Magento Business Intelligence Post-Holiday Webinar

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Magento Business Intelligence Post-Holiday Webinar

Données & analyses

© 2016 Magento, Inc. Page | 1
Analyzing Your Holiday
Season

© 2016 Magento, Inc. Page | 2
Executive Overview
1. Holiday Trends
2. Finding your “Holiday Season”
3. How sensitive is your business to the holiday season
4. What is the value of your holiday customers
5. What was the cost of acquiring these customers

© 2016 Magento, Inc. Page | 3
An overview of the 2016 Holiday Benchmark
Holiday Trends

© 2016 Magento, Inc. Page | 4
Benchmark Overview
1. https://rjmetrics.com/resources/reports/the-ecommerce-holiday-
customer-benchmark/

© 2016 Magento, Inc. Page | 5
Key Takeaways

© 2016 Magento, Inc. Page | 6
Finding Your Holiday
Season

© 2016 Magento, Inc. Page | 7
EOY isn’t always the “Holiday Season”

© 2016 Magento, Inc. Page | 8
Identifying your “Holiday Season”

© 2016 Magento, Inc. Page | 9
How Holiday Sensitive
is Your Business?

© 2016 Magento, Inc. Page | 10
Percent of annual sales acquired during holidays

© 2016 Magento, Inc. Page | 11
Percent of new customers acquired during
holidays

© 2016 Magento, Inc. Page | 12
The Value of a Holiday
Customer

© 2016 Magento, Inc. Page | 13
AOV and LTV
Average Order Value Customer Lifetime Value

© 2016 Magento, Inc. Page | 14
Repeat Order Probability

© 2016 Magento, Inc. Page | 15
How did your Business
Acquire Holiday Sales

© 2016 Magento, Inc. Page | 16
Marketing Costs
Cost of Acquiring a Customer
• Marketing Data
• Facebook Integration
• AdWords Integration

© 2016 Magento, Inc. Page | 17
Coupon Analysis
Coupon % Analysis Library
• Coupon Code Analysis
• Instructions for creating 16 reports
• Usage Rates
• Discount amounts
• Repeat Order Probability
• LTV

© 2016 Magento, Inc. Page | 18
Next Steps for your Analyses
1. The holidays affect every business differently
2. Suggested Analyses
1. Finding your Holiday
2. Holiday Sensitivity
3. Customer Value
4. Acquisition Costs
3. Need a hand?
1. Support@RJMetrics.com
2. Professional Services

© 2016 Magento, Inc. Page | 19
Questions?

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怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

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怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

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Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Valters Lauzums

7. Epi of Chronic respiratory diseases.ppt

7. Epi of Chronic respiratory diseases.ppt

7. Epi of Chronic respiratory diseases.ppt

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

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Sequential and reinforcement learning for demand side management by Margaux B...

Sequential and reinforcement learning for demand side management by Margaux B...

Paris Women in Machine Learning and Data Science

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

Context 1. Housing Agent collected resale prices on HDB apartments in Singapore. Objective 2. To predict resale prices in to advise his potential clients. Strategies 3. Explore & Clean data for analysis. 4. Perform K-Means Clustering, in Orange, to find possible segments in the customer data. 5. Tune the model to improve its performance. 6. Visualise the findings, share conclusions, and give insight-driven recommendations. Author: Anthony mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

ThinkInnovation

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一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

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Magento Business Intelligence Post-Holiday Webinar

1. © 2016 Magento, Inc. Page | 1 Analyzing Your Holiday Season

2. © 2016 Magento, Inc. Page | 2 Executive Overview 1. Holiday Trends 2. Finding your “Holiday Season” 3. How sensitive is your business to the holiday season 4. What is the value of your holiday customers 5. What was the cost of acquiring these customers

3. © 2016 Magento, Inc. Page | 3 An overview of the 2016 Holiday Benchmark Holiday Trends

4. © 2016 Magento, Inc. Page | 4 Benchmark Overview 1. https://rjmetrics.com/resources/reports/the-ecommerce-holiday- customer-benchmark/

5. © 2016 Magento, Inc. Page | 5 Key Takeaways

6. © 2016 Magento, Inc. Page | 6 Finding Your Holiday Season

7. © 2016 Magento, Inc. Page | 7 EOY isn’t always the “Holiday Season”

8. © 2016 Magento, Inc. Page | 8 Identifying your “Holiday Season”

9. © 2016 Magento, Inc. Page | 9 How Holiday Sensitive is Your Business?

10. © 2016 Magento, Inc. Page | 10 Percent of annual sales acquired during holidays

11. © 2016 Magento, Inc. Page | 11 Percent of new customers acquired during holidays

12. © 2016 Magento, Inc. Page | 12 The Value of a Holiday Customer

13. © 2016 Magento, Inc. Page | 13 AOV and LTV Average Order Value Customer Lifetime Value

14. © 2016 Magento, Inc. Page | 14 Repeat Order Probability

15. © 2016 Magento, Inc. Page | 15 How did your Business Acquire Holiday Sales

16. © 2016 Magento, Inc. Page | 16 Marketing Costs Cost of Acquiring a Customer • Marketing Data • Facebook Integration • AdWords Integration

17. © 2016 Magento, Inc. Page | 17 Coupon Analysis Coupon % Analysis Library • Coupon Code Analysis • Instructions for creating 16 reports • Usage Rates • Discount amounts • Repeat Order Probability • LTV

18. © 2016 Magento, Inc. Page | 18 Next Steps for your Analyses 1. The holidays affect every business differently 2. Suggested Analyses 1. Finding your Holiday 2. Holiday Sensitivity 3. Customer Value 4. Acquisition Costs 3. Need a hand? 1. Support@RJMetrics.com 2. Professional Services

19. © 2016 Magento, Inc. Page | 19 Questions?