Methodology

Our intelligence methodology combines proven statistical techniques with modern AI capabilities to deliver insights that are accurate, verifiable, and actionable. Every analytical method we use has been validated in academic literature and adapted for real-world business data with its inherent messiness and incompleteness.

Evidence Tiers Our evidence tier system provides transparency about the strength and type of evidence behind every insight. Each tier represents a different level of analytical rigor with different assumptions, requirements, and confidence levels.

Tier 1: Pattern Recognition Statistical correlation across connected data sources using methods including Pearson and Spearman correlation, chi-squared tests for categorical data, and association rule mining for complex multi-variable patterns. We identify patterns that exist across multiple systems, finding relationships that siloed analytics cannot see. For example, correlating support ticket volume in Intercom with deal stage progression in Salesforce to identify at-risk accounts. Tier 1 findings explicitly state that correlation does not imply causation and include effect size measurements.

Tier 2: Temporal Causal Analysis Time-series Granger causality testing with lag optimization. We prove what drives what by analyzing temporal sequences, answering whether changes in one metric reliably predict changes in another. This goes beyond simple correlation by establishing temporal precedence: if A consistently precedes B, and knowing A improves our prediction of B beyond what B's own history provides, we have evidence of a causal relationship. Vector autoregression models handle multiple interacting time series. Stationarity testing and differencing ensure valid statistical inference.

Tier 3: Quasi-Experimental (Coming Soon) Natural experiment identification in operational data. We find situations where business changes (pricing updates, feature launches, process changes) created natural A/B tests in your data. Using difference-in-differences analysis, regression discontinuity, and synthetic control methods, we estimate causal effects of changes that were not designed as experiments. This tier provides stronger causal evidence than temporal analysis by leveraging natural variation in your data.

Tier 4: Validated Causal (Coming Soon) Continuously validated structural equation models with confidence intervals. Long-running causal models are calibrated against observed outcomes and refined over time. This tier provides the strongest evidence by tracking how well previous causal claims predicted actual outcomes.

Statistical Methods Deep Dive Z-score anomaly detection uses a rolling baseline window (configurable from 7 to 90 days) to establish normal ranges for each metric. Deviations exceeding 2.5 standard deviations trigger anomaly alerts. Seasonal adjustment handles predictable patterns like weekly or monthly cycles. Multi-metric anomaly fusion detects situations where individual metrics are within normal range but their combination is anomalous.

Time-series decomposition separates trends, seasonal patterns, and residual noise to improve detection accuracy. Bayesian updating refines anomaly thresholds as more data accumulates. False positive suppression uses contextual awareness (holidays, known events, planned changes) to reduce alert fatigue.

Validation Process Every analytical pipeline undergoes rigorous validation before deployment. Backtesting verifies that insights generated from historical data align with known outcomes. Cross-validation ensures models generalize beyond their training period. Human expert review confirms that automatically generated insights are meaningful and actionable. A/B testing compares new analytical methods against existing baselines before full rollout.

Accuracy Tracking We continuously monitor the accuracy of our analytical outputs. Key metrics include 82 percent calibration accuracy where predicted probabilities match observed outcomes, 91 percent relevance score where insights are rated useful by users in feedback surveys, and 97 percent data freshness SLA where data is updated within the committed timeframe. These metrics are published in our quarterly transparency reports and tracked internally on daily dashboards.

Limitations and Caveats No analytical system is perfect, and we are transparent about our limitations. Correlation-based findings (Tier 1) should not be treated as causal evidence without further investigation. Temporal causal analysis (Tier 2) assumes stationarity and can be confounded by unobserved common causes. Small sample sizes reduce statistical power and increase uncertainty. Data quality issues in source systems propagate to our analysis. We actively research methods to address these limitations and clearly communicate uncertainty in every insight.