
Store P&L line by line: from net sales to store contribution
Every line as a share of sales, percent vs percentage point
Controllable and non-controllable lines, allocations below the line
Store against the chain average: the gap bridge
The one-point lever: what 1 p.p. of any line is worth
Tools in this lecture (7):
Store P&L line builder
Store vs chain gap bridge
Lever tests: shrink, hours, conversion
One-point hypothesis with check lanes
Rate-reading checklist
Three questions to ask finance
Seven numbers to bring from your last quarter
Sales tree: traffic × conversion × average ticket
Five daily KPIs on one board, UPT and sales per labor hour
Reading the board by day and by hour
Store against the chain: the 2×2 placement
Cause tree for a falling number and a Saturday experiment
Tools in this lecture (6):
Week board of five KPIs
Conversion by hour with a flag
Store vs chain 2×2 placement
Lever calculator: conversion, UPT, ticket
Cause tree
Saturday experiment: cost and possible sales
The hours budget as a fixed wallet
Hourly traffic forecast and the floor coverage rule
Gap check: short and idle hours in the current schedule
Rebuilding shifts around the peaks inside the budget
Publishing rules: notice, changes, rest, predictive scheduling laws
Tools in this lecture (5):
Hourly traffic forecast
Coverage rule and hours wallet
Schedule gap check
Rebuild with labor $ and labor %
Publishing checklist
Shelf check: on-shelf availability on 50 positions
Safety stock, reorder point and order quantity in case packs
Phantom stock: book against shelf
Stock health: turns, GMROI, weeks of supply, dead stock
SKU matrix: sell-through × unit margin
Tools in this lecture (4):
SKU card: safety stock, reorder point, order
Shelf check and phantom stock
Stock health: turns, GMROI, weeks of supply
SKU matrix
Shrink at retail value: book minus physical
Known and unknown loss in the write-off log
Five sources of loss and a risk map: how often × how much
POS exception report: a signal, not a verdict
30-day shrink plan with owners
Tools in this lecture (5):
Shrink at retail value
Known and unknown loss
Risk map
POS exception report
30-day shrink plan
Hiring need from turnover and the holiday season
Applicant funnel: from hires back to applicants
Role profile with must-haves and an honest job preview
Structured interview card with weighted scores
First 7 days plan for a new hire
Tools in this lecture (5):
Hiring need and replacement cost
Applicant funnel
Role profile
Structured interview card
First 7 days plan
The 10-minute huddle script
Will × skill matrix for the floor team
Coaching at the shelf with four questions
Feedback by situation, behavior and impact; the hard conversation
AI role-play with a store associate and a debrief
Tools in this lecture (5):
10-minute huddle script
Shelf coaching card
Hard talk script
AI role-play prompt: associate
Debrief prompt and verdict
Service numbers: complaints, NPS, money at risk
Customer effort instead of chasing "wow"
Service standard for five steps of the visit
Complaint recovery script in five moves and authority limits per role
AI role-play with a difficult customer and a debrief
Tools in this lecture (5):
Service standard sheet
Complaint recovery script
Authority limit ladder
AI role-play prompt: customer
Debrief prompt and verdict
Store walk route through seven zones
Zone score on a 0–2 scale with N/A and the critical-item rule
Price check and shelf check on a 50-item sample
Findings with one owner and a due date
Friday re-walk on the same route
Tools in this lecture (4):
Seven-zone walk with scores
Price and shelf sample
Findings board
Friday re-walk
A prompt that returns a file, not advice; a store project with files
Deep research with an edited plan
Forecast, schedule draft and a mini-app from your data
Shelf photo review, customer reply and training card as checked drafts
A Monday routine on a schedule and a time-saved ledger
Tools in this lecture (11):
Prompt: weekly district manager report
Prompt: store project instructions
Prompt: deep research brief
Prompt: traffic forecast
Prompt: floor schedule draft
Prompt: mini-app calculator
Prompt: shelf photo review
Prompt: customer reply with three checks
Prompt: training card
Prompt: Monday routine
Time-saved ledger
This course contains the use of artificial intelligence
Out of a $40 basket, the store in this course keeps $3.66.
The store manager who knows which line ate the rest gets the next store.
Most store managers run the floor, not the numbers. The P&L arrives from head office ready-made, the schedule is cut by 10% in an email, shelves look full while the system says otherwise, and a shrink figure appears once a year with nobody able to explain it. You answer for a result you were never shown how to take apart.
This course gives you the other half of the job. You step into a simulation: a chain of 40 home-goods stores, your own Store 17, and a district manager who writes every Monday. Each lesson starts with her email and ends with a file you send back by Friday. Then you open the same file for your own store.
What changes after the course
Who teaches it
Mike has 1,800,000+ course enrollments and 170,000 students, about 200 books on Amazon, and the PHRi, SHRM-CP and HCI sHRBP certifications. He built the HR system of the unicorn Preply and worked at Wargaming and other fast-growing companies. The people side of this course, from hiring to hard conversations, comes from that practice; the store numbers come from public company reports and retail research, and every source is named on the slide.
How the course moves
First you take the store apart by numbers: the P&L, the daily KPI board, labor hours and inventory. Then you work on people: shrink as behavior, hiring for the season and leading on the floor. Finally you work on the customer and the routine: service recovery, the store walk and an AI assistant for the week.
What's included
Every month without these numbers is a month your district manager reads them for you. Other managers in your chain are already learning to defend their stores with data.
Enroll now and start your first lesson today.