CAPS Framework

Competitive-signal Anchored Purchasing-power Segmentation

A Python library implementing the CAPS Framework for inferring purchasing power patterns from competitive market signals. The framework integrates behavioral dimensionality reduction with structured qualitative refinement to produce segment profiles with ecological validity that conventional measurement approaches cannot match.

Background

CAPS is built on the theoretical proposition that competitive product launch events constitute high-discrimination natural experiments. When a new product enters an established category, the resulting behavioral signals — adoption speed, switching patterns, price elasticity responses, attribute weighting, and loyalty retention — form a multi-dimensional matrix from which purchasing power strata can be systematically inferred without relying on self-reported income or demographic proxies.

The framework operates through three sequential layers:

1. Signal Acquisition Layer — Structures raw behavioral responses to competitive launch events into a matrix X of dimension n x p.
2. Behavioral Compression Layer — Applies entropy-weighted PCA and two-stage clustering to extract purchasing-power-relevant latent structure.
3. Qualitative Refinement Layer — Validates and semantically labels cluster assignments through structured depth-interview coding and alignment scoring.

Installation

pip install caps-framework

From source:

git clone https://github.com/ahmed-elsayed-99/caps-framework.git
cd caps-framework
pip install -e ".[dev]"

Quick Start

import pandas as pd
from caps import CAPSPipeline

df = pd.read_csv("data/raw/sample_launch_event.csv")

pipeline = CAPSPipeline(
    observation_window=90,
    variance_threshold=0.72,
    min_silhouette=0.50,
    n_qualitative_per_cluster=8
)

results = pipeline.fit(df)
results.segment_map()
results.summary()

Real Data

The library ships with a synthetic dataset modelled on publicly available Nielsen-format retail panel structures. Real applications require longitudinal consumer-level data across a minimum observation window of 60 days spanning a competitive product launch event. See data/README.md for schema documentation.

Citation

@article{elsayed2026caps,
  title={Inferring Purchasing Power Patterns from Competitive Market Signals:
         A Conceptual Framework Integrating Behavioral Dimensionality Reduction
         and Qualitative Refinement},
  author={Elsayed, Ahmed},
  journal={SSRN},
  year={2026},
  note={CAPS Framework v1.0.0},
  url={https://github.com/ahmed-elsayed-99/caps-framework}
}

License

MIT License. See LICENSE.