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Root-Cause Diagnostics
Decompose performance changes across customers, products, channels, cohorts, periods and other drivers.
Business Analytics & Growth Intelligence
From business data to validated decisions
DiLoLid helps you move beyond seeing what changed to understanding why: detect the signal, identify the likely cause, formulate a hypothesis, validate it with data, and turn the evidence into a business decision.
DataSignalsHypothesesValidationDecisions
DiLoLid logic
01
Data → SignalKPIs, segments, cohorts, deviations and weak signals
↓
02
Signal → ExplanationWhere, when, for whom and under which conditions the change occurred
↓
03
Hypothesis → ValidationStatistics, comparisons, time series and models – only when useful
↓
04
Evidence → DecisionBusiness impact, priorities, recommendations and follow-up measurement
Not onlyWhat happened?
ButWhy did it happen?
ThenWhat happens next?
And most importantlyWhat should we do?
Business Analytics & Growth Intelligence
Analytics that reaches a decision
We combine data, statistical analysis and business logic so the work does not end with a report or chart. The goal is to identify the mechanism behind a change, validate it, and show what should happen next.
01
Business Analytics Audit
Initial diagnosis of data, metrics, anomalies and hypotheses with priorities for the next decisions.
Learn more →03
Hypothesis Validation
Statistically test assumptions, estimate effect size and separate real patterns from noise.
04
Forecasting
Time-series and predictive approaches when the business needs to understand future dynamics, load or risk.
05
Anomalies & Weak Signals
Identify unusual changes, rare events, risks and emerging opportunities before they become obvious.
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Decision Support
Translate analytical findings into action options, expected impact, priorities and next experiments.
DiLoLid methodology
Data → Signal → Explanation → Hypothesis → Validation → Decision → Monitoring
A closed loop: we do not stop at detecting a deviation. We explain it, test the hypothesis, recommend a decision and measure whether the decision produced the expected result.
01
Data
Map the business process, data sources and metrics that actually describe its state.
02
Signal
Detect changes, anomalies, risks and opportunities across metrics, segments and cohorts.
03
Explanation
Identify where the change occurred and which factors may explain it.
04
Hypothesis
Formulate one or more testable causes and expected effects.
05
Validation
Use statistics, comparison, backtesting or modeling to separate a real pattern from chance.
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Decision
Estimate business impact and define concrete options and priorities.
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Monitoring
Measure the post-implementation result and feed it into the next analytical cycle.
Analytical toolkit
Choose the tool for the question – not the question for the tool
We do not train a model just because we can. The best answer may come from an SQL slice, a statistical test, a simple rule, a time-series model or forecasting – depending on the problem.
01
SQL
Build the relevant joins, cohorts, slices and baseline metrics.
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Statistics
Assess distributions, variance, relationships and effect size.
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Time Series
Separate trend and seasonality and forecast future dynamics.
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Anomaly Detection
Detect unusual changes, thresholds, rare events and early signals.
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Hypothesis Testing
Test whether the evidence supports the assumption and whether the effect matters to the business.
Entry product
Business Analytics Audit
A fast way to move from accumulated data and routine reporting to a system of signals, explanations, hypotheses and prioritized management actions.
See the audit formatData quality and structure
Key metrics, segments and cohorts
Anomalies, risks and weak signals
Root-cause decomposition
Testable management hypotheses
5–10 priority opportunities or problems
Recommended next actions and how to monitor them
Technology foundation
SQL → Statistics → Time Series → Anomaly Detection → Hypothesis Testing
Technology is a means to validate a business hypothesis reliably and reproducibly. We use only as much complexity as the decision actually requires.
Data
SQLPostgreSQLExcelCRMBAF / ERPAPI
Analysis
PythonpandasPolarsStatisticsCohort Analysis
Prediction & Detection
Time SeriesAnomaly DetectionForecastingHypothesis Testing
Outcome
Decision SupportManagement ReportingPower BIAutomation
Built to expand
Today: internal business data. Tomorrow: a broader intelligence architecture.
DiLoLid starts commercially with Business Analytics & Growth Intelligence. The platform architecture is designed to later combine internal data, external research, legal intelligence and business processes.
Ideal starting clients
When you already have data, but analytics is still fragmented
E-commerce
Orders, customers, repeat purchases, categories and margins.
Retail & Distribution
Sales, assortment, regions, segments and performance.
Service Businesses
Utilization, repeat visits, revenue per client and retention.
B2B Companies
Pipeline, customer base, revenue concentration and churn risk.
Start with one question
What business question could your data help answer?
Tell us what data you have, how it is stored and what you want to understand. We will identify the most useful first analytical step.