RA: Retail Planning,Assortment Analytics with Excel & Python

RETAIL ANALYTICS · RETAIL BUDGETING · OTB PLANNING · ASSORTMENT OPTIMISATION · MERCHANDISING · PYTHON · AUTOML · INVENTORY · PYCARET · RETAIL PLANNING

★ Highest Rated on Udemy — The Retail Planning Course That Practitioners Trust

This course carries Udemy’s Highest Rated badge — earned by 15,400+ enrolled students who completed it and came back to recommend it with a sustained 4.6-star rating. In a category crowded with short overview courses, the Highest Rated badge signals something rarer: a program that retail professionals, buyers, merchandisers, and planners actually found useful in their day-to-day work.

★ Top Performer on Udemy for Business — Chosen by Retailers for Team Upskilling

This course is consistently selected through the Udemy for Business catalogue for retail team training — the platform used by companies like Nasdaq, Volkswagen, and Eventbrite to upskill their professionals. When retail companies search for a course to train their buying, planning, and merchandising teams in data-driven retail analytics, this is the course they find.

★ Built by a Retail Industry Consultant — Co-Founder of Keip, a SaaS Platform for Retail Management

Haytham is an active retail and supply chain consultant who works with retailers including Sephora France and Sharaf Group Dubai, and is the co-founder of Keip — a SaaS platform built specifically for retail management and analytics. The retail metrics, assortment frameworks, OTB models, and pricing techniques in this course are not textbook constructs. They come directly from live client work and from the product built for retail professionals at Keip. That practitioner depth is what separates this course from all other retail analytics programs on Udemy.

★ The Only Retail Course That Teaches Full Budgeting, OTB, and AutoML Forecasting Together

Most retail courses on Udemy cover one area: either metrics, or buying, or assortment, or inventory. This course covers all of them in one 19.5-hour program — and goes further. Section 6 teaches retail budgeting from A to Z: top-down and bottom-up forecasting, OTB planning, budget markdowns, end-of-month stock calculation, seasonality modelling, and OTB by category. Then, added by student demand in August 2023, a full AutoML chapter using PyCaret generates forecasts for OTB automatically. No other retail course on any platform does this.

★ Python Applied to Retail from Day One — No Coding Experience Required

This course is not Python theory followed by retail examples. It is retail problems, solved in Python, from the first section where metrics appear. Conversion rate, inventory turnover, safety stock for all SKUs, reorder points, assortment optimisation, competing product pricing, and ML forecasting — all built in Python, step by step, with the instructor coding alongside you. The Python crash course is fully included. No prior coding experience is needed at any point.

Retail is a data business. Every buying decision, every OTB plan, every assortment call, every markdown — all of them have a number behind them. Most retail professionals know this but lack the tools to build those numbers analytically. This course changes that. It is the only retail planning program on Udemy that combines Excel fundamentals with Python automation and AutoML forecasting in one 19.5-hour program — and it carries Udemy’s Highest Rated badge to prove that retail professionals find it useful.

The course follows the same proven progression as all programs in this series: metrics and theory in Excel first, Python second. But unlike generic Python courses, every section applies Python to a specific retail problem from day one: calculating conversion rate and inventory turnover across a live retail dataset, building an OTB budget with top-down and bottom-up forecasting, optimising assortment against retail space, modelling competing product pricing with multinomial logit models, calculating safety stock for all SKUs simultaneously, and using PyCaret AutoML to generate sales forecasts for OTB automatically — comparing, blending, and stacking models in minutes.

The retail frameworks in this course come from Haytham’s active consulting work with retailers including Sephora France and Sharaf Group Dubai, and from Keip — the SaaS platform for retail management that he co-founded in Bordeaux. Every metric, every OTB model, every assortment framework, and every pricing tool has been tested in real retail operations — not adapted from a textbook.

WHAT MAKES THIS COURSE DIFFERENT

[ FULL ]

End-to-end retail analytics in one program

Metrics, Python, budgeting, OTB, assortment optimisation, competing product pricing, seasonal inventory, safety stock, and AutoML forecasting — all in one 19.5-hour program. No other retail course covers this breadth.

[ SEPH ]

Built by an active retail consultant and SaaS founder

Haytham consults for retailers including Sephora France and Sharaf Group Dubai, and co-founded Keip — a SaaS platform for retail management. Every model in this course comes from live client work and real retail operations.

[ AUTO ]

AutoML for OTB forecasting — PyCaret included

Section 11 applies PyCaret AutoML to generate retail sales forecasts for OTB planning — running model comparison, blending, stacking, and future prediction automatically. Unique to this course.

TOOLS COVERED IN THIS COURSE

Microsoft Excel | Python | PyCaret (AutoML) | Inventorize | Jupyter Notebook / Anaconda

WHAT YOU WILL LEARN

✓ Calculate and benchmark operational, inventory, and financial retail metrics: conversion rate, inventory turnover, gross margin, cash-to-cash cycle time, assets metrics

✓ Apply retail metrics in Python: conversion rate, daily orders, average transaction value, and inventory turnover across a live retail dataset

✓ Build advanced Python data manipulation skills applied to retail data: pivot tables, joins, group-by, imputations, filtering, and melting

✓ Calculate retail buying metrics: mark-ups, margins, sales per square foot, actual vs budgeted stocks, shrinkage, ROI, markdown percentage, weeks of stock, and VMC

✓ Build a retail budget from A to Z: top-down and bottom-up forecasting, OTB planning, budget markdowns, seasonality modelling, and end-of-month stock calculation

✓ Plan OTB by category and apply two distinct OTB planning methods including variations to plan

✓ Build assortment plans: brand assortment, planograms, seasonal calendar, space planning, log model for sales vs space, and assortment optimisation in Python

✓ Model competing product pricing using multivariate regression, multinomial choice models, and logistic regression with Inventorize in Python

✓ Manage seasonal inventory: critical ratio in Excel and Python, MPN model applied across all products simultaneously

✓ Calculate safety stock and reorder points for all SKUs simultaneously: two methods, segmentation by service level, lead time variability

✓ Forecast retail sales using AutoML with PyCaret: model comparison, blending, stacking, and visualisation for OTB-ready outputs

COURSE CONTENT — 13 SECTIONS · 208 LECTURES · 19.5 HOURS · 55 DOWNLOADABLE RESOURCES

PHASE 1 — RETAIL METRICS & PYTHON FOUNDATIONS

SECTION 1: Retail metrics and benchmarking

Understand the numbers that drive retail performance. Apply benchmarking to Tesco, Walmart, and Kroger. Calculate operational metrics (conversion rate, average transaction value) and inventory metrics (turnover, days of stock) in Excel. Measure gross margin, net profit, assets metrics, and cash-to-cash cycle time — the metric that connects procurement, operations, and finance in one number. Graded quiz.

Excel

SECTION 2: Python crash course for retail professionals

No Python experience? No problem. Install Anaconda, explore Jupyter Notebook and Spyder, and learn Python from scratch with a retail data mindset: dataframes, arithmetic, lists, dictionaries, arrays, data import, subsetting, conditions, functions, mapping, and for loops. Includes the Inventorize package introduction. Two-part graded assignment.

Python Anaconda Inventorize

SECTION 3: Advanced data manipulation in Python for retail

The data engineering section that makes all retail analysis possible. Master pandas operations on real retail data: dropping duplicates and nulls, conversions, filtering, imputation, indexing and slicing, group-by, pivot tables with aggregate functions, melting, and all join types (left, inner, outer, full). Five-part graded assignment.

Python

SECTION 4: Retail operational metrics in Python

Apply Python to live retail operational analysis. Calculate conversion rate from transactional data, compute daily orders, build a dictionary for order analysis, calculate average transaction value and average selling price. Then build a stocks analysis: daily sales, common keys, year-week keys, average weekly performance, join sales with stocks, and calculate inventory turnover end-to-end. Graded assignment.

Python

PHASE 2 — RETAIL BUYING, BUDGETING & OTB

SECTION 5: Retail buying metrics and financial KPIs

The financial language of retail buying. Calculate mark-ups and margins, top-line and bottom-line performance, sales per square foot, actual vs budgeted stocks, conversion rate dynamics, average selling price, shrinkage rate, and ROI. Apply dollar markdown calculations, markdown percentage, weeks of stock (two-part), VMC analysis, and supplier-retailer margin dynamics. All in Python with real retail data. Graded quiz.

Python Excel

SECTION 6: Retail budgeting and OTB planning

The section no other retail course on Udemy covers at this depth. Build a retail budget from A to Z: understand budgeting components, apply top-down and bottom-up forecasting as the budget trigger, calculate OTB, plan budget markdowns and stock turns, model end-of-month stocks, integrate seasonality, and plan OTB by category using two distinct methods. Apply variations to plan and complete a graded budgeting assignment.

Excel Python

PHASE 3 — ASSORTMENT, PRICING & INVENTORY

SECTION 7: Assortment planning and space optimisation

Plan the right products in the right space. Understand brand assortment planning, store personas, and planograms. Apply seasonal calendar assortment. Model sales against retail space using a log model. Build and solve an assortment optimisation model in Python to maximise retail space profit. Derive coefficients, develop the optimisation function, and run the solver. Graded assignment.

Python Excel

SECTION 8: Competing product pricing with multinomial models and Inventorize

Price one product and you affect all the others. Model the relationships between competing products using multivariate regression and multinomial choice models (logit) in Python. Apply multi-competing product optimisation using Inventorize. This section is the pricing analytics module that most retail analytics courses do not offer.

Python Inventorize

SECTION 9: Seasonal inventory: critical ratio and MPN

Seasonal products require a different inventory logic. Manage seasonal products analytically: model seasonal demand, identify the point of maximum profit, apply critical ratio analysis in Excel and Python, prepare data for the Maximum Profit Newsvendor (MPN) model, create margin of error bounds, and apply MPN across all products simultaneously. Graded assignment.

Python Excel

SECTION 10: Safety stock and reorder point for all SKUs

Scale safety stock calculation to your full retail assortment. Apply two safety stock methods under demand and lead time variability. Prepare SKU data, calculate average demand and standard deviation, segment by service level, calculate reorder points for all SKUs simultaneously, and handle lead time variability in Python. Graded assignment with detailed explanation.

Python

PHASE 4 — AUTOML FORECASTING WITH PYCARET

SECTION 11: AutoML retail sales forecasting with PyCaret

The most technologically advanced section of the program — added by student demand in August 2023. Use PyCaret to build an AutoML retail forecasting pipeline: import and describe data, transform to monthly time series, extract seasonality features, generate coherent time series across all SKUs, impute missing data per level, split history, run model comparison experiments, create and tune the best models, blend and stack for improved accuracy, predict and visualise the future. Output feeds directly into OTB planning.

Python PyCaret

THIS COURSE IS NOT FOR YOU IF...

✗ You are looking for a general retail management overview — this course focuses specifically on retail planning analytics, budgeting, OTB, assortment, and inventory; a separate EP1 course covers retail fundamentals and sales forecasting

✗ You want a customer analytics or trade area modelling course — those topics are covered in EP3 of the RA Retail Series, available separately

✗ You have no interest in ever using Python — Phases 2–4 are entirely Python-based (a complete crash course is included from scratch in Section 2)

✗ You need an ERP or POS system implementation guide — this course builds analytical models and Python scripts, not software configuration skills

WHAT STUDENTS AND CLIENTS SAY

“This is a very heavy and extensive course with theories assisted by real-life examples. The depth of the content and the way it builds from Excel into Python is exactly right for retail professionals who want to move beyond spreadsheets.”

Tarlis — Verified Udemy student

“Haytham mentored me in my role of Head of Supply Chain Efficiency. He is extremely knowledgeable about supply chain concepts, latest trends, and benchmarks. His analytics-driven approach was very helpful to recommend and implement significant changes to our supply chain.”

Saify Naqvi — Head of Supply Chain Efficiency — Aster Group

“I participated in the Supply Chain Forecasting & Management training conducted by Haytham. It helped me enormously in my daily work in the purchasing department. Haytham has the pedagogy to explain very difficult calculations and formulas in a simple way. I highly recommend this training.”

Djamel Bouremiz — Purchasing Manager, Mineral Circles Bearings W.L.L.

WHO THIS COURSE IS FOR

Retail buyers and merchandisers

You manage OTB, assortment decisions, and buying budgets and want a data-driven framework — in both Excel and Python — to make every purchasing decision faster, smarter, and more defensible.

Retail planners and analysts

You build retail budgets, track inventory turnover, and plan assortments and want to automate these workflows in Python — scaling from one store to an entire chain in minutes.

Retail managers and operations professionals

You manage store performance metrics — conversion rate, shrinkage, sales per square foot, weeks of stock — and want a Python toolkit to calculate, compare, and report them automatically across your portfolio.

Supply chain and inventory professionals in retail

You manage stock replenishment, safety stock, and seasonal inventory for retail and want the quantitative models — critical ratio, MPN, reorder point for all SKUs — to make those decisions analytically.

Data analysts entering retail

You know Python or analytics and want a structured program that applies your skills to real retail problems — budgeting, OTB, assortment optimisation, competing product pricing, and AutoML forecasting.

Students and career changers into retail

You want a portfolio of working retail analytics models — budgets, OTB plans, assortment optimisation, Python inventory scripts — to stand out in retail, merchandise planning, and buying job applications.

REQUIREMENTS

● Motivation to learn retail analytics — no prior knowledge of retail, Python, or data science is assumed.

● No Python experience needed — Section 2 is a complete Python crash course built for retail professionals, with installation included.

● Basic familiarity with Excel is helpful for Sections 1, 5, and 6 — all Excel models are built step by step from scratch.

● A computer with Anaconda (free) installed — setup is fully guided in Section 2. PyCaret and Inventorize are both free and open-source.

WHAT IS INCLUDED

● 13 sections, 208 lectures, and 19.5 hours of on-demand content covering the complete retail planning and analytics workflow

● 55 downloadable resources: Excel workbooks, Python project files, retail datasets, and OTB planning templates

● Graded assignments in every section — all on real retail use cases, not synthetic data

● AutoML forecasting with PyCaret (Section 11) — added August 2023 by student demand, including model comparison, blending, stacking, and OTB-ready output

● Competing product pricing with Inventorize (Section 8) — multinomial logit and multivariate regression applied to retail assortment pricing

● Part of the RA Retail Series (Episode 2) — can be taken independently or combined with EP1 (retail management & sales forecasting) and EP3 (customer analytics & trade area modelling)

● Lifetime access to all content and any future updates to the curriculum

● 30-day money-back guarantee — no questions asked

● Certificate of completion upon finishing the course

YOUR INSTRUCTOR

Haytham Omar, Ph.D.

Supply Chain & Business Intelligence Consultant · Developer · Trainer — UAE & France · Founder, Rescale Analytics & Keip, Bordeaux

Haytham holds a Ph.D. in Supply Chain and Forecasting from the University of Bordeaux and a Master of Science in Global Supply Chain Management from Bordeaux École de Management. He is the co-founder of Keip  — a SaaS platform built for retail management and analytics — and an active consultant who works with retailers including Sephora France and Sharaf Group Dubai. Every retail framework, assortment model, OTB tool, and pricing technique in this course has been applied in live client engagements or built into Keip for retail professionals.

He has trained over 70,000 supply chain and retail professionals across 70+ workshops in the UAE. Additional consulting clients include Sharaf Group, Aster Group, DNO, PWC Training Academy Dubai, Qarar, Lamprell, and the Higher College of Technology. He is also the creator of the Inventorize package for Python and R — used by over 90,000 supply chain and retail professionals worldwide.

This course is Episode 2 of the RA Retail Series — a three-part program that together forms the most comprehensive retail analytics curriculum on any online learning platform. Episode 2 focuses on retail planning, assortment analytics, budgeting, and Python. It can be taken independently or as part of the full series.

Stop managing retail by intuition. Start planning it by data.

13 sections · 19.5 hours · Excel + Python · OTB + Budgeting · AutoML · Highest Rated · Keip SaaS founder