STABLE TV × MARTHA ANN (UA)
Full Funnel Impact. 90d window. Jan 26 - Apr 26, 2026.
Period window
Default: Era. Phase 1 hero, -24% / 8-week window.
Pulse · 7-14D · Apr 12 - Apr 26, 2026Tactical · 14-30D · Mar 27 - Apr 26, 2026Confidence · 30-60D · Feb 25 - Apr 26, 2026Lift · 60-90D · Jan 26 - Apr 26, 2026Phase Compare · 90-120D · Dec 27, 2025 - Apr 26, 2026Era · 120-180D · Oct 28, 2025 - Apr 26, 2026Full Arc · 180D+ · May 2025 - Apr 2026
Lift read. Trailing 90 days. Brand spend correlates with reactivations (r=+0.96). CTV strong-positive with Acq KPIs (r=+0.83), strong-negative with performance CAC (r=-0.78). Northbeam Metrics Explorer 10-mo dataset.
Operational view below. Same underlying data, alternate framings live in the methodology card. Methodology card ↓ shows all three with the math.
Lift read
Trailing 90 days. Brand spend correlates with reactivations (r=+0.96). CTV strong-positive with Acq KPIs (r=+0.83), strong-negative with performance CAC (r=-0.78). Northbeam Metrics Explorer 10-mo dataset.

Is the relationship real?

L2

Weekly read. When TV moves, do downstream channels actually follow? Three different math approaches all confirm the same answer here. Not a fluke, not double-counted, holds up to a CFO audit.

Synthetic mock · methodology preview. Dashboard shell + ingestion plumbing + per-layer compute skills are scaffolded and deployed; the actual measurement engines (mix model fitter, cohort-CLV model, geo holdouts, brand-pulse LLM) wire to brand data at onboarding.

When does today's TV spend show up in acquisition MER?

+0.93at 2w lag
population default · case-study-blinded-DTC
0w2w · peak4w6w8w
Builds from 0 lag (response still in flight), peaks at 2w (0.93 — the brand-layer half-life zone), then decays as the spend variation falls past the response window. Cut TV at week 4 because acquisition MER looks flat at 0 lag and you cut two weeks before the peak shows up.

When TV moves, here's what follows

checked two different ways
ChannelStrength · linearStrength · rankedLag to peakConfidence
Stablebrand-layer $ → Reactivations+0.80+0.782wHigh
AcqAcquisition MER+0.93+0.912wHigh
AcqAcquisition CAC-0.59-0.612wHigh
ReactReactivation Subs+0.96+0.952wHigh
GoogNew Visits Brand Srch + PMax+0.89+0.867dHigh
GoogAcq Rev Brand Srch + PMax+0.83+0.8030dHigh

Solves "every channel claims the lift"

cross-channel credit isolation
TV
62%
overlap
62%
Meta
48%
overlap
48%
Google
55%
overlap
55%
Without penalty: 165% credit attributed (every channel claims the lift)With ridge: 100% isolated by source
Show the mathThree different ways of reading the relationship · why we use all three

Linear strength. The standard correlation read. Sensitive to outliers · a few weird weeks can pull the number up or down.

Ranked strength. Same idea but compares the ranking instead of the absolute values. Robust to outliers. If linear and ranked agree, the relationship is real, not driven by a few extreme weeks.

Cross-channel isolation (Ridge regression). Solves the "every channel takes credit" problem. Standard reporting tools double-count: Meta says it drove the lift, Google says it drove the lift, Northbeam says they all drove the lift, and the totals add to 165% credit. Ridge applies a penalty when channels overlap, redistributes credit cleanly to 100%.

When all three agree, the read survives a CFO audit. When they disagree, diagnose the data shape before acting on either number.

Open-source: statsmodels.tsa.stattools.grangercausalitytests · pingouin partial correlations · ridge regression with alpha tuned via cross-validation. All free.