A prototype for understanding African consumer behaviour
# Makiti Toile π¬
**Fashion-Data Lab for African Consumer Behaviour**
An experimental ML project that combines behavioural analytics, attitudinal data, and AI-generated synthetic personas to predict fashion demand and understand African consumer preferences.
## π¬ About Makiti Toile
**Makiti Toile** is our experimental fashion-data lab β a prototype for modeling African consumer behaviour through language, data, and synthetic simulation.
Built on Makiti's real sales data, Toile combines **behavioural analytics** (what shoppers do), **attitudinal data** (what they say), and **AI-generated synthetic personas** (what they might do next) to predict which items will sell in our next restock.
### π― Goal
To merge behavioural and attitudinal insights into a single predictive model that helps us:
- **Understand** why certain styles and price points perform
- **Simulate** realistic customer reactions before launching new stock
- **Reduce** over-ordering and waste by forecasting demand across categories
### π§© Project Phases
| Phase | Focus | Output |
|-------|-------|--------|
| **1. Behavioural Foundation** | Analyse past Makiti sales to uncover spending patterns, category performance, and linguistic cues in product descriptions | β
Complete: Customer segments, product analysis, keyword extraction |
| **2. Attitudinal Survey** | Collect short, anonymous responses on style identity, price comfort, and purchase motivation | π In Progress |
| **3. Persona Grounding** | Merge behavioural & attitudinal data to build 3-5 realistic customer archetypes | π Planned |
| **4. Synthetic Simulation** | Use LLMs and semantic similarity rating to generate synthetic purchase intent scores for new products | π Planned |
| **5. Validation** | Compare predicted intent scores to actual post-restock sales to measure model reliability | π Planned |
### π§ Core Concepts
- **Behavioural Data** β How customers actually shop: average order value, item count, category mix, descriptive language β¦