1. Houston E-Retailers Presented BY: Bala AnuDeep Guduri (LEAD)
Kavya Hegde
Divya Gangwani
Suhas Malavalli

2. Background
HOUSTON E-RETAILERS, INC is a startup E-Retail store based in Houston, Texas and carries various products from different famous franchisees for a number of internationally recognized brands like Dr. Pepper under Beverages, Proctor and Gamble for Toys and Cosmetics and many more.
The company is mainly an E-Retail business which is looking on to expand its customer base and also grow their business and compete amongst their competitors in the E-Retail market.

3. Business Need To build a long-term relationship with the customer.
To run a profitable operation which typically means increasing revenue while limiting the expense.
To analyze the transactional data and finally drive the company towards the success by making effective strategic decisions.
Use effective tools or techniques which help in analyzing the data and produce reports to know the trending for past and the upcoming years.

4. Why Data Warehousing ?
Houston e-retailers being a startup company needed an appropriate technique or tool to drive the company towards success.
Hence a well designed data warehousing that would feed our business with the right information at the right time in order to make strategic decisions in e-retail environment is needed.
This technique is useful for generating reports and analyzing the collected data over years.

5. How Would Houston E-Retailers Benefit from Data Warehouse?
This would make us aware of the past and future trends with regards to customer purchase, the product sales and many more by analyzing the data collected over the years.
Knowing the sale of a particular product under a particular category at a given time that would facilitate in increasing the revenue for the future years.
Finally, an important benefit being that its facilitates or company to provide the right product to the customer when they want.

6. Reports Overall yearly sales report based on Product Category
Sales Report based on Gender
Sales Report based on Gender and Customer Age group
Quarterly Sales Report based on Customer Age group and Gender
Sales Report based on Product category, Customer Age group and Gender

7. Dimensional Model Star SCHEMA Customer Product ProductCategory
Fact_Sales
Vendor
Date

8. Dimensional MODEL

9. Dimension Tables
Customer Dimension: (In SQL Server and Access DB)

10. Dimension Tables
Vendor Dimension:(In SQL Server and Access DB)

11. Dimension Tables
Date Dimension: (In SQL Server and Access DB)

12. Dimension Tables
Product Dimension: (In SQL Server and Access DB)

13. Dimension Tables
Product Category Dimension: (In SQL Server and Access DB)

14. Dimension Tables
Fact_Sales Dimension: (In SQL Server and Access DB)

15. Workflow EXCEL Access DB SQL Server & BI
E-Retail data collected from an online site
Files where in Excel format
Created few of our own tables like Product Category, Fact Sales
Access DB
Imported the Excel files into Access DB
Created their Relationship in form of a star schema
SQL Server
& BI
Access DB files imported into MS SQL Sever (SBUS-DB) as Ecommerce-Retailers.
Using Visual Studio BI, created a Cube for our Ecommerce-Retailers database.
Generated reports appropriately as required.

16. Date Dimension
Created hierarchies in order to navigate easily between various level attributes within the date dimension.
Further an attribute relationship was created within the hierarchy.

17. Product Dimension
Created hierarchies in order to navigate easily between various level attributes within the date dimension.
Further an attribute relationship was created within the hierarchy.

18. Customer Dimension Named Calculation:
Created an Age column in the Customer dimension

19. Reports Following reports have been generated using the cube:
Sales Report
Gender and Age Based Sales Report
Product Sales Report

20. Report 1 Yearly Sales Report based on Product Category Conclusion:
From the report we can infer that cosmetics sales are high in a fiscal year which is 7, with is 21 % of over all sales of “Houston E-Retailers “

22. Report 2 Sales Report based on Gender Conclusion:
From the report we can infer that female customers are making more purchases than male customers and the purchasing trend for this female group is increasing.

23. Report 3 Sales Report based on Gender and Customer age group
Conclusion:
From the report we can infer that Female customers under the age group generate more sales than the other group.

25. Report 4 Quarterly Sales Report based on Customer Age group and Gender
Conclusion:
From this we can infer that the customers under the age group for both the genders have to be looked up upon to retain them in the future years.

27. Report 5
Sales Report based on Product Category and Customer Age group

29. Conclusion
We have build a data warehouse specifically for our company, the Houston E- Retailer from the data that we collected. 
We have successfully developed a cube which gives an organized and summarized  data which is current or collected over the years.
We have also learnt generating different reports according to our requirement and analyzing the measures for a given criteria.
On an overall note, the project was really a learning and interesting experience from the start to finish.

30. Thank You 
Comments or Queries?