How your data becomes recommendations. Every insight in your daily report and dashboard is built from your own history — then cross-referenced against your vertical.
Connect sources
Normalize
Model patterns
Benchmark vs vertical
Recommend
Patterns detected in your data
Creative fatigue
6 creatives tracked
powers 3 rotation recs this week
Dayparting concentration
9 campaigns analyzed
powers 4 budget-shift recs
Audience saturation
5 segments
powers 2 expansion recs
Spend-pacing drift
12 campaigns
powers 5 pacing-cap recs
Vertical benchmarks
vs 14 home-goods peers
powers context on every metric
Creative → outcome
18 creatives mapped
powers format & message guidance
Every recommendation traces back to these patterns in your data — drawn from your live sources and uploaded historicals below.
Ad platforms
· 6 of 8 connected
M
Meta
Ad platform
Connected12.4M rows · 4h ago
G
Google Ads
Ad platform
Connected9.8M rows · 4h ago
T
TikTok
Ad platform
Connected6.1M rows · 4h ago
in
LinkedIn
Ad platform
Connected1.2M rows · 6h ago
►
YouTube
Ad platform
Action neededToken expired
P
Programmatic
Ad platform
Connected5.5M rows · 4h ago
a
Amazon Ads
Ad platform
Connected2.1M rows · 5h ago
R
Reddit
Ad platform
Not connected
Analytics, commerce & warehouses
· 3 of 7 connected
GA
Google Analytics 4
Analytics
Connected8.9M rows · 1h ago
Sh
Shopify
Storefront
Connected742K rows · 1h ago
SN
Snowflake
Warehouse
ConnectedStreaming · Live
St
Stripe
Payments
Not connected
BQ
BigQuery
Warehouse
Not connected
S3
Amazon S3
Object storage
Not connected
At
Airtable
Database
Not connected
Upload historicals
· Backfill data from before your accounts were connected
Drag historical exports here, or browse files
CSV, TSV, or Excel · platform exports auto-detected · up to 2 GB