Retail Strategy Analytics

Data Science

Analytics

Customer segmentation and demand analysis for retail strategy.

Overview

This project analyses [N] transactions across [N] customers to support retail strategy decisions around [PRICING / INVENTORY / CUSTOMER TARGETING].

Methodology

Segmentation — [K-means / RFM analysis] clustering customers by [purchase frequency / basket size / recency]
Demand analysis — [regression / time series] modelling of [sales volume / revenue] against [price / promotions / season]
Strategy recommendations — segment-level insight for [pricing / marketing] decisions

Key Findings

[KEY FINDING — e.g. “[N] distinct customer segments identified. Segment [X] accounts for [Y]% of revenue despite being [Z]% of customers.”]

[Finding 1]
[Finding 2]
[Finding 3 — actionable recommendation]

Code

Python Notebook

---
title: "Retail Strategy Analytics"
description: "Customer segmentation and demand analysis for retail strategy."
categories: [Data Science, Analytics]
---

## Overview

This project analyses [N] transactions across [N] customers to
support retail strategy decisions around [PRICING / INVENTORY /
CUSTOMER TARGETING].

## Methodology

- **Segmentation** — [K-means / RFM analysis] clustering customers
  by [purchase frequency / basket size / recency]
- **Demand analysis** — [regression / time series] modelling
  of [sales volume / revenue] against [price / promotions / season]
- **Strategy recommendations** — segment-level insight
  for [pricing / marketing] decisions

## Key Findings

::: {.result-box}
[KEY FINDING — e.g. "[N] distinct customer segments identified.
Segment [X] accounts for [Y]% of revenue despite being [Z]%
of customers."]
:::

- [Finding 1]
- [Finding 2]
- [Finding 3 — actionable recommendation]

## Code

[Python Notebook](https://github.com/tkay305/Retail-Projects/blob/main/retail_strategy_analytics.ipynb)