Module Overview

Retail Merchandising Analytics

Analytics has become one of the most powerful tools available to retailers. This module introduces learners to merchandising analytics in the fashion environment, enabling them to gain an understanding of the concepts, techniques and analytical tools utilized in retail merchandising and buying. This module aims to equip the learner with analytical skills needs to deal with the fast paced retail environment. Participants will gain practical knowledge of merchandising analytics in a user-friendly lab environment.

Module Code

RETL 1124 CRN: 324

ECTS Credits

5

*Curricular information is subject to change

Introduction

The role of Fashion buyer and merchandiser, budgeting in the fashion retail

environment, weekly sales sheets, costing sheets.

 

Sales Analysis

Track performance based on key metrics such as target achievements, growth over last

period. Evaluate the impact of store size and employee strength on sales and resultant

margins among various merchandising categories. Identify sales and margin growth by

sales channel.

 

Merchandising Analysis

Develop merchandising plans. Tract merchandising plan, evaluate plans against actual.

Optimise merchandising mix based on margins and sales value.

 

Pricing Analysis

Retail objectives and pricing. Calculating initial, cumulative, maintained and average

mark-up. Pricing to consumer demand.

 

Promotion and Mark-Down Analysis

Calculating mark-downs, mark-down cancellations and employee discounts.

 

Profitability Analysis

Understanding factors that impact on profitability, calculation of profitability index and

performance index such as sales per employee hour.

 

Inventory Analysis

Calculation of book inventory, calculation and estimation of shortage, inventory

evaluation, gross margin return on inventory, inventory planning. Merchandising Control and Report Analysis Control standards, Report format and analysis Forecasting Identify patterns in data, forecast using a number of techniques including moving average, exponential smoothing and regression. Evaluate the accuracy of forecasts. Evaluate the seasonal factor within data sets and seasonally-adjust data sets.

The module will incorporate a range of teaching and learning methods including lectures, class discussion and e-learning. The learning environment will be practical and integrative, using excel to develop solutions to real world problems.

Module Content & Assessment
Assessment Breakdown %
Formal Examination70
Other Assessment(s)30