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E-raamat: Analytics for Retail: A Step-by-Step Guide to the Statistics Behind a Successful Retail Business

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 25-Jun-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484278307
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Analytics for Retail: A Step-by-Step Guide to the Statistics Behind a Successful Retail BusinessSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 25-Jun-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484278307
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Examine select retail business scenarios to learn basic mathematics, as well as probability and statistics required to analyze big data. This book focuses on useful and imperative applied analytics needed to build a retail business and explains mathematical concepts essential for decision making and communication in retail business environments.  

Everyone is a buyer or seller of products these days whether through a physical department store, Amazon, or their own business website.  This book is a step-by-step guide to understanding and managing the mechanics of markups, markdowns, and basic statistics, math and computers that will help in your retail business. You'll tackle what to do with data once it is has accumulated and see how to arrange the data using descriptive statistics, primarily means, median, and mode, and then how to read the corresponding charts and graphs. 

Analytics for Retail is your path to creating visual representations that powerfully communicate information and drive decisions.
 
What You'll Learn
  • Review standard statistical concepts to enhance your understanding of retail data
  • Understand the concepts of markups, markdowns and profit margins, and probability  
  • Conduct an A/B testing email campaign with all the relevant analytics calculated and explained
Who This Book Is For

This is a primer book for anyone in the field of retail that needs to learn or refresh their skills or for a reader who wants to move in their company to a more analytical position. 

About the Author ix
About the Technical Reviewer xi
Introduction xiii
Chapter 1 The Basics of Statistics 1(14)
Descriptive Statistics
2(3)
Measures of Central Tendency (Mean, Median, Mode)
5(1)
Measures of Variability (Range, Variance, Standard Deviation)
6(4)
Example of Standard Deviation and Variance
8(2)
Computational Example
10(3)
Cleaning the Data Using Descriptive Statistics
13(1)
Summary
14(1)
Chapter 2 The Normal Curve 15(8)
A Statistical Introduction
16(1)
An Important Theorem and a Law
16(2)
The Standard Normal Curve and Its Generalizability Factor
18(2)
How the T-Distribution Converges to the Normal Curve
20(1)
Summary
21(2)
Chapter 3 Probability and Percentages, and Their Practical Business Uses 23(14)
What Percentages Tell Us, and Their Uses
24(1)
Hints to Use to Solve Percent Problems
25(1)
General Business Examples
26(2)
Example 1
26(1)
Example 2
26(1)
Example 3
27(1)
Example 4
27(1)
Example 5
27(1)
Real-Life Probability and Percent Examples: Markup
28(3)
Example 1
29(1)
Example 2
29(1)
Example 3
29(1)
Example 4
30(1)
Example 5
30(1)
Example 6
30(1)
Example 7
30(1)
Example 8
31(1)
Real-Life Percent Examples: Discount
31(2)
Example 1
32(1)
Example 2
32(1)
Example 3
32(1)
Example 4
33(1)
Real-Life Percent Examples: Profit Margin
33(2)
Example 1
33(1)
Example 2
34(1)
Example 3
34(1)
Example 4
35(1)
Summary
35(2)
Chapter 4 Retail Math: Basic, Inventory/Stock, and Growth Metrics 37(16)
Financial Statements at a Glance
39(1)
Retail Math Basic Metrics
40(7)
Inventory/Stock Metrics
47(4)
Growth Metrics
51(1)
Summary
52(1)
Chapter 5 Financial Ratios 53(12)
Financial Ratios at a Glance
53(1)
Liquidity Ratios
54(4)
Debt or Leverage Ratios
58(2)
Profitability Ratios
60(2)
Efficiency Ratios
62(1)
Summary
63(2)
Chapter 6 Using Frequencies and Percentages to Create Stories from Charts 65(12)
Frequencies: How to Use Percentages
66(5)
Simple Charts: Horizontal, Vertical, and Pie
71(5)
Horizontal and Vertical Bar Charts
73(2)
Pie Charts
75(1)
Summary
76(1)
Chapter 7 Hypothesis Testing and Interpretation of Results 77(6)
Step 1: The Hypothesis, or Reason for the Business Question
78(1)
Step 2: Confidence Level
79(1)
Step 3: Mathematical Operations and Statistical Formulas
80(1)
Step 4: Results
81(1)
Step 5: Descriptive Analysis
81(1)
Summary
81(2)
Chapter 8 Pearson Correlation and Using the Excel Linear Trend Equation and Excel Regression Output 83(24)
Pearson Correlation Defined
83(2)
Hypothesis Testing and Descriptive Steps for a Pearson Correlation
85(8)
Step 1: The Hypothesis, or the Reason for the Business Question
85(1)
Step 2: Confidence Level
86(1)
Step 3: Mathematical Operations and Statistical Formula
87(3)
Step 4: Results
90(2)
Step 5: Descriptive Analysis Interpretation of Results
92(1)
Three Examples Using Small Datasets
93(13)
Step 1: Hypotheses Are All the Same
93(1)
Step 2: Level of Confidence
93(1)
Step 3: Mathematical Operations and Statistical Formula
94(11)
Step 4: Results
105(1)
Step 5: Descriptive Analysis
105(1)
Summary
106(1)
Chapter 9 Independent T-Test 107(8)
Independent T-Test at a Glance
107(1)
Hypothesis Test
108(1)
Step 1: The Hypothesis, or the Reason for the Business Question
109(1)
Step 2: Confidence Level
109(1)
Step 3: Mathematical Operations and Statistical Formula
110(3)
Step 4: Results
113(1)
Step 5: Descriptive Analysis
114(1)
Summary
114(1)
Chapter 10 Putting It All Together: An Email Campaign 115(20)
Test Goal
115(1)
Method
116(1)
Data Constants
117(2)
Type of Shopper Targeting
118(1)
Time of Year and Duration
118(1)
Cost of Dresses
118(1)
Medium Type
118(1)
Steps to Assess the Success of the Email Campaign
119(1)
Statistics Conducted: Results and Explanations
120(7)
Independent T-Test 1: Conversion Rate Between Models and No Models
121(1)
Independent T-Test 2: Revenue Between Models and No Models
122(1)
Independent T-Test 3: Dresses Sold Between Models and No Models
123(1)
Independent T-Test 4: Orders of Dresses Between Models and No Models
124(1)
Pearson Correlation by Model: Relationship Between Conversion Rate and Revenue
125(2)
Sell-Through Rate for Model and No Model
127(3)
Average Order Value for Model and No Model
128(1)
Total Metrics on Key Performance Indicators for Email Campaign
129(1)
Average Click-Through Rate
130(1)
Type of Model
130(1)
Profit per Dress
130(1)
ROI and ROAS
131(1)
Summary and Discussion on Results
132(1)
Thoughts for Further Analyses
132(1)
Summary
133(2)
Chapter 11 Forecasting: Planning for Future Scenarios 135(8)
Regression at a Glance
136(1)
Establishing Data Collection
136(1)
Predictive Analysis Using the Spreadsheet
137(2)
Scenario Analysis
139(1)
Campaign Analysis and Prediction
140(1)
Consumer Analysis and Prediction
141(1)
Summary
142(1)
Chapter 12 Epilogue 143(4)
Index 147
Rhoda Okunev is an Associate in Professional Studies at Columbia Universitys School of Applied Analytics department.  Rhoda teaches math and statistics at Nova Southeastern University.  Rhoda taught at the Fashion Institute of Technology Continuing Education department in the Retail Analytics department where she created her own Applied Analytics course.  She also taught math and statistics at the Fashion Institute of Technology in New York. 





 





Rhoda has a Masters in Mathematics from the Courant Institute at New York University, a Masters in Biostatistics from Columbia University, and a Masters in Psychology from Yeshiva University.  She also has an Advanced Certification in Finance from Fordham University. Rhoda has worked in portfolio management and market risk for over 10 years at a rating agency, clearing house and bank.  Rhoda has also extensive experience in research and statistical programming at Harvard University, Massachusetts General Hospital, Columbia Presbyterian Hospital, Cornell Medical Center, Massachusetts Department of Public Health, and Emblem Health (HIP). 
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