Services
Decision Science
Mathematics and behavioural insight applied to the choices that shape performance.
Overview
Decision science combines statistics, optimisation, simulation and an understanding of human judgement to help leaders choose between alternatives under uncertainty. It asks what is the decision, what are the options, what do we believe about outcomes and what trade-offs are we willing to make.
We use it when prediction alone is not enough: allocating budgets, setting prices, designing incentives or deciding where to invest. The product is a decision framework people can inspect and challenge, not a verdict from a black box.
Problems we help with
- Decisions made on intuition or the loudest voice rather than structured evidence
- Forecasts presented without uncertainty, leading to overconfident plans
- Competing objectives that nobody has made explicit
- Analyses that stop at insight and never reach an action
Capabilities
Decision framing
Decision trees, influence diagrams and option maps that make assumptions visible.
Optimisation
Linear, integer and heuristic methods for allocation, scheduling and routing.
Simulation
Monte Carlo and agent-based models for risk and scenario analysis.
Experimentation and causal inference
A/B tests, quasi-experiments and uplift analysis to separate cause from correlation.
Value and risk quantification
Expected value, downside and sensitivity measures in business terms.
Decision support tools
Interactive models that let leaders test options themselves.
How we approach it
- 01
Clarify the choice
Identify the decision maker, alternatives, constraints and timing.
- 02
Model the uncertainty
Capture what is known, estimated and unknown, with ranges.
- 03
Evaluate options
Compare alternatives on agreed criteria and test sensitivity.
- 04
Embed
Put the model into a repeatable review process.
Where it is applied
- Marketing and capital budget allocation
- Pricing and promotion design
- Workforce and capacity planning under uncertainty
- Evaluating whether an intervention actually worked
Outcomes to aim for
These are goals we design towards, not guarantees.
- Explicit trade-offs and assumptions
- Decisions that can be reviewed after the fact
- Quantified upside and downside
- A repeatable decision rhythm for the leadership team
Frequently asked questions
Bring us a decision you want to improve.
Tell us about the problem and the data you have. We will reply with a candid view of where we can help.