the checkout flow captures and passes the missing fields. Third, we monitor match quality daily for the following two weeks, since it typically takes several days of new purchase volume with the corrected payload before the score meaningfully moves.
The most common root cause we find isn't a fundamentally broken integration - it's usually one or two missing fields in an otherwise correct setup, which makes this a genuinely high-leverage, low-effort fix once identified.
Common Signal Mistakes We Correct
- Assuming email alone is sufficient for identifier matching. Phone, name, and location data each independently improve match rate over email alone - skipping them leaves real match quality on the table.
- Passing unhashed data or hashing inconsistently. Identifier data must be hashed using Meta's specified method; inconsistent hashing across devices or checkout paths silently breaks matching for a portion of events.
- Never revisiting the setup after initial implementation. A checkout flow redesign or new payment provider can quietly change what data is captured and passed, degrading a previously healthy setup without any visible alert.
- Optimising toward add-to-cart events indefinitely. This should be a temporary bridge for very new accounts with low purchase volume, not a long-term optimisation target once real purchase data becomes available.
A Real Example
A jewelry D2C brand had event match quality stuck at 5/10 for over a year, despite Conversions API technically being "live." Auditing the implementation showed only email was being passed, unhashed inconsistently across devices. We rebuilt the event payload to include hashed email, phone, and location data, plus purchase value on every event. Match quality rose to 8/10 within two weeks of sufficient volume, and cost per purchase dropped 21% with zero changes to targeting, budget, or creative - purely from Andromeda having better signal to retrieve against.
The brand's team had assumed their Conversions API setup was fine simply because it showed as connected in the dashboard - nobody had ever checked the actual contents of the event payload until we ran the audit above. That gap between "technically connected" and "actually sending complete data" is the single most common signal issue we find across new client accounts.
FAQ
What signal does Meta Andromeda actually use?
Primarily server-side purchase events via Conversions API, with hashed customer identifiers (email, phone, name, location) attached, plus purchase value. Weaker engagement signals like view-content events matter far less and should not be the primary optimisation target once purchase volume supports better data.
What is a good event match quality score?
Aim for 7 or above out of 10. Scores below 6 typically indicate incomplete identifier data being passed with events, which materially limits how well Andromeda can match your ads to real converting customers.
Does passing purchase value actually change delivery?
Yes. Value-based optimisation lets the algorithm prioritise finding customers who resemble your highest-value buyers, not just anyone who converts at any amount. Brands with wide order-value ranges typically see the largest improvement from enabling this.
How often should I audit my Conversions API setup?
Quarterly is a reasonable minimum. Checkout flows, third-party apps, and platform updates can all silently degrade identifier passing without any visible error, so a setup that worked correctly at launch can quietly drift out of alignment over time.
Is a "connected" Conversions API integration enough to trust?
No. A dashboard showing "connected" only confirms the technical link exists, not that it's sending complete identifier and value data. Always check the actual event payload in the Test Events tool rather than assuming connection status equals data quality.
Fix Your Signal Before Changing Anything Else
If your Meta account's targeting and creative both look fine but performance still lags, the underlying signal quality is the most likely culprit worth checking first. Book a call with Balistro and we will audit your Conversions API setup and event match quality.
