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
