E-commerce
Orders, repeat purchases, channels, categories and margin.
DiLoLid Business Analytics Audit
Find not only the metric change, but its cause and the next decision
We start with your data, detect signals and anomalies, decompose changes into drivers, formulate testable hypotheses, and identify the actions with the strongest potential.
Excel / CSVCRMBAF / ERPSQL7–10 business days*
Core outcome
01What exactly changed?
02Where and for whom did it change?
03What is the most likely cause?
04Does the evidence support it?
05What should happen next?
* Indicative timeline for a standard audit after analysis-ready data is received. Scope and timing are confirmed after the initial review.
When it helps
You already have data, but not one coherent answer to the business question
Business Analytics Audit is an entry product for companies that have accumulated transactional or customer data while analytics remains fragmented, manual or focused mainly on reporting.
Retail & Distribution
Sales, assortment, regions, customers and revenue concentration.
Service Businesses
Repeat visits, utilization, revenue per client and retention.
B2B Sales
Pipeline, customer base, churn risk and revenue concentration.
What we analyze
From data condition to causes, hypotheses and opportunities
The exact analytical scope depends on the business model and data quality. A typical audit covers:
Data Quality & Logic
Structure, missing values, duplicates, reference data, periods and alignment with the business process.
Key Metrics
KPI dynamics, segments, cohorts, seasonality, inflection points and rate changes.
Signals & Anomalies
Unusual changes, rare events, weak signals, risks and potential opportunities.
Root-Cause Diagnostics
Decompose changes across customers, products, channels, periods and other drivers.
Testable Hypotheses
Formulate possible causes and test whether the data supports them and the effect matters to the business.
Decisions & Opportunities
Priorities, action options, experiments and growth opportunities grounded in evidence.
What you receive
Not just a report – a map of the next decisions
The audit produces a concise evidence-based view of what is happening, why it may be happening, what the data actually supports, and what should be tested or changed next.
Assessment of data structure and reliability
Key KPIs, segments, cohorts and trends
Detected anomalies, risks and weak signals
Root-cause decomposition of key changes
3–7 testable business hypotheses
5–10 priority opportunities, problems or control points
Recommended actions, experiments and metrics for follow-up monitoring
Process
Data → Signal → Explanation → Hypothesis → Validation → Decision → Monitoring
The audit is built around the business question. SQL, statistics, time series or models are used only where they produce a better answer.
01
Data
Define the business question, sources and data quality.
02
Signal
Detect changes, deviations, risks and opportunities.
03
Explanation
Locate where the change occurred and the factors associated with it.
04
Hypothesis
Formulate possible causes in a testable form.
05
Validation
Test hypotheses statistically or through backtesting / modeling.
06
Decision
Define priorities, actions and expected business impact.
07
Monitoring
Define how the result should be measured after implementation.
What you need to start
One clean export can be enough for the first step
An initial audit does not require a full BI implementation or systems integration. A relevant-period export is often enough.
DATA
Excel / CSV
Sales, orders, customers, products, payments, interactions or operations.
SYSTEMS
CRM / BAF / ERP
Exports from business systems without permanent access at the first stage.
DATABASE
SQL / structured data
Structured tables or extracts if the company already maintains its own database.
FAQ
About the format
Do we need Power BI or a data warehouse already?
No. The audit can start from Excel/CSV or CRM/BAF exports. A BI solution is only recommended if it is justified by the next stage.
Do you transfer scripts and internal methodology?
The standard product delivers analytical results, findings and agreed deliverables. Source-code transfer is outside the base scope unless separately agreed.
What if the data quality is poor?
That is still a valuable audit result. We identify what prevents reliable analysis and what should be fixed first.
Can we continue after the audit?
Yes. The next stage can include recurring analytics, BI/reporting, customer or profitability intelligence, and data-preparation automation.
Start with your data
Show us what data you have – we will identify the most useful first analysis
Send a short description of your data sources, time period and business question. You do not need to share confidential data for the initial discussion.