Optimization
Quantitative Portfolio and Pricing Optimization
Simulation-based approach to optimize offer mix, pricing scenarios, attribute value and expected market share.
Dataset type: simulated. No confidential client or employer data.
Executive Summary
This case uses quantitative preference and pricing methods to compare offer configurations, estimate attribute value and simulate expected market response.
Business Question
Which combination of features, price points and portfolio options is most likely to improve expected value without relying on opinion-only prioritization?
Statistical Question / Hypothesis
The analysis estimates whether observed preference patterns support a differentiated value hierarchy across attributes, price levels and offer bundles.
Dataset
The dataset is simulated and includes respondent-level choice tasks, attribute levels, price alternatives, preference scores and portfolio constraints.
Methodology
The workflow combines price elasticity, MaxDiff, conjoint analysis, TURF and market share simulation. The emphasis is on comparing scenarios under explicit assumptions rather than presenting a single deterministic optimum.
Implementation
Python and R are used for data preparation, model estimation and simulation. Excel is used for scenario tables that can be reviewed by commercial stakeholders.
Results
Outputs include attribute importance, price sensitivity, reach overlap, simulated market share and a ranked set of portfolio scenarios.
Limitations
Limitations include simulated preference data, stated-preference bias, uncertainty in competitive response and sensitivity to the chosen attribute set.
Executive Recommendation
Prioritize portfolio options that remain strong across plausible pricing assumptions and avoid choices that perform well only under one narrow scenario.
Tools Used
Python, R and Excel.
Links
Notebook, GitHub repository and executive PDF are coming soon.