Optimization Methodology
A deep dive into the algorithms, statistical models, and decision frameworks that power BidHelm's autonomous optimization engine.
Core Savings Calculation
BidHelm calculates savings using a forward-projection model based on historical spend velocity. When we pause a wasteful keyword, we project how much it would have continued to waste.
Primary Savings Formula
S = Vspend x Tproj x Cconf
Spend Velocity
Vspend = Σ Costi ÷ ndays
Where Cost_i is daily spend and n_days is the lookback window (typically 30 days).
Worked Example
A keyword spent $150 over 30 days with 0 conversions. BidHelm pauses it.
ROAS Efficiency Scoring
BidHelm uses a composite efficiency score to rank campaigns. This score weighs multiple performance factors with statistical normalization.
Composite Efficiency Score
E = ω1Rn + ω2Cn + ω3Vn
Min-Max Normalization
Xn = (X − Xmin) ÷ (Xmax − Xmin)
Ensures all metrics are on a 0-1 scale before applying weights.
45% — ROAS Weight
ROAS directly measures return on investment and carries the highest weight in the composite score.
35% — CPA Weight
Cost per acquisition captures cost efficiency, ensuring each conversion is achieved economically.
20% — Volume Weight
Volume factor ensures scale is not sacrificed when optimizing for efficiency metrics.
Why These Weights?
ROAS (45%) directly measures ROI. CPA (35%) captures cost efficiency. Volume (20%) ensures scale isn't sacrificed.
Anomaly Detection System
BidHelm continuously monitors performance using Z-score analysis to detect statistically significant deviations from baseline.
Z-Score Detection
Z = (X − μ) ÷ σ
| Z-Score | Status | Action |
|---|---|---|
Z > +2.0 | Above normal | Scale budget up |
-2 ≤ Z ≤ +2 | Normal (95% CI) | Continue monitoring |
-3 ≤ Z < -2 | Moderate drop | Enter Safe Mode |
Z < -3.0 | Critical | Pause + Alert |
Conversion Rate Deviation
ΔCR = (CRnow − CRbase) ÷ CRbase x 100
ΔCR < -60% triggers Safe Mode. ΔCR < -80% triggers Critical Alert.
Dynamic Budget Allocation
BidHelm uses a proportional allocation algorithm that redistributes budget from underperformers to top performers.
Budget Adjustment
Bnew = Bcur x (1 + δ x M)
Conservative mode — minimal changes, maximum safety.
Balanced mode — moderate adjustments for steady growth.
Aggressive mode — maximum optimization velocity.
Budget Tier Classification
Classify budget tier
if (budget < $10) tier = "micro"Apply tier constraints
micro: max_Δ = 10%, floor = $5Calculate performance score
score = E (Section 2)Apply bounded adjustment
B = clamp(B, min, max)| Tier | Range | Max Δ | Floor |
|---|---|---|---|
| Micro | $1-$10 | 10% | $5 |
| Small | $10-$100 | 15% | $10 |
| Medium | $100-$1K | 20% | $50 |
| Large | $1K-$10K | 25% | $100 |
| Enterprise | $10K+ | 30% | $500 |
Time-Decay Weighting
Recent performance is more predictive. BidHelm applies exponential time-decay weighting to ensure recent signals carry more weight.
Exponential Decay
Wt = e−λt
Time-Weighted Performance
Pw = Σ(Pt x Wt) ÷ Σ(Wt)
Yesterday = ~90% weight. 7 days = ~50%. 30 days = ~5%.
Decay Examples
Statistical Confidence
BidHelm requires minimum sample sizes before taking action. This prevents over-optimization based on noise.
Minimum Sample Size
nmin = (Zα2 x p x (1 − p)) ÷ E2
Confidence Factor Scaling
C = min(0.95, 0.5 + (n ÷ nt) x 0.45)
50% min for sparse data, scaling to 95% max for high-volume.
Minimum confidence threshold for any optimization action.
Maximum confidence cap to prevent overconfidence bias.
Clicks target for full statistical significance.
Decision Thresholds
Evidence-based thresholds derived from millions of ad interactions. All configurable in your dashboard.
| Action | Trigger Condition |
|---|---|
| Pause Keyword | Cost ≥ $25 AND Conv = 0 |
| Pause (Alt) | Clicks ≥ 100 AND Conv = 0 |
| Scale Up | ROAS ≥ 400% AND Conv ≥ 5 |
| Reduce Budget | ROAS < 100% AND Cost ≥ $50 |
| Add Negative | N-gram Cost ≥ $30, Conv = 0 |
| Safe Mode | ΔCR ≤ -60% |
| Critical Alert | ΔCR ≤ -80% OR Z < -3 |
Optimization Frequency
BidHelm runs optimization cycles every 30 minutes. Each cycle evaluates campaigns, applies time-decay weighting, and executes changes within safety bounds.
Known Limitations
Important Disclaimers
Savings projections are estimates based on historical patterns. Actual results depend on market conditions, competition, and campaign quality. Past performance does not equal future results.
Projection Limitations: Our calculations assume paused entities would continue spending at historical rates. Market dynamics could alter this.
Attribution Uncertainty: Some paused keywords might have eventually converted. We use 30-day lookback windows.
External Factors: BidHelm cannot account for website changes, landing page issues, pricing changes, or economic conditions.
Learning Periods: New campaigns need 2-4 weeks before algorithms reach full effectiveness.
No Guaranteed ROI: We track efficiency improvements but cannot guarantee specific outcomes. Results vary by industry.