CTS FWR5-3105SFP-CW-AC-DR Series User manual

User Guide for Predictive Analytics Cloud Image - Trial Edition
SAP Store –Trial Edition

Table of Contents
1INTRODUCTION......................................................................................................................................3
2PREREQUISITES.....................................................................................................................................3
3SYSTEM ACCESS...................................................................................................................................3
4PREDICITIVE SCENARIOS (FRONT-END COMPONENTS).................................................................5
4.1 Banking....................................................................................................................................................5
Customer Attrition Analysis.......................................................................................................................5
4.2 Consumer Products .............................................................................................................................10
Brand Sentiment and Sales Analysis .....................................................................................................10
Demand Data Analysis...........................................................................................................................12
Product Fulfillment and Optimization......................................................................................................14
4.3 Finance ..................................................................................................................................................18
Company Performance Analysis ............................................................................................................18
Late-Payment Management ...................................................................................................................18
Customer Cash Collection Analysis .......................................................................................................19
4.4 Manufacturing.......................................................................................................................................22
Customer Demand and Inventory Management.....................................................................................22
Overall Equipment Effectiveness............................................................................................................23
Asset Breakdown Analysis .....................................................................................................................24
Maintenance Cost Analysis ....................................................................................................................28
4.5 Portfolio & Project Management.........................................................................................................34
Project Profitability Analysis....................................................................................................................35
4.6 Retail......................................................................................................................................................42
Market Basket Opportunities ..................................................................................................................42
Customer Loyalty Programs...................................................................................................................43
Store Clustering......................................................................................................................................45
4.7 Sales & Marketing.................................................................................................................................47
Customer Segmentation.........................................................................................................................48
Market Segmentation..............................................................................................................................49
Market Campaign Success.....................................................................................................................50
Product Recommendation ......................................................................................................................51
Pipeline and Revenue Forecasting.........................................................................................................53
4.8 Telco.......................................................................................................................................................56
Churn Modeling and Offer Recommendation.........................................................................................59
Post-Paid Analysis..................................................................................................................................64
Rotational Churn Detection.....................................................................................................................65
Multi-SIM Detection ................................................................................................................................68
5HANA MODELS (OPTIONAL BACK-END COMPONENTS)................................................................73
6OPEN SOURCE R INCLUDING INSTALLATION (OPTIONAL)...........................................................82
7SECURITY ASPECTS............................................................................................................................82
8APPENDIX..............................................................................................................................................82
8.1 Further Documentation........................................................................................................................82
8.2 SQL Scripts ...........................................................................................................................................82

1 INTRODUCTION
Users learn how to use SAP Predictive Analytics tools in the context of SAP HANA with a few pre-built
scenarios in this trial edition for the cloud. Sample data is available so that the users can understand how
the tools could be utilized. Predominantly the emphasis is on SAP Predictive Analytics tool (both Expert
Analytics and Automated Analytics roles), SAP Lumira and SAP UI5 (HTML 5). SAP HANA is showcased
for housing the data and modeling the data.
2 PREREQUISITES
The trial edition is available in the SAP Store. Users access the trial landscape from their laptop through a
remote desktop connection.
3 SYSTEM ACCESS
Process Steps
In the SAP Cloud Appliance Library, choose Instances to display the list of available instances. Then
choose the Connect operation for your instance and follow the instructions. Alternatively you can
manually configure a connection in the following way.
Choose the name of the running Error! Reference source not found. instance. The system opens
the Instance dialog box with the properties of the solution instance. The IP Addresses area displays
the access details of the solution instance.
Copy the IP address of the running instance.
Use the SAP Logon New functionality and enter the details of the new system, then start an SAP GUI
connection.
Parameter
ID
Parameter
Value
Note
IP address of
the instance
x.x.x.x
To be retrieved from SAP Cloud Appliance Library when viewing
the details of the solution instance.
SID
00
System ID of Predictive Analytics Trial Edition on SAP HANA
Remote Desktop
Group
IP
User
PW
All
IP address of your
personal instance
Administrator
The master password is used for
accessing the system. It is provided by the
user during the creation of the solution
instance in SAP Cloud Appliance Library.
HANA Studio: <your system>, Instance <your instance>
Group
Backend SAP
HANAServer
Name
User
PW
All
VHCALHDBDB
SYSTEM
master password
Client Tools (SAP Predictive Analytics –Expert Analytics role and Automated Analytics role)
Tool
Group
Backend SAP
HANAServer
Name
User
PW

Predictive Analytics
All
VHCALHDBDB
RDSPAUSER
master
password
Predictive Analytics
(for Sales&Markeing
use cases)
All
VHCALHDBDB
RDS_CRM_DS
USER
master
password
Connect to Remote Desktop
Start Remote Desktop by following the menu Start > All Programs > Accessories > Remote Desktop
Connection
Now the Remote Desktop is launched.
Enter the following information:
Computer: IP addresses of your personal instance
User: Administrator
Choose Connect.
Enter the following information:
Password: Master Password
Choose OK.
Connect to Data Sources (ODBC)
Start Data sources (ODBC) by following the menu Start > All Programs > Administrative Tools > Data
Sources (ODBC)
Click on System DSN tab and click on the Add button
In the new data source pop-up dialog, select HDBODBC as the data source and click Finish

Now enter the data source name, data source description as vhcalhdbdb and Server:Port as
vhcalhdbdb:30015
Click connect
Enter user id and password as RDSPAUSER and master password
NOTE: Location of LUMs files on the instance
1. Once the instance is created and you have done a remote desktop, you get access to the files on the
remote desktop.
2. The Predictive Analytics LUMs files are stored in the D:\PA_Documents
3. Most of the LUMs files are already imported and you should find them when you launch “Expert
Analytics” from the SAP Predictive Analytics software. If you happen to not find the LUMs file that you
are looking to open, please import them from the location specified in step 2.
4 PREDICITIVE SCENARIOS (FRONT-END COMPONENTS)
4.1 Banking
For the Banking industry, as part of the solution, we have a pre-built predictive model for one use case, the
customer attrition analysis. In this use case, we focus on analyzing the customer attrition trends using 2 different
approaches, identifying the customers like to leave
Using the Automated Analytics tool in Predictive Analytics
Using the Expert Analytics tool in Predictive Analytics
We have built a generic data structure and seeded sample data so that compelling predictive models could be
built. Most of the fields in the data structure are generic and which are used by many banking customers.
Customer Attrition Analysis
The following section describes configuration for the Customer Attrition Analysis scenario, which can be used
with either Automated Analytics or Expert Analytics. The generation and applying of this model will generate a
table in SAP HANA, which is then combined with Profile information and made visible via a database view in
SAP HANA. The data in this view can then be displayed using any tool that can read SAP HANA.
Note: Any SAP or non-SAP customer would be able to deploy or mimic the data structure, load the data and use
the pre-built models.
Automated Analytics (using the first approach)
Training the Model
Launch SAP Predictive Analytics
Choose the Modeler section
Select Create a Classification/Regression Model
Select Database from Select a Data Source
Select your SAP HANA instance
Logon to the <DOMAIN USER> account via the Browse button
Specify the Data Set that is used to Train your data model. The set is the table you populated with
your data: SAP_RDS_PA_BANK.CUST_ATTRITION

For more information on populating SAP_RDS_PA_BANK.CUST_ATTRITION, see the SAP
HANA Deployment for Banking on SAP Predictive Analytics Content Adoption rapid-deployment
solution (VD2) configuration guide that is part of this solution.
Choose Analyze
BUS_PARTNER is the only column with a Key value of 1. This value is generated by SAP
Predictive Analytics that identifies this column as the Primary Key of the table
On the next screen, enter Attrition as the Target Variable and make BUS_PARTNER the Excluded
Variable
The value of the key is meaningless in terms of the analysis
On the next screen, enter the Model name: ATTRITION_SAP_RDS_PA_BANK_MODEL
Choose Generate
A Model is created with the statistics and the metadata that are applied to the data set of customers to
predict whether or not they will attrite.
Review the Training the Model display to validate your Model.
The most important values are the quality indicator KI and the robustness indicator KR. For more
information about SAP Predictive Analytics values and indicators, see the Appendix.
On the Using the Model screen, verify that you are able to use the Model (see the following section on
the predict phase)
If you feel confident that this model is reflective of the additional data sets you are planning to apply
this model to for prediction purposes, Save this model for these future runs.
a. Expand the Save/Export band and then choose Save Model
b. Specify where you want the model saved (SAP HANA database, Text file, and so on) and
SAVE.
Once saved, models may be opened and reused. The folder location for the saved model is
selected when you open a saved model in SAP Predictive Analytics by selecting Load a Model.
If you feel less confident about your model because the Predictive Power (KI) and Predictive
Confidence (KR) on the screen are low (less than .9), then more modeling is required.
When additional modeling fails to indicate a trend or does not provide significant degree of
confidence in the results, review the data set used. If the data set is divergent or sparse, it
cannot support the generation of a robust model.
In Applying the Model, select your SAP HANA instance and logon to the <DOMAIN USER> account.
In the dialog box, select SAP_RDS_PA_BANK.VW_CUST_PROFILE
Result: The Model is ready for use as the basis for prediction. Continue to the next section for the
prediction phase.
Predicting
In SAP Predictive Analytics, the training and the prediction phases are a continuous process. To initiate the
Predict Phase, continue from the last step of the previous section.
From Run, select Apply Model
Specify the data set for predictions using your Model in the Application Data Set section.
a. Select Database
b. Browse and populate the dialog box with your SAP HANA instance
c. Logon to the <DOMAIN USER> account.
d. Choose Browse Data
e. In the dialog box, select SAP_RDS_PA_BANK.CUST_PROFILE (returning the data from the
table you previously loaded into SAP HANA)
If browsing the data doesn’t show this view, enter the name in the dialog box to use as a search
field

From Generation Options, select Decision for the Generation Options
Leave Apply as the Mode
Select where the generated output is stored by making the following entries in the Results Generated
by the Model section:
a. Select Database
b. Browse and populate the dialog box with your SAP HANA instance
c. Logon to the <DOMAIN USER> account.
d. Browse Data and in the resulting dialog box, select
SAP_RDS_PA_BANK.CUST_ATTRITION_RESULTS
The correct SAP_RDS_PA_BANK.CUST_ATTRITION_RESULTS table is all upper case
Select the Apply button to execute the Model
Choose the Delete option for the Update will attempt to merge the runs together dialog box, to remove
output rows from the previous run. When the cycle is complete, SAP Predictive Analytics returns
statistical information available under the Display bar.
Result: The predicted data is written back to the SAP HANA schema SAP_RDS_PA_BANK as a table
CUST_ATTRITION_RESULTS. To view the results in SAP Predictive Analytics, select the VW_
CUST_ATTRITION_RESULTS View, which joins the results with the original data.
Expert Analytics (using the second approach)
Training the Model
Select the LUMS for Banking_Customer_Attrition_Analysis - Train
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Banking_Customer_Attrition_Analysis - Train.lums file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
Switch to the Predict panel to view the predictive model
Adjust the HANA C4.5 - 4 independent cols Algorithm Component
a. Choose Configure Settings
b. Select the Properties/Output Information/Output Mode to Trend
c. Select Properties/Column Selection/Features for the independent columns (variables) for
analysis. By default, the following are enabled:
Average Tenure
Average Income
Average Savings Amt
Average Investment Amt
d. Choose Properties/Column Selection/Target Variable for the dependent column (variable) for
analysis. By default Attrition is enabled
e. Select Advanced/Tree Pruning/Minimum Split and enter the value 10.
f. Select Done
At this point, run the Algorithm Component (Run Till Here) before continuing the analysis. Choose Yes
to switch to the Results view for verifying the execution results
Adjust the HANA C4.5 - 3 independent cols Algorithm Component
a. Choose Configure Settings
b. Select Properties/Output Information/Output Mode to Trend

c. Select Properties/Column Selection/Features for the independent columns (variables) for
analysis. By default, the following are enabled:
Average Tenure
Average Income
Average Investment Amt
d. In the Properties/Column Selection/Target Variable, choose the dependent column (variable) for
analysis. By default, Attrition is enabled
e. Select Advanced/Tree Pruning/Minimum Split and enter the value 50.
f. Select Done
At this point, run the Algorithm Component first (Run Till Here) before continuing the analysis. Choose
Yes to switch to the Results view for verifying the execution results. The results should look similar to
HANA C4.5 - 4 independent cols Algorithm, though with less complexity in decision tree.
a. Select HANA C4.5 - 3 independent cols component. From Component Actions select Save as
Model.
b. Specify the Save Model Attrition_C4.5_3_indep_cols as Model Name
c. Select the Overwrite row and choose Save
Export Saved Model
a. From Components list, expand Models
b. Select Attrition_C4.5_3_indep_cols
c. From Component Actions, select Export Model
d. Choose Use this option to export data models to the SAP Predictive Analytics Archive (*.spar)
file
e. Change File Name to Attrition_C4.5_3_indep_cols.spar.
f. Select Save
Predicting
Once the model is trained, the PREDICT LUMS is used with SAP Predictive Analytics for predictive modeling
and the visualizations for the analysis using current data. The following steps must be executed to configure the
PREDICT LUMS.
Select the LUMS for Banking_Customer_Attrition_Analysis - Predict
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Banking_Customer_Attrition_Analysis - Predict.lums file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
Switch to the Predict panel to view the predictive model. Banking_Customer_Attrition_Analysis -
Predict.lums include default saved model Attrition_C4.5_3_indep_cols imported into the analysis.
From Components list, select the ‘+’ button.
Select Import Model
a. Locate the saved Attrition_C4.5_3_indep_cols.spar, and choose Open
b. Select model Attrition_C4.5_3_indep_cols and choose Finish
Expand Models to select and drag Attrition_C4.5_3_indep_cols into Analysis screen
Adjust the HANA C4.5 - 3 independent cols Algorithm Component
a. Choose Configure Settings
b. Select in the Properties/Output Information/Output Mode to Trend

c. Select Properties/Column Selection/Features and choose the independent columns (variables)
for analysis.
d. Select: Average Tenure, Average Income, and Average Investment Amt identical to saved
model
Adjust the HANA Writer Data writer component
a. Select Configure Settings
b. Specify Schema Name, Table Type, and Table Name as appropriate for your site
Choose Run to execute the scenario
Choose Yes to switch to the Results view for verifying the execution results.

4.2 Consumer Products
For the Consumer products industry, we have pre-built scenarios for 3 use cases such as Brand sentiment and
sales analysis, Demand data analysis, Product fulfillment and Optimization. Depending on the use case and the
functionality that we are analyzing, we have picked up either the Automated Analytics or the Expert Analytics
approach.
Basically for the Demand data analysis and Product fulfillment and Optimization use cases, we have used the
leading SAP application Demand signal management as the data source and sample data sets are available for
the same. With regard to the Brand sentiment and sales analysis, we have built a generic data set and seeded
sample data set for building the predictive models.
Note: Any SAP or non-SAP customer would be able to deploy or mimic the data structure, load the data and use
the pre-built models.
Brand Sentiment and Sales Analysis
In this use case, we are trying to identify the brand sentiment during a major event and predicting the sales for
the upcoming similar events based on the brand value.
The following section describes configuration for the Brand Sentiment and Sales Analysis scenario. The
generation and applying of this model will generate a table in SAP HANA, which is then combined with Profile
information and made visible via a database view in SAP HANA. The data in this view can then be displayed
using any tool that can read SAP HANA.
Automated Analytics
Training the Model
Launch SAP Predictive Analytics
Choose the Modeler section
Select Create a Classification/Regression Model
Select Database on the Select a Data Source screen
Select your SAP HANA instance
Logon to the <Domain User> account via the Browse button
You now need to specify the Data Set to use to Train your data model. Choose
SAP_RDS_PA_CPG.SEM_MODEL_BASE
For more information on loading data into the SAP_RDS_PA_CPG.SEM_MODEL_BASE data
set, see Predictive Analytics for Consumer Products (K38) configuration guide that is part of this
solution.
On the next screen, choose Analyze
Assign Key as “1” against SALES_DATE, PRODUCT_NAME and EVENT
Check the Add Filter in data Set on the bottom left section of the screen.
Choose Next
Enter a filter for the date range of SALES_DATE using the format of YYYY-DD-MM, for example,
SALES_DATE <= 2014-02-15 and SALES_DATE >= 2012-02-13 (your range is based upon the date
range of your data set).
Enter a filter for the PRODUCT_NAME = PRODUCT 1/PRODUCT 2 (based on which product you
want to select for modeling)
On the next screen, use SALES_AMT as the target variable and
a. For training a model, which is based on event, use SMC1, SMC2, HLYDSSN, SALES_DATE
as Explanatory Variables Selected and exclude the rest of the variables

b. For training a model, which is not based on event, use SALES_DATE as predictor variables
and exclude the rest of the variables
On the next screen, name the Model:
a. P1_EVENT: If you applied “PRODUCT 1” filter on PRODUCT_NAME and kept SMC1, SMC2
and HLYDSSN as predictor variables
b. P2_EVENT: If you applied “PRODUCT 2” filter on PRODUCT_NAME and kept SMC1, SMC2
and HLYDSSN as predictor variables
c. P1_NOEVENT: If you applied “PRODUCT 1” filter on PRODUCT_NAME and did not keep
SMC1, SMC2 and HLYDSSN as predictor variables
d. P2_NOEVENT: If you applied “PRODUCT 2” filter on PRODUCT_NAME and did not keep
SMC1, SMC2 and HLYDSSN as predictor variables
Choose Generate
You have created a model with the statistics and the metadata used for applying to your date-specific
sales data.
Review the Training the Model display to validate your model. The most important values are the
quality indicator KI and the robustness indicator KR. For more information, see the Appendix for links
to SAP Predictive Analytics documentation.
Choosing Using the Model initiates the Predict Phase that is described in the following section.
If you feel confident that this model is reflective of the additional data sets you are planning to
apply this model to for prediction purposes, SAVE this model for future runs.
a. Expand the Save/Export band and then choose Save Model
b. Specify the location where you want the model saved (SAP HANA Database, text file, and so
on).
c. Choose Save to that location.
The location of the saved model is entered when using Load a Model for additional predictions.
If you feel less confident about your model because the Predictive Power (KI) and Predictive
Confidence (KR) on the screen are low (less than .9), then more modeling is required. For more
information you about modeling using SAP Predictive Analytics, see the User Guide referenced in the
Appendix.
When additional modeling fails to indicate a trend or does not provide significant degree of confidence
in the results, review the data set being used. If the data set is divergent or sparse, it cannot support
the generation of a robust model.
Result: The Model is ready for use as the basis for prediction. Continue to the next section for the
prediction phase.
Predicting
SAP Predictive Analytics combines the training and the prediction phases into a continuous process. To initiate
the Predict Phase, continue from the last step of the previous section.
Use the following procedure to specify which data set to apply to the model, what is generated, and where the
generated output is stored.
From the Run band, select Apply Model
Specify which data set to apply the model in the Application Data Set section.
Select Database
Browse and populate the dialog box with your SAP HANA instance
Logon using the <Domain User> account
Use SAP_RDS_PA_CPG.SEM_MODEL_BASE to apply to the model. The data includes the future
values of the dates and events.
To specify what is to be generated:

From Generations Options, select Predicted Value Only
Use Apply as the Mode
To specify where the generated output is stored, enter the following in Results Generated by the
Model section:
Select Database
Browse and enter the SAP HANA instances and logon for the <Domain User> account in the dialog
box.
Depending on the model you use, stored tables that were applied to the model appear, for example
SAP_RDS_PA_CPG.SEM_MODEL_BASE_P1_EVENT,
SAP_RDS_PA_CPG.SEM_MODEL_BASE_P1_NOEVENT,
SAP_RDS_PA_CPG.SEM_MODEL_BASE_P2_EVENT and
SAP_RDS_PA_CPG.SEM_MODEL_BASE_P1_NOEVENT.
Select the Apply button to execute the model
In the Update will attempt to merge the runs together dialog box, choose the Delete option to remove
output rows from the previous run.
Result: The predicted data is written back to the SAP HANA schema SAP_RDS_PA_CPG as tables.
To view the results in SAP Predictive Analytics, select the table created in the predict phase.
Demand Data Analysis
In this use case, we are identifying what products are doing well in a particular region and how well they are
selling across different segments of population in those communities.
The following section describes configuration for two scenarios. The solution preconfigured content is provided in
LUMS format files for each scenario. There are no dependencies and scenarios can be deployed individually or
together, depending on your needs.
Expert Analytics
Demand Forecasting
Select the LUMS for Consumer Demand Forecasting
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Consumer_Demand_Forecasting_Analysis.lums or
Consumer_Demand_Forecasting_Analysis_DSiM.lums (for SAP Demand Signal Management
data) file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
Switch to the Predict panel to view the predictive model.
Select Filter in the analysis panel
a. Choose Configure Settings
b. Select Row Filter and update the value and Validate
Adjust the HANA Triple Exponential Smoothing independent cols Algorithm Component
a. Choose Configure Settings
b. Select Output Mode: Forecast
c. Select Target Variable
d. Select Period Custom
e. Select Period Per Year

f. Select Start Year
g. Select Start Periods
h. Select Periods to Predict
Adjust the HANA Triple Exponential Smoothing Advanced Properties (Optional)
a. Select Alpha, Beta, and Gamma
Adjust the HANA Writer Data writer component
a. Select Configure Settings
b. Specify Schema Name, Table Type and Table Name as appropriate for your site. Keep the
default configuration whenever possible.
Choose Run to execute the scenario
Choose OK to switch to the Results view for verifying the execution results.
Select available charts icon to display results in charts.
Switch to Visualize panel and Select Components selector to display results in additional charts.
Switch to Compose panel and Select Components selector to display results in storyboards.
Demand Cluster
Select the LUMS for Demand Cluster Analysis
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Customer_Demand_Cluster_Analysis.lums or Customer_Demand_Cluster_Analysis_DSiM.lums
(for SAP Demand Signal Management data) file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
Switch to the Predict panel to view the predictive model.
Adjust the SAP HANA K-Means Algorithm Component
a. Choose Configure Settings
b. Select Features
c. Select Number of Clusters
Adjust the Filter (only in Customer_Demand_Cluster_Analysis.lums)
a. Choose Configure Settings
b. Select Row Filter and update the value and Validate
Adjust the SAP HANA C4.5 Component (only in Customer_Demand_Cluster_Analysis.lums)
a. Choose Configure Settings
b. Select Features
c. Select Advanced and select Minimum Split
Adjust the SAP HANA Normalization
a. Choose Configure Settings
b. Select Selected Columns
c. Select Normalization Type
d. Select New Maximum
e. Select New Minimum
Adjust the SAP HANA Writer Data writer component
a. Select Configure Settings
b. Specify Schema Name, Table Type and Table Name as appropriate for your site
Choose Run to execute the scenario

Choose OK to switch to the Results view for verifying the execution results.
Select available charts icon to display the results in charts.
Switch to Visualize panel and Select Components selector to display results in charts.
Switch to Compose panel and Select Components selector to display results in storyboards.
Product Fulfillment and Optimization
In this use case, we are identifying the different clusters of population and their buying trends of products. Based
on this we are doing product association of what products are being sold together and how many quantities and
if any outliers. Finally we also do a forecasting analysis of the sales of these different products.
The following section describes configuration for the two scenarios provided as part of the Cloud Trial. The
solution preconfigured content is provided in LUMS format files for each scenario. There are no dependencies
and scenarios can be deployed individually or together, depending on your needs.
Expert Analytics
Product Cluster
Select the LUMS for Product Cluster
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Consumer_Product_Cluster_Analysis.lums or Consumer_Product_Cluster_Analysis_DSiM.lums
(for SAP Demand Signal Management data)file.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
b. To edit the input data, from Data menu select Edit.
c. In the dialog box, enter your SAP HANA username and password.
Switch to the Predict panel to view the predictive model.
For Consumer_Product_Cluster_Analysis.lums, adjust the R-K-Means Algorithm Component
a. Choose Configure Settings
b. Select Number of Clusters
c. Select Features
d. Select Cluster Name
e. Select Advanced and select Algorithm Type
For Consumer_Product_Cluster_Analysis_DSiM.lums, adjust the HANA K-Means Algorithm Component
a. Choose Configure Settings
b. Select Number of Clusters
c. Select Features
d. Select Cluster Name
Choose Run to execute the scenario
Choose OK to switch to the Results view for verifying the execution results. Choose available charts
icon to display the results in charts.
Switch to Visualize panel and Select Components selector to display results in charts.
Product InterQuartile
Select the LUMS for Product Interquartile

a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Consumer_Product_InterQuartile_Analysis.lums or
Consumer_Product_InterQuartile_Analysis_DSiM.lums (for SAP Demand Signal Management
data) file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
b. To edit the input data, from Data menu select Edit.
c. In the dialog box, enter your SAP HANA username and password.
Switch to the Predict panel to view the predictive model
Select Filter in the analysis panel
a. Choose Configure Settings
b. Select Row Filter and update the value and Validate
For Consumer_Product_InterQuartile_Analysis.lums, adjust the Inter Quartile Range Algorithm
Component
a. Choose Configure Settings
b. Select Output Mode: Show Outliers
c. Select Features: Quantity_Sold
d. Select Predicted Column Name
For Consumer_Product_InterQuartile_Analysis_DSiM.lums, adjust the HANA Inter Quartile Range Test
Algorithm Component
a. Choose Configure Settings
b. Select Output Mode: Show Outliers
c. Select Features: Sales Quantity
d. Select Predicted Column Name
Choose Run to execute the scenario
Choose OK to switch to the Results view for verifying the execution results.
Choose available charts icon to display the results in charts.
Switch to Visualize panel and Select Components selector to display results in charts.
Product Time Series
Select the LUMS for Product Timeseries
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Consumer_Product_Timeseries_Analysis.lums or
Consumer_Product_Timeseries_Analysis_DSiM.lums (for SAP Demand Signal Management
data) file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
b. To edit the input data, from Data menu select Edit.
c. In the dialog box, enter your SAP HANA username and password.
Switch to the Predict panel to view the predictive model.

Select Filter in the analysis panel
a. Choose Configure Settings
b. Select Row Filter and update the value and Validate
For Consumer_Product_Timeseries_Analysis.lums, adjust the R-Triple Exponential Smoothing
Algorithm Component
a. Choose Configure Settings
b. Select Output Mode: Forecast
c. Select Period to Predict: 10
d. Select Target Variable: Quantity_Sold
e. Select Period
f. Select Periods per Year
g. Select Start Period
h. Select Start Year
i. Select Year Values
j. Select Period Values
For Consumer_Product_Timeseries_Analysis_DSiM.lums
c. Adjust the HANA Triple Exponential Smoothing Algorithm Component
d. Choose Configure Settings
e. Select Output Mode: Forecast
f. Select Period to Predict: 10
g. Select Target Variable: Sales Quantity
h. Select Period
i. Select Periods per Year
j. Select Start Period
k. Select Start Year
l. Select Year Values
m. Select Period Values
Choose Run to execute the scenario
Choose OK to switch to the Results view for verifying the execution results.
Choose available charts icon to display the results in charts.
Switch to Visualize panel and Select Components selector to display results in charts.
Product Association
Select the LUMS for Product Association
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Consumer_Product_Association_Analysis.lums or
Consumer_Product_Association_Analysis_DSiM.lums (for SAP Demand Signal Management
data) file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Verify the data
a. Choose Prepare panel to ensure that the data has loaded properly.
Switch to the Predict panel to view the predictive model.

Select Filter in the analysis panel
a. Choose Configure Settings
b. Select Row Filter and update the value and Validate
Adjust the SAP HANA Apriori Algorithm Component
a. Choose Configure Settings
b. Select Apriori Type: Apriori Lite
c. Select Item Column: Product Name
d. Select TransactionID Column: Transaction ID
Choose Run to execute the scenario
Choose OK to switch to the Results view for verifying the execution results.
Choose available charts icon to display the results in charts.
Switch to Visualize panel and Select Components selector to display results in charts.
Switch to Compose panel to see the results in storyboard.

4.3 Finance
For the Finance LoB, we have pre-built scenarios for 3 use cases such as Company performance analysis, Late
payment management, Customer cash collection analysis. Depending on the use case and the functionality that
we are analyzing, we have picked up either the Automated Analytics or the Expert Analytics approach.
Basically for the Late Payment management use case, we have used the SAP HANA Live views for Finance as
the data source and sample data sets are available for the same. With regard to the Company performance
analysis and Customer cash collection analysis use cases, we have built a generic data set and seeded sample
data set for building the predictive models.
Note: Any SAP or non-SAP customer would be able to deploy or mimic the data structure, load the data and use
the pre-built models.
Company Performance Analysis
In this use case, we focus on leveraging the key trends such as fuel prices, unemployment and many more
variables to do more accurate predictions of revenue, margin and profit for a particular company.
Expert Analytics
Select the LUMS for Company_Performance_Correlation.lums
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Company_Performance_Correlation.lums file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Choose Prepare tab and ensure that the data has loaded properly
Switch to the Predict tab to view the predictive model.
Configure the filter components for filtering the data, when necessary
Adjust the properties of the Correlation of revenue with external factors component, for example, the
independent columns selected for the analysis.
Adjust the properties for the other algorithm components:
Correlation of profit with external factors
Correlation of margin with external factors
Choose Run to run the algorithm and execute the scenario
Choose Yes to switch to the Results view.
Late-Payment Management
In this use case, we focus on vendors who are likely to be the late payers ahead of time so that they can be
handled accordingly.
There are two LUMS files used for this scenario. In the configuration, you use the
Finance_Customer_Late_Payments_train.lums to train the prediction model. When predicting, you use the
Finance_Customer_Late_Payments_predict.lums.
.
Expert Analytics
Select the LUMS for Finance_Customer_Late_Payments_Train

a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Finance_Customer_Late_Payments_train.lums file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
To verify the data, choose Prepare tab to ensure that the data has loaded properly
Switch to the Predict panel to view the predictive model.
Configure the following components:
a. Adjust the Filter component, if necessary. For example, you can choose to restrict the time
period of the financial documents being analyzed for training the model.
b. Adjust the HANA C4.5 Decision Tree Component, if necessary.
c. Select/deselect from the independent columns already selected for the analysis, if needed.
d. Adjust any other properties, if necessary. For more information about properties, see the SAP
Predictive Analytics User Guide in the Appendix.
Choose Run to execute the scenario and run the algorithm
Choose Yes to switch to the Results view to verify the execution results.
(Optional) Highlight the SAP HANA C4.5 Decision Tree and save the model
(Optional) Export the model as SAP Predictive Analytics (.spar) file, for use with PREDICT LUMS file.
For configuration purposes, only the procedure for the TRAIN LUMS is described. Actual data is
run with the PREDICT LUMS after the model has been trained.
Customer Cash Collection Analysis
In this use case, we focus on the debt collectors who are likely to recover payments from a pool of customers
who would likely pay, if approached ahead of time in a particular mode.
Automated Analytics
Training the Model
Launch SAP Predictive Analytics
Choose Automated Analytics - Modeler - Create a Classification/Regression Model
On the Select a Data Source enter the SAP HANA server details, your user name and password and
specify the Data Set name VW_PREDICT.
Choose Analyze and wait for the metadata to appear
Set the Key and Order of TENANCY_SKEY to 1's, for PERSON_SKEY to 2's, and KxIndex to 3's
On the Selecting Variables screen, move PAYMENT_EVALUATION to be the only Target Variables
entry,
Move all variables that are not part of the Regression Analysis to the Excluded Variables box
The ones to be moved are:
TENANCY_SKEY
PERSON_SKEY
PAR_REFNO
PAR_REUSABLE_REFNO
CREATE_RUN_SKEY
MODIFY_RUN_SKEY
ACCOUNT_BALANCE_AVG
HOW_CONTACTED
CONTACT_SUCCESSFUL
DAYS_BEFORE_DUE
REPAIR_NOTICE_DATE_1
REPAIR_TYPE_1

REPAIR_DEBT_WITHHELD_1
DEBT_PAID_AFTER_COMPLETION_1
REPAIR_NOTICE_DATE_2
REPAIR_TYPE_2
REPAIR_DEBT_2_WITHHELD
DEBT_PAID_AFTER_COMPLETION_2
REPAIR_TYPE_3
REPAIR_DEBT_WITHHELD_3
DEBT_PAID_AFTER_COMPLETION_3
REPAIR_NOTICE_DATE_3
TOTAL_REPAIR_DEBT_OUTSTANDING
CONTACTED
CONTACTED_BY_EMAIL
CONTACTED_BY_HOME_VISIT
CONTACTED_BY_LETTER
CONTACTED_BY_LARGE_PRINT
EMAIL_SUCCESS
HOME_VISIT_SUCCESS
LETTER_SUCCESS
LARGE_PRINT_SUCCESS
On the next screen, Summary of Modeling Parameters, select Generate
The Model Overview screen then appears
View the KI and KR values
Go to the Next and view various Display Views.
For additional displays of the data, go to Section 5.4 Visualization, Insight Portion
Predicting
Launch SAP Predictive Analytics
Choose Automated Analytics - Modeler - Create a Clustering Model
On the Select a Data Source enter the SAP HANA server details, your user name and password and
specify the Data Set name VW_PREDICT_1_HOME_VISIT.
Choose Analyze and wait for the metadata to appear
Set the Key and Order of TENANCY_SKEY to 1's, for PERSON_SKEY to 2's, and KxIndex to 3's
On the Selecting Variables screen, move HOME_VISIT_SUCCESS to be the only Target Variables
entry and move all variable that are not part of the Clustering Analysis to the Excluded Variables box
The ones to be moved are:
TENANCY_SKEY
PERSON_SKEY
PAR_REFNO
PAR_REUSABLE_REFNO
CREATE_RUN_SKEY
MODIFY_RUN_SKEY
ACCOUNT_BALANCE_AVG
HOW_CONTACTED
CONTACT_SUCCESSFUL
DAYS_BEFORE_DUE
REPAIR_NOTICE_DATE_1
REPAIR_TYPE_1
REPAIR_DEBT_WITHHELD_1
DEBT_PAID_AFTER_COMPLETION_1