AMAP - Meta -

AMAP<>Meta
Simple. Safe. Scalable. More results.
Outcome engine for Meta that learns what converts and shifts spend automatically using AMAP data and optional first‑party/sales data. No PII.
Works directly in your ad account.
Complete AMAP Solution Overview
What it is
Auto‑optimizing Meta program focused on your KPI (CPA, ROAS, lead quality, LTV proxy)
Works in your existing ad account; no SDK/code
Who it's for
Brands & agencies that want higher ROAS/lower CPA with minimal ops work
How it works — 3 clear steps
01
Direct Meta handshake (PII-free)
We connect via Meta's API or via a bulk edit spreadsheet.
02
Smart geo line items
Your campaign is automatically broken into postal-code and geofence line items.
03
Hotspot optimization
Our Hotspot Algorithm groups the right geos into testable cohorts, learns fast, and reallocates budget to winners.
Outcomes
+ROAS / −CPA
Less waste
(smarter allocation)
Faster learning
Simple workflow
Clear insights
Core capabilities
Goal‑based optimization & budget orchestration
Structured tests; amplify winners quickly
Weekly explainers: what changed, why, what's next
Flexible modes: Insights‑Only • Co‑Pilot • Fully Managed
Data & privacy
Inputs: Census, Google; optional aggregated 1P/sales/CRM
Aggregated, cohort‑style modeling — no PII, no user‑level profiling
Aligns with Meta policies; least‑privilege access (read‑only unless managed)
Setup (what we need)
Goal/KPI, budget & guardrails, Meta ad account access
Optional: aggregated first‑party/sales data
Implementation Timeline
1
Step 1
Baseline + quick tests → early lift
2
Step 2
Scale winners; stabilize KPIs
3
Step 3
Fewer underperforming dollars; less manual tuning
Reporting
Outcome dashboard
(KPI, budget moves)
Driver insights
(what's working & why)
Action log
(changes & next steps)
Case Study: Netherlands Lower Cost Per Result
Objective
Segment Netherlands into 15 ad set clusters of postal codes, balanced by Market Population × Avg Score
Ensures equal audience potential per cluster
Enables fair budget allocation & clear performance insights
Clustering Method
01
Inputs
Postal Code, Market Population, Avg Score → Weighted Value (Pop × Score)
02
Process
Sort postal codes by weighted value, Group into 15 clusters with near-equal audience potential
Allocation
Total Budget
$5,000 (Aug 25–Sep 4 → 11 days)
Daily Pace
$455/day
Clusters
15 ad sets (275 postal codes each)
Per-Ad Set Budget
$20–30/day
10-Day Optimization
Track (per ad set)
Spend, Impressions, CTR, Conversions, CPA/ROAS
Analyze
Avg Score vs. performance, Market size vs. efficiency, Geo lift (over-performing regions)
Optimize
Shift 20–30% budget from weak → strong clusters, Pause low CTR/CPA clusters, Test creative (top vs. bottom quartiles), Map results to postal codes for geo hotspots
Deliverables
Cluster mapping file
Performance dashboard (daily spend, CTR, CPA, conversions)
Visuals: Heatmap (NL), Scatter (Avg Score vs. CPA), Pivot table
Results
Cost Efficiency
14.2% lower cost per result after clustering
Best Results
From clusters where budget was reallocated to top performers
Trends
Weak clusters paused weekly to prevent waste
Delivery
Strong impressions across clusters, fair budget distribution, ongoing optimizations
CLICK HERE FOR A DEMO