RA: Retail Customer Analytics and Trade Area Modeling.
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“This is one of the three courses in the Retail Series by RA, each course can be taken independently.”
Master Retail management and analytics with Excel and Python
Retailers face fierce competition every day and keeping up with the new trends and customer preferences is a guarantee for excellence in the modern retail environment. one Keyway to excel in retail management is utilizing the data that is produced every day. It is estimated that We produce an overwhelming amount of data every day, roughly 2.5 quintillion bytes. According to an IBM study, 90% of the world’s data has been created in the last two years.
Retail analytics is the field of studying the produced retail data and making insightful data-driven decisions from it. as this is a wide field, I have split the Program into three parts. in this course, we focus on the customer analytics part of retail. Understanding the customer is key for maintaining loyalty and developing products to boost retail business and profitability.
RA: Retail Customer Analytics and Trade Area Modeling.
1- Understanding the importance of customer analytics in retail.
2- Manipulation of Data with Pandas.
3-Working with Python for analytics.
5- Trade area modeling
6- Recommendation systems
7- Customer lifetime value prediction
8- Market Basket analytics
9- Churn prediction
Don’t worry If you don’t know how to code, we learn step by step by applying retail analysis!
*NOTE: Full Program includes downloadable resources and Python project files, homework and Program quizzes, lifetime access, and a 30-day money-back guarantee.
Who this Program is for:
· If you are an absolute beginner at coding, then take this Program.
· If you work in Retail and want to make data-driven decisions, this Program will equip you with what you need.
· If you are switching from Excel to a data science language. then this Program will fast-track your goal.
· If you are tired of doing the same analysis again and again on spreadsheets and want to find ways to automate it, this Program is for you.
Program Design
the Program is designed as experiential learning Modules, the first couple of modules are for retail fundamentals followed by Python programming fundamentals, this is to level all of the takers of this Program to the same pace. and the third part is retail applications using Data science which is using the knowledge of the first two modules to apply. while the Program delivery method will be a mix of me explaining the concepts on a whiteboard, Presentations, and Python-coding sessions where you do the coding with me step by step. there will be assessments in most of the sections to strengthen your newly acquired skills. all the practice and assessments are real retail use cases.
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1IntroductionVideo lesson
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2Tesco and Andrew PoleVideo lesson
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3False PositivesVideo lesson
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4WalmartVideo lesson
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5Notable mentionsVideo lesson
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6Why Customer analyticsVideo lesson
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7CurriculumVideo lesson
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8The retail CustomerVideo lesson
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9types of retail customersVideo lesson
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10Types of retail customrsVideo lesson
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11Why we need customer analyticsVideo lesson
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12types of retail DataVideo lesson
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13Sales Data Vs Market basket DataVideo lesson
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14Retail Data structreVideo lesson
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15Customer analytics and machine learning applicationsVideo lesson
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16Quiz on section 1Quiz
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17SummaryVideo lesson
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24IntroVideo lesson
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25Data FramesVideo lesson
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26Arithmetic Calculations in PythonVideo lesson
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27ListsVideo lesson
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28DictionariesVideo lesson
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29ArraysVideo lesson
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30Importing Data in PythonVideo lesson
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31Subsetting DataFramesVideo lesson
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32ConditionsVideo lesson
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33Writing FunctionsVideo lesson
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34MappingVideo lesson
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35For LoopsVideo lesson
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36For looping a functionVideo lesson
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37Mapping on DataframeVideo lesson
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38For Looping a DataFrameVideo lesson
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39SummaryVideo lesson
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40AssignmentVideo lesson
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41Assignment Answer 1Video lesson
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42Assignment Answer 2Video lesson
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43InroVideo lesson
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44Dropping Duplicates and NAsVideo lesson
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45Conversions lectureVideo lesson
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46ConversionsVideo lesson
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47FilterationsVideo lesson
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48ImputationsVideo lesson
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49Indexing TutorialVideo lesson
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50Slicing indexVideo lesson
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51Manipulation lectureVideo lesson
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52GroupbyVideo lesson
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53Slicing the GroupbyVideo lesson
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54Dropping levelsVideo lesson
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55The proper formVideo lesson
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56Pivot tablesVideo lesson
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57Aggregate function in pivot tableVideo lesson
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58Melting the DataVideo lesson
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59Left joinVideo lesson
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60inner & outer joinVideo lesson
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61Joining in PythonVideo lesson
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62inner, left join and full join(outer)Video lesson
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63SummaryVideo lesson
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64AssignmentVideo lesson
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65Assignment answer 1Video lesson
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66Assignment answer 2Video lesson
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67Assignment answer 3Video lesson
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68Assignment answer 4Video lesson
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69Assignment answer 5Video lesson
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70Tade Area ModellingVideo lesson
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71IntroductionVideo lesson
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72Different trade area modellingVideo lesson
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73Drive time and Zip codesVideo lesson
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74The huff modelVideo lesson
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75Some considerations about trade area modelingVideo lesson
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76Summary of a Huff modelVideo lesson
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77Huff ModelVideo lesson
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78Example DemonstrationVideo lesson
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79Scaling attractivenessVideo lesson
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80Developing Huff modelVideo lesson
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81The Huff model in PythonVideo lesson
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82Reading the data in pythonVideo lesson
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83Getting the upper termVideo lesson
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84Probability per Customer CommunityVideo lesson
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85Where should I locate my store ?Video lesson
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86AssignmentVideo lesson
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87Assignment AnswerVideo lesson
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88SummaryVideo lesson
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