Becausely
A metric-change investigation workbench for small SaaS and ecommerce teams that aligns anomalies with the company's own deploy, pricing, campaign, incident, messaging, and product-event timeline, ranks candidate explanations, exposes confounders, and turns each hypothesis into a reviewable next test.
Small teams can see revenue, signups, activation, conversion, retention, or churn move without knowing whether the change came from a release, price edit, campaign, incident, broadcast, tracking break, seasonality, mix shift, or external event. Becausely joins metric definitions and source freshness to an authorized change timeline and produces ranked hypotheses rather than a causal verdict. Temporal proximity is not causation, correlation strength is not business impact, an anomaly can be a data defect, and a prescribed action is still an experiment proposal. Source event, metric observation, anomaly, candidate explanation, confounder, analyst review, intervention, outcome, causal conclusion, rollback, and correction remain distinct.
A founder, growth, product, analytics, revenue, or ecommerce operations lead at a ten-to-one-hundred-person company with trusted core metrics and a fragmented change timeline.
The source identifies a current shift from dashboards toward explanations, though no forcing deadline is supplied.
A reusable metric contract, event connectors, and investigation engine can serve many accounts once each customer maps trusted sources.
Two cross-references, four inbound connections, and four direct connections show repeated interest while the grounded score remains five.
Two cross-references, four inbound and four direct links, confirmed adjacent anomaly-alerting and marketing-attribution products, and no identified SMB product joining metric changes to an internal event graph support a useful investigation layer.
Two cited seed products could not be verified, the original causal and single-most-likely language overstates observational evidence, the buyer quartet and budget are incomplete, metrics and events are often unreliable, confounding is severe, and adjacent analytics vendors can add timelines.
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