Business Statistical Analysis Services
We provide business statistical analysis services and data analytics consulting for startups — rigorous, defensible analysis that turns your data into decisions, not just dashboards.
Business Statistical Analysis Services
Our business statistical analysis services cover hypothesis testing, regression modelling, Bayesian inference, and time-series analysis — the kind of rigorous statistical work that holds up under scrutiny from a finance team or a board, not just a chart that looks convincing in a slide deck.
We translate raw, messy business data into conclusions your team can act on and defend, with the methodology and assumptions made explicit rather than hidden behind a polished dashboard. Where the data doesn’t support a strong conclusion, we say so — a defensible “we don’t know yet” is more useful than a confident but unsupported claim.
Data Analytics Consulting for Startups
Through data analytics consulting for startups, we help early-stage teams set up the measurement foundations most skip too early — proper event tracking, a clear data model, and A/B testing frameworks that produce results you can actually trust, rather than ad hoc analysis bolted on after the fact.
We build dashboards around the handful of metrics that genuinely drive decisions for your stage of business, instead of a wall of charts nobody checks. Get this foundation right first, and the predictive modelling and forecasting you build on top of it will actually be worth trusting later.
Predictive Modelling & MLOps
Once the statistical groundwork is in place, we build and productionise predictive models — demand forecasting, churn prediction, anomaly detection — designed around the decision they’re meant to support, not as a standalone data science exercise.
We also put the MLOps pipelines, monitoring, and retraining processes in place that keep models accurate as your business and data change, instead of quietly degrading in production until someone notices the numbers look wrong. Monitoring includes drift detection, so you find out a model needs attention before it causes a bad decision.
Pricing & Delivery FAQs
Transparent expectations on investment, timelines, and technical ownership before starting.
help_outlineWhat is the difference between statistical analysis and machine learning for our use case?
Statistical analysis (hypothesis testing, regression, Bayesian inference) explains why something happened and whether a relationship in your data is real or noise. Machine learning predicts what will happen next. Most decisions benefit from statistical rigour first, then a predictive model built on that foundation.
help_outlineHow much historical data do we need before predictive modelling is worthwhile?
It depends on the signal you’re modelling, but we typically look for at least 6–12 months of consistent event tracking before forecasting or churn models produce results you can trust. If you don’t have that yet, we’ll help you set up the measurement foundation first.
help_outlineHow do you validate that a model’s conclusions are statistically sound?
We make methodology and assumptions explicit rather than hiding them behind a polished dashboard, and we say so plainly when the data doesn’t support a strong conclusion. A defensible "we don’t know yet" holds up better under board or investor scrutiny than an unsupported claim.
help_outlineDo you set up the analytics and event tracking foundation, or just the modelling?
Both. For early-stage teams we typically start with proper event tracking, a clear data model, and A/B testing frameworks, then build predictive modelling and MLOps monitoring on top once that foundation is trustworthy.
Got a dataset
that needs answers?
Whether it's a one-off analysis or an ongoing analytics partnership, we'll scope the right level of rigour for your decision.