Case Study: SAP BW Data Mining (Association Analysis)

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1 Case Study: SAP BW Data Mining (Association Analysis) Product SAP Netweaver Release 2004s Level Undergraduate Focus BW Data Mining Author Paul Hawking Robert Jovanovic Version 1.0 MOTIVATION The management of ShopEasy supermarkets believes their policies on in store product placement, cross product promotion and pricing could be enhanced if they had a better understanding of the buying behaviour of their customers. Management has decided that the best way to obtain this knowledge is to record all the purchases made by its customers and then analyze the recorded data using a statistical technique known as association analysis You have been employed as a data mining developer at the company. You have been given the task of creating the data mining model, loading the data and executing the analysis process. PREREQUISITES Required Business Content has been activated and appropriate authorizations have been implemented. Access to the shoppingdataset.csv file is required. Students must obtain their login details from their workshop leader. HINTS This case study was created using SAPGUI 710 Patch 7, IE 7.0, BEx BI- Addon 7.X SP4, 2008 SAP AG Page 1

2 SAP BW Data Modelling (Association Analysis) Task:. Student will need to create an appropriate association analysis model in order to evaluate the supermarket data and then use the Analysis Process Designer (APD) to map the model with the recorded supermarket data and execute the analysis. Students will then be required to analyze and interpret the derived results Association Analysis The aim of association analysis is to find and extract patterns in stored data. The patterns are expressed in terms of rules. The rules highlight dependencies between data items (e.g., between products, materials, etc.). The dependencies are expressed in the form of leading and dependent items and are calculated using statistical terms such as support, confidence and lift. A typical rule would be expressed in the following format. If a customer purchases product X then they are n% likely to also purchase product Y. Product X would be considered as being the leading item and product Y the dependent item. Based on the above analysis, When beer (leading item) is purchased there is Expressed in terms of confidence; a % chance a frozen meal (dependent item) is also purchased. Expressed in terms of lift; when beer is purchased, a frozen meal purchase is 1.8 times more likely. Support indicates how often the leading and dependent items are purchased together in the data space. In this instance, beer and a frozen meal were purchased together 18.1 % of the time SAP AG Page 2

3 Association analysis can assist in management strategies relating to cross product selling, product marketing and in-store placement. Association analysis may also be referred to as market basket analysis or affinity analysis. 1 Analyzing the Data Source Data mining models can use data from a variety of sources for its analysis. In SAP BW, data can be obtained from data sources that include master data, InfoProviders, text files, queries, and operational data stores. A data mining model is created and structured using an appropriate data source. It is therefore critical when building a data mining model to know the field names and data delimiter used in the data source file. The data source to be used in this exercise is text file called shoppingdataset.csv.. The first row of the file is a field header which identifies the field names used in the file. The field names used to represent the data in this data file are called 0COOrder and 0Material and a comma is used as the delimiter to separate individual data items. 0COORDER,0MATERIAL 10150, softdrink Field Header 10150, fruitveg 10360, fish 10360, cannedveg 10360, beer Field Delimiter Note: Field names used in a data source file must correspond to existing InfoObjects within a BW system. It is important to understand how data is stored and structured within a data file. The data structure for the file used in this module is defined as followings. The field 0COOrder is used to collectively group what was purchased in a customer order and the field OMaterial is used to identify the exact name of the product (material) that was purchased. 0COORDER,0MATERIAL 10150, softdrink # , fruitveg 10360, fish # , cannedveg 10360, beer #1 shows that on order two products were purchased; soft drink and fruitveg and #2 shows that three items were purchased on order 10360; fish, cannedveg and beer. Note: Purchase quantities were not recorded in the data file and therefore are not part of this analysis SAP AG Page 3

4 2 Creating an Association Model 2.1 Accessing SAP Data Mining Workbench 1. Logon to SAP BW. 2. Click the icon to display the full SAP Menu. 3. Double-click.icon to expand this directory. 4. Double-click to access the Data Mining Workbench. The Data Mining Workbench screen appears as follows: The workbench includes a list of Data Mining methods that can be used to analyze data commonly stored in business applications. These methods include classification (decision trees), clustering, association analysis, approximation (scoring: weighted score table and regression), and further analyses techniques such as ABC classification. The various methods can be classified into two distinct categories, predictive or informative. 2.2 Creating an Association Model 1. Click the icon next to heading to expand the directory. 2. Right click the heading to display its context menu. 3. Click Create Model to begin creating an Association model. The Create Model dialog screen appears as follows; 2008 SAP AG Page 4

5 4. Type XQNNNA1 as the Model Name (where NNN represents your login number). 5. Type XQNNN Shopping Basket Analysis for its description. 6. Set the radio button in the Options tab to Manual. 7. Click to accept the entry. The Create Model screen will appear as follows: You now need to select the fields (InfoObjects) needed for your model. The fields selected will need to match those identified in the data source file shoppingdataset.csv. The following InfoObjects are going to be used in this model. 0COOrder (Order Number) and 0Material (Material). 8. Click the icon to insert a row for the first InfoObject (Material). 9. Type Material in the Name field. 10. Press <TAB> to move to the next field. 11. Type Product Description in the Description field. 12. Press <TAB> to move to the next field. 13. Type 0Material in the InfoObject field. Note InfoObject used in this section must already exist within the SAP BW system. If you are unsure of its name then you could use to search for it. 14. Press <TAB> to move to the next field. Chooses Item for materials Notice that properties (data type and length) of the InfoObject are automatically populated. The Content Type option defines how each field is to be treated within the model. There are three possible options; transaction, item and transaction weight. Transaction: Represents the field that groups specified items together. (OrderNO, TransactionID, BasketID etc) Item : Identifies the field on which the analysis is to based. Transaction Weight: Provides each transaction with a weighting SAP AG Page 5

6 15. Click to insert another row. 16. Type the following details: Name Description InfoObject Content Type OrderNO Order Number 0COOrder Transaction After completing this step the field assignment for the Association model show appear as follows; 17. Click the tab to set the following parameters. Support can be used to help determine how useful a product association rule is. The probability that a customer will buy both beer and potato chips is referred to as the support for the rule. Support measures how often items occur together and expresses this as a percentage of the total transactions. You should target a support in a range of (50%). This indicates it is not an unusual event, nor one that happens all the time. For the purpose of this exercise you will set the Support at 2%. 18. Type 2.00 in the Minimum Support (%) field. Confidence (or predictability) measures how dependent a particular item. For example one could examine the dependency between beer and potato chips. Confidence is expressed in the following terms; if a customer purchases product X, they also likely to purchase product Z 60% of the time. In this case, 60% is a confidence measure. For the purpose of this exercise you will set the Confidence at 10% SAP AG Page 6

7 19. Type in the Minimum Confidence (%) field. The association model uses leading and dependent items. Leading and dependent items can be expressed in the following context; If a leading item has been purchased what is the likelihood the following dependent items is also purchased. If a customer purchases beer (leading item or antecedent) what is the likelihood they will also purchase potato chips (dependent item i.e. consequent). The relationship between leading and dependent items is not limited to one to one. The parameter Leading Depth can be set to identify the number of products to be considered together as leading items and the parameter Dependent Depth can be used to set the number of dependent items to be considered in the model s analysis. Leading Items Beer Beer, Wine Beer Dependent Items Potato Chips Potato Chips Potato Chips, Chocolate 20. Type 3 in the Maximum Leading Depth field 21. Type 1 in the Maximum Dependent Depth field. 22. Click to return to the previous screen. You now need to save your model. 23. Click to save your model. A message appears in the Status Bar. 24. Click to activate the model. A log screen will appear indicating the status of each step of the model building process SAP AG Page 7

8 25. Click to close this screen. 2.3 Execution of the Association Model The concept of training the association model involves creating an application process that links Data Sources, Transformations and Data Targets together. Application Process A data file is assigned to the Data Source and a specific analysis model is assigned to the Data Target. The objects are then joined together through a line connection. This activity is completed using the Analysis Process Designer. 1. Click to return to the Data Mining Workbench. 2. Click to access the APD. The Analysis Process Designer (APD) screen will appear as follows; 2008 SAP AG Page 8

9 In order to create an analysis process you must 3. Click icon to create a new application. 4. Type the word General in the Application field. This will store this instance of the model in the general directory 5. Click to create the application and close the dialog screen. The APD has three main functional areas Data Sources, Transformations, and Data Targets SAP AG Page 9

10 You will need to create a process by dragging and dropping the appropriate Data Source, Transformation and Data Target objects onto the Work Area. Identify an appropriate Data Source. 6. Click the icon to identify a Data Source (Flat File). The Source File dialog screen appears SAP AG Page 10

11 7. Type xqnnn Shopping File in the Description field. 8. Select the Client Workstation radio button to select it. You will need to identify the location of the Data Source (Shoppingdataset.csv). 9. Browse to enter the file location of Shoppingdataset.csv in the File field. The Data Source in this exercise is a flat file. Therefore you will need to specify the format of the data Type, in the Field Separator field. 12. Click to accept the entries. You can check whether your Data Source assignment has worked by viewing the data stored in the Data Source. 13. Right click the icon to display its context menu SAP AG Page 11

12 14. Click Display Data to select this command. The data should appear in the following format. 15. Click to return to the previous menu screen. You can also perform a basic statistical analysis on the data stored in the data source. 16. Right click the icon to display the context menu. 17. Click Display Basic Statistics to select this command. The Basic Statistics Settings dialog screen appears: 2008 SAP AG Page 12

13 You need to specify the field(s) in which the basic analysis is to be performed on. 18. Click Material to select this field. 19. Click to transfer the selected field. 20. Click to perform the statistical analysis. A histogram and frequency table for the material field will be generated SAP AG Page 13

14 21. Review the results. 22. Click to return to the previous screen. The next stage in the process is to identify the type of Transformation to be performed on the Data Source. Transformations indicate what actions are going to be performed on the data. These are classified as general data manipulation or Data Mining analysis SAP AG Page 14

15 In this exercise there is only one Data Source and therefore no Transformations are required. The possible Data Targets are as follows: 23. Click the icon to select the Association Analysis Data Target. The icon appears in the Work Area. 24. Right Click the icon to display its context menu SAP AG Page 15

16 25. Click Properties to select this command. The Train a data mining model screen appears. 26. Type xqnnn Association Analysis 1 in the Description field. You now need to identify the Model to be used in this analysis. 27. Click the Model field to select it. 28. Click to display the available Models. 29 Click xqnnna1 model to select it. The selection is transferred to the previous screen. 30. Click to accept the entries and return to the previous screen. 31. Drag of the Data Source to the of the Data Target. A connection appears between the two objects SAP AG Page 16

17 You will now need to map the InfoObjects of the Data Source and the Data Target model together. 32. Right click the line connection between the objects to display the context menu. 33. Click Properties to select this command. The Change Field Assignment screen appears. You need map the fields in the Target Structure to those in the Source Structure. 34. Place the cursor in the Source Structure field of the Product Description row and click icon to display all possible options SAP AG Page 17

18 35. Select Material and press the icon to return to the previous screen. 36. Repeat the above process again and select Order Number to match the target structure. 37. Click the icon to accept the field assignments. 38. Click to save your model. A dialog screen appears requesting a technical name for your model. 39. Type xqnnna Click the icon to save the analysis process. 41. Click the icon to activate the analysis process. Execution of the Association Model 1. Click the icon to execute the model. A log screen appears to indicate the success of the model SAP AG Page 18

19 2. Click the icon to return to the previous screen. Reviewing the results of the analysis 3. Right click the icon to display the context menu. 4. Click Data Mining Model to select this command. 5. Click View Model Results. The following screen appears which summarises the parameters on which the model is based. For the purpose of this exercise we only want to view the top twenty results. 6. Click to select it. 7. Type 20 in the Top N field. 8. Click icon to execute the model. The resultant output displays all the rules created by the association process SAP AG Page 19

20 If you wish to execute the application process again with different model parameters, perform the following: Reset the Data Target This can be achieved by accessing the context menu of the data target 1. Change the parameters in the model. 2. Click the icon to return to the Data Mining Workbench. 3. Select the appropriate model to change. 4. Click the icon to 5. Click the tab to change parameters. Click the icon to save your changes and the icon to activate the model. Click to return to the previous screen Click the icon to access the Analysis Process Designer Click icon to execute the model SAP AG Page 20