Applied Statistics for Business Decisions
Most expensive decisions are made with an average and a spreadsheet. Our applied statistics practice turns the data your company already has into forecasts, experiments and cost models with explicit error margins, so leadership decides knowing how wrong it could be. The team brings together statisticians, data engineers and university faculty in the field.
What this service covers
What we solve with applied statistics
- Forecasts for demand, sales, ticket volume or cash flow, with confidence intervals.
- Experiment design and readouts (A/B tests) to tell whether a change worked or was noise.
- Cost and opportunity-cost models to compare alternatives before you invest.
- Anomaly detection in operations, fraud or quality.
- Customer segmentation, cohort and retention analysis.
- Sampling and statistical process control for audit and quality.
How we work
- We define the decision and the metric that drives it before touching the data.
- We audit data quality: gaps, bias and definitions that changed over time.
- We choose the simplest method that answers the question, from moving averages to Bayesian models.
- We validate out of sample: the model is tested on data it hasn't seen.
- We deliver the result with its uncertainty, and keep it running in production when needed.
What you get
- An executive brief with the recommendation, the expected range and the assumptions.
- Reproducible code and notebooks in your repository.
- When it makes sense, a forecast or dashboard that refreshes itself with new data.
Example: the cost model before automating
Before we build an agent or an automation we build its cost model: baseline, investment, run cost, payback, NPV and the alternatives we ruled out. It's the same analysis we publish in our case studies, and it decides whether a project is worth doing.
Which method fits the question
| Criterion | Descriptive statistics | Statistical model | Machine learning |
|---|---|---|---|
| Typical question | What happened? | Why did it happen, and how much can it vary? | What will happen in this specific case? |
| Data volume needed | Low | Low to medium | Medium to high |
| Interpretability | Full | High | Varies |
| Explicit uncertainty | No | Yes | Only if designed for it |
We start with the simplest method that answers the question with the precision you need.
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Frequently asked questions
Do I need a data warehouse to start?
No. We work with whatever sources exist: ERP, CRM, spreadsheets or exports. If the analysis becomes recurring, that's when it pays to automate the pipeline.
How is this different from a BI dashboard?
A dashboard shows what happened. Applied statistics estimates what will happen, how much it can vary, and whether a difference is real or noise. The output often ends up feeding a dashboard.
How long does an analysis take?
A scoped forecast or experiment takes two to four weeks, including out-of-sample validation.
Making an expensive decision on an average?
Tell us about the decision and the data you have, and we'll tell you which analysis backs it up.