Overview
📅
Last 7 days (2026-05-21 → {{MODEL_END_DATE}})
▾
You spent $293k with a 4.4× paid ROI over the last 7 days
Outcome & ROI
$967k
Baseline + Spikes Outcome
▬ 0% from last week
↗ 17% from last year
$1.23M
Paid Outcome
▲ 8% from last week
↗ 52% from last year
$2.21M
Total Outcome
▲ 5% from last week
↗ 34% from last year
$293k
Total Spend
▲ 18% from last week
↗ 33% from last year
4.4×
Paid ROI
▼ −6% from last week
↗ 14% from last year
7.5×
Blended ROI
▼ −11% from last week
↗ 1% from last year
Marketing Effectiveness
Performance by Channel
↑
Overperformance
vs. share of spend
↓
Underperformance
vs. share of spend
Direct Contribution by Channel
All Channels
Spend Summary
Weekly
Monthly
Performance by Week
Spend per Channel
Saturation by Channel
Impact
ROI
mROI
Time Shift by Channel
Drivers of Lower Funnel Spend
search_branded
influencers
You spent $6.53k with a 4.3× paid ROI over the last 7 days
Overview
📅
Last 7 days (2026-05-21 → {{MODEL_END_DATE}})
▾
Channel Saturation
Recent spend level
Historical Performance
ROI
Marginal ROI
Impact
Impact
Impact Shifted
Time Shift
Baseline, Spikes, and Context Summary
Baseline
Monthly
Weekly
Daily
Summary Plot
Summary Plot with Spikes
Baseline, Spikes, and Context Summary
Spikes
Individual
Grouped
Individual Spikes
Spike Detail
Baseline, Spikes, and Context Summary
Context Summary
Effect of Contextual Metrics
Variable over Time
brand_awareness
price
Effect over Time
Evaluating {{BRAND_NAME}}'s Predictions
Backtests
11 Days
📅 0.71% forecast error
32 Days
📅 0.24% forecast error
60 Days
📅 0.45% forecast error
186 Days
📅 1.2% forecast error
Fixed Configuration 30 days
📅 0.44% forecast error
Predicted vs. Actual Outcome
Daily
Cumulative
Changes to Predictions
Prior vs Posterior
Average Time-Shifts Prior vs Posterior
Baseline Prior vs Posterior
Experiments
This table shows all experiments that are actively calibrating {{BRAND_NAME}}. The main rows are the results of your lift tests, including point estimates and standard errors.
The drop-downs beneath each row show {{BRAND_NAME}}'s point estimates and confidence intervals for these same channels after incorporating lift test information. These estimates combine the model's previous knowledge of channel performance with the information provided in the lift test.