| Oracle® Retail Data Model Reference 10g Release 2 (10.2) Part Number E10084-04 |
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PDF · Mobi · ePub |
| Oracle® Retail Data Model Reference 10g Release 2 (10.2) Part Number E10084-04 |
|
|
PDF · Mobi · ePub |
This chapter provides reference information about the data mining models that are provided with Oracle Retail Data Model if you do choose to install the Data Mining Option. "About Data Mining in Oracle Retail Data Model" provides overview information. The rest of this chapter describes the following data mining models in more detail:
Each model topic provides the following types of information: a description of the model, examples of desired rules, a discussion of what the discovered rules explain, a discussion of what the model mines, a list of the target variables, a list of columns included in the target views, and sample reports.
Note:
For instructions on setting up and loading the data mining source, and executing the data mining models, see "Populating the Data Mining Component" in Oracle Retail Data Model Operations Guide.Oracle Retail Data Model includes data mining packages. The data mining portion of Oracle Retail Data Model consists of source tables that are populated by detail data for use by the data mining packages. This data is organized in a specific way to be compatible with the data mining modules so they can properly analyze and mine the data. Data mining packages pull in the source data and feed it into the data mining packages, and populate the target tables with the results. The data in the target tables are presented by the OBIEE reports.
Tip:
Changed or new data models are not supported by Oracle Retail Data Model. Consequently, do not change the data models that are defined and delivered with Oracle Retail Data Model, but, instead, copy a delivered data model to create a new one.As outlined in Table 6-1, the Oracle Retail Data Model data mining models are of three types: Adaptive Bayes Network (ABN), Decision Tree (DT) and Apriori Association (APASS).
Table 6-1 Oracle Retail Data Model Data Model Types
| Model | ABN | DT | APASS |
|---|---|---|---|
|
yes |
yes |
no |
|
|
yes |
yes |
no |
|
|
yes |
yes |
no |
|
|
no |
no |
yes |
|
|
yes |
yes |
no |
|
|
no |
no |
yes |
|
|
yes |
yes |
no |
|
|
yes |
yes |
no |
|
|
yes |
yes |
no |
|
|
yes |
yes |
no |
See also:
"Physical Data Model of the Data Mining Component" and "Data Mining ETL Packages" on page 5-28.This model addresses the business problem of building a profile of associates to explain their basket KPIs, such as Total Baskets, Average Basket Value, and other statistics.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF SALARY ELIGIBLITY is (N) AND EMPLOYEE_TYPE is (TEMPORARY) THEN NUMBER OF BASKETS IS THE HIGHEST
IF SALARY ELIGIBLITY is (N) AND EMPLOYEE_TYPE is (TEMPORARY) THEN NUMBER OF BASKETS IS THE LOWEST
The discovered rules provide correlations between the basket KPIs and associate (employee) attributes.
This analysis identifies which key attributes of an associate influence his or her number of baskets sold, average basket value, and size. This model mines the various attributes of associates. It takes the binned variables one at a time for the Total Basket Count, Average Basket Value, and Average Basket Size as the target variable of an Adaptive Bayes Network (ABN) and Decision Tree (DT) model with a single feature and discovers rules described in terms of associate attributes.
The rules are designed to be generated monthly. Therefore, nine ABN and nine DT models are created every month across all the associates using the following variables as targets in this order:
Total Basket Count Quartile (TBCQR)
Total Basket Count Quintile (TBCQN)
Total Basket Count Decile (TBCDE)
Average Basket Value Quartile (ABVQR)
Average Basket Value Quintile (ABVQN)
Average Basket Value Decile (ABVDE)
Average Basket Size Quartile (ABSQR)
Average Basket Size Quintile (ABSQN)
Average Basket Size Decile (ABSDE)
Note:
Associates are grouped into N-Tiles according to their sales performance figures.The following attributes of associates are identified from the Oracle Retail Data Model data warehouse tables as source variables for the models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Year
Month
Employee Id
Designation Name
Designation Title
Designation Level
Nationality
Gender
Marital Status
Age
Net Income
Demographics Code
Title
Total Months of Job
Employee Type
Correspondence Language
Disability Indicator
Rehire Recommendation Indicator
HR Based Salary Eligibility Indicator
Overtime Hours Salary Eligibility Indicator
Commission Eligibility Indicator
SPIFF Allowed Flag
Total Hours Worked
Total Overtime Hours
Total Basket Count Quartile (Target)
Total Basket Count Quintile (Target)
Total Basket Count Decile (Target)
Average Basket Value Quartile (Target)
Average Basket Value Quintile (Target)
Average Basket Value Decile (Target)
Average Basket Size Quartile (Target)
Average Basket Size Quintile (Target)
Average Basket Size Decile (Target)
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and are also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of correlating associate characteristics to shrink and theft.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF DESIGNATION is (TEMPORARY CASHIER) AND COMMISSION_ELIGIBILITY is (Y) THEN SHRINK TO SALES RATIO IS THE HIGHEST
IF DESIGNATION is (TEMPORARY CASHIER) AND COMMISSION_ELIGIBILITY is (N) THEN SHRINK TO SALES RATIO IS THE LOWEST
The discovered rules explain the reasons for the shrink and theft associated with an Associate.
This model mines the Total Shrink Count, Total Shrink Amount, Shrink as a percentage of Sales, Total Theft Count, Total Theft Amount and Theft as a percentage of Sales of individual associates to identify which of their key attributes influence their shrinkage and theft.
This model takes the binned variables one at a time for Total Shrinkage and Theft Count or Value or percentage of Sales as the target variable of an Adaptive Bayes Network (ABN) and Decision Tree (DT) model and discovers rules described in terms of associate attributes.
The rule are designed to be generated monthly. Therefore, eighteen ABN models and eighteen DT models are created every month across all the associates using the following variables as targets in this order:
Total Shrink Count Quartile (TSCQR)
Total Shrink Count Quintile (TSCQN)
Total Shrink Count Decile (TSCDE)
Total Shrink Amount Quartile (TSAQR)
Total Shrink Amount Quintile (TSAQN)
Total Shrink Amount Decile (TSADE)
Shrink as a percentage of Sales Quartile (STSQR)
Shrink as a percentage of Sales Quintile (STSQN)
Shrink as a percentage of Sales Decile (STSDE)
Total Theft Count Quartile (TTCQR)
Total Theft Count Quintile (TTCQN)
Total Theft Count Decile (TTCDE)
Total Theft Amount Quartile (TTAQR)
Total Theft Amount Quintile (TTAQN)
Total Theft Amount Decile (TTADE)
Theft as a percentage of Sales Quartile (TTSQR)
Theft as a percentage of Sales Quintile (TTSQN)
Theft as a percentage of Sales Decile (TTSDE)
The following attributes of associates are identified from the data warehouse tables as source variables for the models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Year
Month
Employee Id
Designation Name
Designation Title
Designation Level
Nationality
Gender
Marital Status
Age
Net Income
Demographics Code
Title
Total Months of Job
Employee Type
Correspondence Language
Disability Indicator
Rehire Recommendation Indicator
HR Based Salary Eligibility Indicator
Overtime Hours Salary Eligibility Indicator
Commission Eligibility Indicator
SPIFF Allowed Flag
Total Hours Worked
Total Overtime Hours
Total Shrink Count Quartile (Target)
Total Shrink Count Quintile (Target)
Total Shrink Count Decile (Target)
Total Shrink Amount Quartile (Target)
Total Shrink Amount Quintile (Target)
Total Shrink Amount Decile (Target)
Shrink as a percentage of Sales Quartile (Target)
Shrink as a percentage of Sales Quintile (Target)
Shrink as a percentage of Sales Decile (Target)
Total Theft Count Quartile (Target)
Total Theft Count Quintile (Target)
Total Theft Count Decile (Target)
Total Theft Amount Quartile (Target)
Total Theft Amount Quintile (Target)
Total Theft Amount Decile (Target)
Theft as a percentage of Sales Quartile (Target)
Theft as a percentage of Sales Quintile (Target)
Theft as a percentage of Sales Decile (Target)
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
The Model Signature Target View outlines the attribute structure of the model (built using an ABN or DT algorithm). The Model Signature Target View contains the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of profiling associate characteristics to sales, cost, and profit patterns.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF ASSOCIATE IS NOT ELIGIBLE FOR SPIFF AND ASSOCIATE IS ELIGIBLE FOR SALARY AND ASSOCIATE IS NOT ELIGIBLE FOR COMMISSION THEN ASSOCIATE PROFIT IS THE LOWEST
IF ASSOCIATE IS ELIGIBLE FOR SPIFF AND ASSOCIATE IS NOT ELIGIBLE FOR SALARY AND ASSOCIATE IS NOT ELIGIBLE FOR COMMISSION THEN ASSOCIATE PROFIT IS THE HIGHEST
The discovered rules provide correlations between associate (employee) characteristics and their sales, cost, and profit profiles.
This model mines the various attributes of associates. It takes the binned variables one at a time for Sales, Costs, and Profits as the target variable of an Adaptive Bayes Network (ABN) and Decision Tree (DT) model with a single feature and discovers rules described in terms of associate attributes.
The rules are designed to be generated monthly. Therefore, nine ABN models and nine DT models are created every month across all the associates using the following variables as targets in this order:
Sales Amount Quartile (SAQR)
Sales Amount Quintile (SAQN)
Sales Amount Decile (SADE)
Cost Amount Quartile (CAQR)
Cost Amount Quintile (CAQN)
Cost Amount Decile (CADE)
Profit Amount Quartile (PADR)
Profit Amount Quintile (PAQN)
Profit Amount Decile (PADE)
The following attributes of associates are identified from the data warehouse tables as source variables for the models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Year
Month
Employee Id
Designation Name
Designation Title
Designation Level
Nationality
Gender
Marital Status
Age
Net Income
Demographics Code
Title
Total Months of Job
Employee Type
Correspondence Language
Disability Indicator
Rehire Recommendation Indicator
HR Based Salary Eligibility Indicator
Overtime Hours Salary Eligibility Indicator
Commission Eligibility Indicator
SPIFF Allowed Flag
Total Hours Worked
Total Overtime Hours
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Year (PK)
Analysis Desc
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of discovering product categories that are frequently bought by customers. The model is used to understand the Categories purchased by a Customer in a typical transaction in terms of the components like the Categories in the Basket, Target Category in a Basket and additional information like Basket Significance (Sales Value), Target Category Significance (Sales Value) which are generated from regular Customer Transactional data.
Using Oracle Data Mining, the KPIs are modeled with the APRIORI algorithm utilised by the Association Rules model. The model type used for Association Rules with Apriori Algorithm is APASS. This model type is an example of Unclassified Learning since the Categories (or Target Category) which constitute the Category Basket are not inferred or guided (as part of data preparation) but are generated by the model itself.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF CUSTOMER HAS BOUGHT 'BABY', 'GRAB AND GO' THEN CUSTOMER IS LIKELY TO BUY 'PACKAGED BEVERAGES' (11 Support: 36%, Probability:56%)
Category Basket Significance of ('BABY', 'GRAB AND GO', 'PACKAGED BEVERAGES') is 45% of Sales Value => The Sales from the 3 categories in Category Basket ('BABY', 'GRAB AND GO', 'PACKAGED BEVERAGES') account for 45% of the Total Sales across all categories in that particular store.
The Category Basket Significance (Sales Value) KPI allows us to filter out Rules which may be insignificant from a Basket Sales Value perspective.
Target Category Significance of ('PACKAGED BEVERAGES') is 60% of the Basket Sales Value => The Sales from the Target Category ('PACKAGED BEVERAGES') account for 60% of the Total Sales from the Category Basket ('BABY', 'GRAB AND GO', 'PACKAGED BEVERAGES') in that particular store.
The Target Category Significance (Sales Value) KPI allows us to filter out Rules determining insignificant Customer Purchases (insignificant Target Category). In other words, it helps us to extract Rules which relate to singificant Customer Purchases, where the Target Category is significant within the Basket of Categories (from a Sales Value perspective). Identifying this information is useful from a campaign/promotion/upsell perspective.
IF CUSTOMER HAS BOUGHT 'FLORAL', 'PHARMACY', 'HOT FOODS' THEN CUSTOMER IS LIKELY TO BUY 'BABY' (Support: 36%, Probability: 62%)'
The discovered rules explain customer behavior and buying patterns regarding various product categories. They help indicate the groups of product categories that sell well together, for example:
The chances of a customer buying a BABY product increases from 11% to 62% if that customer purchases FLORAL and HOT FOODS products. This Rule is 28% significant in terms of Basket Value (Sales Value - all 3 categories in Basket) and The Target Category (BEAUTY) significance is about 75% of the Basket.
The chances of a customer buying a BEAUTY product is 34% if that customer has purchased a product from categories 'HEALTH', 'MAGAZINES' and 'PHARMACY'. This Rule is about 66% significant in terms of Basket Value (Sales Value of all 4 categories in Basket) and The Target Category (BEAUTY) significance is about 45% of the Basket.
BEAUTY products are very rarely sold together (about 3% of the time) with LIQUOR and AUTOMOTIVE PRODUCTS.
The significance of a rule can be measured in terms of support and confidence and a host of additional supporting measures; for example, Basket Significance (Value), Target Category Signifcance (Value), Basket Significance (Customers), Target Category Signifcance (Customers), Basket Significance (Transactions), Target Category Signifcance (Transactions), and so on..
This model mines the monthly purchases of individual customers and discovers rules about the categories that are frequently bought in groups by customers.
The purchase patterns are designed to be generated monthly for each individual store. Therefore, the APASS models are created every month for each store.
The following item attributes are the source variables:
Case Id Alt (PK)
Store ID
Year
Month
ID
Name
Value
Note:
If the mining must be performed at multiple levels, such as category, subcategory, item, there may be multiple source tables.The mined patterns and rules are visible through the target view CUST_CATEGORY_MIX_APASS_RULES with the following columns and can be displayed in an OBIEE report:
STORE_ID - Store ID
YEAR - Year
MONTH - Month
MODEL_NAME - Model Name (CCM_MDL_APASS_<Store_ID>)
MODEL_TYPE - Model Type (APASS)
MODEL_TYPE_DESC - Model Description (Apriori Association)
ANALYSIS_NAME - Analysis Name (CUST_CATEGORY_MIX)
ANALYSIS_DESC - Analysis Description (Customer Category Mix Analysis)
RULE_ID - Rule Id .. IF (antecedent) THEN (consequent) END
RULE_ANTECEDENT_ITEMS - List of Categories making up the IF part of the Category Basket (1 upto max as specified in settings while building the model)
RULE_CONSEQUENT_ITEMS - List of Categories making up the THEN part of the Category Basket (usually 1 category)
RULE_SUPPORT - Support (number of cases in the input dataset which pertain to the current basket)
RULE_CONFIDENCE - Confidence (Probability of the THEN part of the Rule coming true based on input dataset)
RULE_DISPLAY_ORDER - Default or recommended display order of the rules. Critical for DT models. Not critical for APASS models.
RULE_LENGTH - Number of Categories in the antecedent (IF) part of the Rule
BSKT_CTGRY_COUNT - Number of categories in the Category Basket (includes antecedent and consequent)
BSKT_ALL_SLS_VAL - Sales Value (total) for all categories in the Category Basket
BSKT_ALL_SLS_UNITS - Sales Units (total) for all categories in the Category Basket
BSKT_ALL_TRX_COUNT - Transaction Count for all categories in the Category Basket
BSKT_ALL_CUST_COUNT - Customer Count for all categories in the Category Basket
BSKT_ALL_SLS_VAL_SIGN - Significance of Category Basket within Store per Sales Value ....... ratio of Basket Sales Value to Store wide Sales Value (all categories)
BSKT_ALL_SLS_UNITS_SIGN - Significance of Category Basket within Store per Sales Units ....... ratio of Basket Sales Units to Store wide Sales Units (all categories)
BSKT_ALL_TRX_COUNT_SIGN - Significance of Category Basket within Store per Transaction Count.. ratio of Basket Transaction Count to Store wide Transaction Count (all categories)
BSKT_ALL_CUST_COUNT_SIGN - Significance of Category Basket within Store per Customer Count......ratio of Basket Customer Count to Store wide Customer Count (all categories)
BSKT_ALL_AVG_SLS_VAL_TRX - Avg Sales Value per Transaction (within Category Basket)
BSKT_ALL_AVG_SLS_VAL_CUST - Avg Sales Value per Customer (within Category Basket)
BSKT_ALL_AVG_SLS_UNITS_TRX - Avg Sales Units per Transaction (within Category Basket)
BSKT_ALL_AVG_SLS_UNITS_CUST - Avg Sales Units per Customer (within Category Basket)
BSKT_TGT_SLS_VAL - Sales Value of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_SLS_UNITS - Sales Units of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_TRX_COUNT - Transaction Count of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_CUST_COUNT - Customer Count of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_SLS_VAL_SIGN - Significance of the Target Category within Category Basket per Sales Value ..... ratio of Target Category Sales Value to Basket Sales Value (all categories in Basket)
BSKT_TGT_SLS_UNITS_SIGN - Significance of the Target Category within Category Basket per Sales Units ..... ratio of Target Category Sales Units to Basket Sales Units (all categories in Basket)
BSKT_TGT_TRX_COUNT_SIGN - Significance of the Target Category within Category Basket per Transaction Count ..... ratio of Target Category Transaction Count to Basket Transaction Count (all categories in Basket)
BSKT_TGT_CUST_COUNT_SIGN - Significance of the Target Category within Category Basket per Customer Count ..... ratio of Target Category Transaction Count to Basket Customer Count (all categories in Basket)
BSKT_TGT_AVG_SLS_VAL_TRX - Avg Sales Value per Transaction (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_VAL_CUST - Avg Sales Value per Customer (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_UNITS_TRX - Avg Sales Units per Transaction (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_UNITS_CUST - Avg Sales Units per Customer (Target Category .. consequent category /THEN part of RULE)
STR_ALL_SLS_VAL - Sales Value (total) for all categories in Store
STR_ALL_SLS_UNITS - Sales Units (total) for all categories in Store
STR_ALL_TRX_COUNT - Transaction Count for all categories in Store
STR_ALL_CUST_COUNT - Customer Count for all categories in Store
This model addresses the business problem of discovering the impact of customer characteristics on customers' loyalty to a store.
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF YEARS OF RESIDENCE IS (8 - 10) AND HOUSEHOLD_SIZE IS (3+) THEN CUSTOMER IS GROUP A
IF YEARS OF RESIDENCE IS (1 ñ 3) AND HOUSEHOLD_SIZE IS (LESS THAN 3) THEN CUSTOMER IS GROUP E
The discovered rules help explain the loyalty of a customer.
This model mines the Customer and Account demographic characteristics of Customers to identify the key attribute influencing the Customer Loyalty scores (RFMP Category Value).
The rules are designed to be generated monthly. Therefore, one ABN model and one DT model is created every month across all stores combined using the Customer Loyalty variable as the target.
The following attributes of customers are identified from the data warehouse tables as source variables for the models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Year
Month
Customer Number
Registered as Gift Receiver
Registered as Gift Giver
Customer Occasion Type This Month
Campaign This Month
Membership Account Type Code (None if the customer does not have any account; the last used account if the customer has multiple accounts)
Life-To-Date Points
Available Points
Customer Account Type (None if the customer does not have any account; the last used account if the customer has multiple accounts)
Customer Identity Required Indicator
Customer Identity Type Name
Customer Group Code (None if the customer does not belong to any group)
Age
Marital Status
Gender
Income
Race
Education
Profession
Household Size
Years of Residence
Demography Group Name
Customer County or District
Customer City
Customer State
Customer Country
Customer World Region
The RFMP algorithms provide functionality to group customers into quartiles, deciles, and quintiles. Each customer falls into one of the following five loyalty categories based on the RFMP quintile he or she belongs to in a particular month:
Group A (RFMP Quintile 5)
Group B (RFMP Quintile 4)
Group C (RFMP Quintile 3)
Group D (RFMP Quintile 2)
Group E (RFMP Quintile 1)
Note:
The definition of each of the loyalty types as well as the number of loyalty types may vary with each implementation.The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of finding product categories that are frequently bought by frequent shoppers. Finding these product categories can help in optimizing Merchandising and Category Mix options that relate to Store Layout, Display and Frontage, Promotional Campaigns, Co-branding, and others.
The model is used to understand the Categories purchased by a Frequent Shopper in a typical transaction in terms of the components like the Categories in the Basket, Target Category in a Basket and additional information like Basket Significance (Sales Value), Target Category Significance (Sales Value) which are generated from regular Customer Transactional data.
Using Oracle Data Mining, the KPIs are modeled with the APRIORI algorithm utilised by the Association Rules model. The model type used for Association Rules with Apriori Algorithm is APASS. This model type is an example of Unclassified Learning since the Categories (or Target Category) which constitute the Category Basket are not inferred or guided (as part of data preparation) but are generated by the model itself.
This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF CUSTOMER HAS BOUGHT 'FILM ACCESSORIES' THEN CUSTOMER IS LIKELY TO BUY 'BEAUTY' [Support - 80%, Confidence - 70%]
IF CUSTOMER HAS BOUGHT 'FILM ACCESSORIES' AND CUSTOMER HAS BOUGHT 'MAGAZINE' AND CUSTOMER HAS BOUGHT 'PET' THEN CUSTOMER IS LIKELY TO BUY 'BEAUTY' [Support – 48%, Confidence - 82%]
Category Basket Significance of ('FILM ACCESSORIES', 'MAGAZINES', PET', 'BEAUTY') is 62% of Sales Value => The Sales from the 4 categories in Category Basket ('FILM ACCESSORIES', 'MAGAZINES', PET', 'BEAUTY') account for 62% of the Total Sales across all categories in that particular store.
The Category Basket Significance (Sales Value) KPI allows us to filter out Rules which may be insignificant from a Basket Sales Value perspective.
Target Category Significance of (BEAUTY') is 70% of the Basket Sales Value => The Sales from the Target Category ('BEAUTY') account for 70% of the Total Sales from the Category Basket ('FILM ACCESSORIES', 'MAGAZINES', PET', 'BEAUTY') in that particular store.
The Target Category Significance (Sales Value) KPI allows us to filter out Rules determining insignificant Customer Purchases (insignificant Target Category). In other words, it helps us to extract Rules which relate to singificant Customer Purchases, where the Target Category is significant within the Basket of Categories (from a Sales Value perspective). Identifying this information can be useful from a campaign/promotion/upsell perspective.
IF CUSTOMER HAS BOUGHT 'BAKERY' THEN CUSTOMER IS LIKELY TO BUY 'BEAUTY' [Support – 36%, Confidence - 90%]
The discovered rules help explain purchase patterns of frequent shoppers, for example:
The chances of a frequent shopper buying a BEAUTY product increases from 30% to 70% if he or she purchases a FILM ACCESSORIES product. It further increases to 74% if the frequent shopper buys a MAGAZINES product and to 82% if he or she also buys a PET product.
The chances of a customer buying a BEAUTY product is 82% if he or she has purchased a product from categories 'FILM ACCESSORIES', 'MAGAZINES' and 'PET'. This Rule is about 62% significant in terms of Basket Value (Sales Value of all 4 categories in Basket) and The Target Category (BEAUTY) significance is about 70% of the Basket.
BEAUTY products are very rarely (about 3% of the time) sold with BAKERY products.
The significance of a rule can be measured in terms of support and confidence and a host of additional supporting measures like Basket Significance (Value), Target Category Signifcance (Value), Basket Significance (Customers), Target Category Signifcance (Customers), Basket Significance (Transactions), Target Category Signifcance (Transactions), and so on.
This model mines the monthly purchases of individual frequent shoppers and discovers rules about the categories that are frequently bought in groups by frequent shoppers.
The purchase patterns are designed to be generated monthly for each individual store. Therefore, APASS models are created every month for each store.
The following item attributes are the source variables:
Case Id Alt (PK)
Store ID
Year
Month
ID
Name
Value
Note:
If the mining must be performed at multiple levels, such as category, subcategory, item, and others, there may be multiple source tables.The mined patterns and rules are visible through the target view FS_CATEGORY_MIX_APASS_RULES with the following columns and can be displayed in an OBIEE report:
STORE_ID - Store ID
YEAR - Year
MONTH - Month
MODEL_NAME - Model Name (CCM_MDL_APASS_<Store_ID>)
MODEL_TYPE - Model Type (APASS)
MODEL_TYPE_DESC - Model Description (Apriori Association)
ANALYSIS_NAME - Analysis Name (CUST_CATEGORY_MIX)
ANALYSIS_DESC - Analysis Description (Customer Category Mix Analysis)
RULE_ID - Rule Id .. IF (antecedent) THEN (consequent) END
RULE_ANTECEDENT_ITEMS - List of Categories making up the IF part of the Category Basket (1 upto max as specified in settings while building the model)
RULE_CONSEQUENT_ITEMS - List of Categories making up the THEN part of the Category Basket (usually 1 category)
RULE_SUPPORT - Support (number of cases in the input dataset which pertain to the current basket)
RULE_CONFIDENCE - Confidence (Probability of the THEN part of the Rule coming true based on input dataset)
RULE_DISPLAY_ORDER - Default or recommended display order of the rules. Critical for DT models. Not critical for APASS models.
RULE_LENGTH - Number of Categories in the antecedent (IF) part of the Rule
BSKT_CTGRY_COUNT - Number of categories in the Category Basket (includes antecedent and consequent)
BSKT_ALL_SLS_VAL - Sales Value (total) for all categories in the Category Basket
BSKT_ALL_SLS_UNITS - Sales Units (total) for all categories in the Category Basket
BSKT_ALL_TRX_COUNT - Transaction Count for all categories in the Category Basket
BSKT_ALL_CUST_COUNT - Customer Count for all categories in the Category Basket
BSKT_ALL_SLS_VAL_SIGN - Significance of Category Basket within Store per Sales Value ....... ratio of Basket Sales Value to Store wide Sales Value (all categories)
BSKT_ALL_SLS_UNITS_SIGN - Significance of Category Basket within Store per Sales Units ....... ratio of Basket Sales Units to Store wide Sales Units (all categories)
BSKT_ALL_TRX_COUNT_SIGN - Significance of Category Basket within Store per Transaction Count.. ratio of Basket Transaction Count to Store wide Transaction Count (all categories)
BSKT_ALL_CUST_COUNT_SIGN - Significance of Category Basket within Store per Customer Count......ratio of Basket Customer Count to Store wide Customer Count (all categories)
BSKT_ALL_AVG_SLS_VAL_TRX - Avg Sales Value per Transaction (within Category Basket)
BSKT_ALL_AVG_SLS_VAL_CUST - Avg Sales Value per Customer (within Category Basket)
BSKT_ALL_AVG_SLS_UNITS_TRX - Avg Sales Units per Transaction (within Category Basket)
BSKT_ALL_AVG_SLS_UNITS_CUST - Avg Sales Units per Customer (within Category Basket)
BSKT_TGT_SLS_VAL - Sales Value of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_SLS_UNITS - Sales Units of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_TRX_COUNT - Transaction Count of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_CUST_COUNT - Customer Count of Target Category (consequent category /THEN part of RULE)
BSKT_TGT_SLS_VAL_SIGN - Significance of the Target Category within Category Basket per Sales Value ..... ratio of Target Category Sales Value to Basket Sales Value (all categories in Basket)
BSKT_TGT_SLS_UNITS_SIGN - Significance of the Target Category within Category Basket per Sales Units ..... ratio of Target Category Sales Units to Basket Sales Units (all categories in Basket)
BSKT_TGT_TRX_COUNT_SIGN - Significance of the Target Category within Category Basket per Transaction Count ..... ratio of Target Category Transaction Count to Basket Transaction Count (all categories in Basket)
BSKT_TGT_CUST_COUNT_SIGN - Significance of the Target Category within Category Basket per Customer Count ..... ratio of Target Category Transaction Count to Basket Customer Count (all categories in Basket)
BSKT_TGT_AVG_SLS_VAL_TRX - Avg Sales Value per Transaction (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_VAL_CUST - Avg Sales Value per Customer (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_UNITS_TRX - Avg Sales Units per Transaction (Target Category .. consequent category /THEN part of RULE)
BSKT_TGT_AVG_SLS_UNITS_CUST - Avg Sales Units per Customer (Target Category .. consequent category /THEN part of RULE)
STR_ALL_SLS_VAL - Sales Value (total) for all categories in Store
STR_ALL_SLS_UNITS - Sales Units (total) for all categories in Store
STR_ALL_TRX_COUNT - Transaction Count for all categories in Store
STR_ALL_CUST_COUNT - Customer Count for all categories in Store
This model addresses the business problem of identifying the extent to which item (product) characteristics influence the items' sales KPIs.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF FOOD STAMP is allowed AND ENVIRONMENT TYPE CODE is (NORMAL) AND Item Basket Analysis Report Data Mining Reports 6-21 FREQUENT_SHOPPER_POINTS (800 - 999) THEN AVERAGE BASKET VALUE is (HIGHEST)
IF FOOD STAMP is NOT allowed AND ENVIRONMENT TYPE CODE is (REFRIGERATED) AND FREQUENT_SHOPPER_POINTS (800 - 999) THEN AVERAGE BASKET VALUE is (LOWEST)
The discovered rules draw the profile of items that have the extreme values of the target KPI. For example, the Examples of Desired Rules for this model discover the profiles of items showing extreme average basket values.
This model identifies which key attributes of an item influence the number of baskets sold, average basket value, and size in a particular store. This model mines the various attributes of items. It takes the binned variables one at a time for Total Basket Count, Average Basket Value, and Average Basket Size as the target variable of an ABN model and DT model with a single feature and discovers rules described in terms of item characteristics.
The rules are designed to be generated monthly for each individual store. Therefore, nine ABN and nine DT models are created every month for each store using the following variables as targets in this order:
Total Basket Count Quartile (TBCQR)
Total Basket Count Quintile (TBCQN)
Total Basket Count Decile (TBCDE)
Average Basket Value Quartile (ABVQR)
Average Basket Value Quintile (ABVQN)
Average Basket Value Decile (ABVDE)
Average Basket Size Quartile (ABSQR)
Average Basket Size Quintile (ABSQN)
Average Basket Size Decile (ABSDE)
The following item attributes are identified from the data warehouse tables as source variables for the ABN and DT models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Store ID
Year
Month
Item ID
Brand Name
Category Name
Department Name
Customer Pickup Type Code
Discount Indicator
Environment Type Code
Hazardous Material Type Code
Perishable Indicator
Kit Set Code
Order Collection Code
Price Audit Flag
Sale Weight or Unit Count Code
Security Required Type Code
Sell Unit Landed Cost Amount
Sell Unit Last Received Base Cost Amount
Sell Unit Last Received Net Cost Amount
Item Sale Unit Price Amount
Shrink Flag
Substitute Identified Indicator
Swell Flag
Item Usage Code
Vendor Item Number
Max Shipping Capability
Min Order Quantity
Sale Unit per Packet Unit Count
Shipping Capability Units
Store Order Allowed Flag
Store Receipt Allowed Flag
Style Description
Terms Code
Vendor Number
Vendor Class Code
Buy Status Indicator
Credit Limit Offered
Inform Government Indicator
Vendor Number of Years in Business
Pay Status Indicator
Competitor Retail Item Name
Competitor Name
Competitor Item Local Advertising Flag
Competitor Item On Promotion Flag
Competitor Item Promotion Store Coupon Indicator
Competitor Sale Unit Price Amount
Allow Coupon Multiply Indicator
Allow Food Stamp Indicator
Coupon Restricted Indicator
Electronic Coupon Flag
Employee Discount Allowed Flag
Frequent Shopper Points
Frequent Shopper Points Eligibility Indicator
Give Away Flag
Item Tender Restriction Group Code
Manufacturer
Manufacturer Family Code
Maximum Sale Unit Count
Price Entry Required Flag
Prohibit Repeat Key Flag
Prohibit Return Flag
Selling Status Code
Visual Verify Price Flag
Weight Entry Required Flag
The mined patterns and rules are visible through in a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Store ID (PK)
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of building a profile of item (product) characteristics regarding POS losses.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
See:
Oracle Retail Data Model Operations Guide for a sample report based on this model.This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF EMP_DISCOUNT_ALLOWED_IND IS 'Y' AND SCANNED_PER_UNITS_SOLD IS (0.8-1) THEN THEFT_AMOUNT_TO_SALES_AMOUNT_QUARTILE IS THE HIGHEST
IF
SCANNED_PER_UNITS_SOLD IS (0.8-1)
AND
DEPARTMENT_NAME In ('BEVERAGE', 'CIGARETTES')
THEN
SHRINK_AMOUNT_TO_SALES_AMOUNT_QUARTILE IS THE HIGHEST
The discovered rules describe correlations between item shrinkage and item characteristics.
This model mines the POS transactions along with the item attributes to identify their impact on Total Shrink Count, Total Shrink Amount, Shrink as a percentage of Sales, Total Theft Count, Total Theft Amount, and Theft as a percentage of Sales.
The rules are designed to be generated monthly for each individual store. Therefore, eighteen ABN and eighteen DT models are created every month for each of the stores using the following variables as targets in this order:
Total Shrink Count Quartile (TSCQR)
Total Shrink Count Quintile (TSCQN)
Total Shrink Count Decile (TSCDE)
Total Shrink Amount Quartile (TSAQR)
Total Shrink Amount Quintile (TSAQN)
Total Shrink Amount Decile (TSADE)
Shrink as a percentage of Sales Quartile (STSQR)
Shrink as a percentage of Sales Quintile (STSQN)
Shrink as a percentage of Sales Decile (STSDE)
Total Theft Count Quartile (TTCQR)
Total Theft Count Quintile (TTCQN)
Total Theft Count Decile (TTCDE)
Total Theft Amount Quartile (TTAQR)
Total Theft Amount Quintile (TTAQN)
Total Theft Amount Decile (TTADE)
Theft as a percentage of Sales Quartile (TTSQR)
Theft as a percentage of Sales Quintile (TTSQN)
Theft as a percentage of Sales Decile (TTSDE)
The following attributes of POS and items are identified from the data warehouse tables as source variables for the models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Store
Year
Month
Item ID
Brand Name
Category Name
Department Name
Customer Pickup Type Code
Discount Indicator
Hazardous Material Type Code
Perishable Indicator
Kit Set Code
Order Collection Code
Price Audit Flag
Sale Weight or Unit Count Code
Security Required Type Code
Sell Unit Landed Cost Amount
Sell Unit Last Received Base Cost Amount
Sell Unit Last Received Net Cost Amount
Item Sale Unit Price Amount
Shrink Flag
Substitute Identified Indicator
Swell Flag
Item Usage Code
Vendor Item Number
Max Shipping Capability
Min Order Quantity
Sale Unit per Packet Unit Count
Shipping Capability Units
Store Order Allowed Flag
Store Receipt Allowed Flag
Style Description
Terms Code
Vendor Number
Vendor Class Code
Buy Status Indicator
Credit Limit Offered
Inform Government Indicator
Vendor Number of Years in Business
Pay Status Indicator
Competitor Retail Item Name
Competitor Name
Competitor Item Local Advertising Flag
Competitor Item On Promotion Flag
Competitor Item Promotion Store Coupon Indicator
Competitor Sale Unit Price Amount
Allow Coupon Multiply Indicator
Allow Food Stamp Indicator
Coupon Restricted Indicator
Electronic Coupon Flag
Employee Discount Allowed Flag
Frequent Shopper Points
Frequent Shopper Points Eligibility Indicator
Give Away Flag
Item Tender Restriction Group Code
Manufacturer
Manufacturer Family Code
Maximum Sale Unit Count
Price Entry Required Flag
Prohibit Repeat Key Flag
Prohibit Return Flag
Selling Status Code
Visual Verify Price Flag
Weight Entry Required Flag
Total Number of Retail Transactions For Item
Total Amount of Retail Transactions For Item
Average Amount Per Retail Transaction For Item
Number of Distinct Currency Used For Item
Total Units Sold For Item
Average Units Sold Per Retail Transaction For Item
Total Idle Interval For Item (This attribute is the sum of idle intervals of all transactions that contain this Item)
Average Idle Interval Per Retail Transaction For Item
Total Ring Interval For Item (This attribute is the sum of ring intervals of all transactions that contain this item)
Average Ring Interval Per Retail Transaction For Item
Total Tender Interval For Item (This attribute is the sum of tender intervals of all transactions that contain this item)
Average Tender Interval Per Retail Transaction For Item
Total Lock Interval For Item (This attribute is the sum of lock intervals before or after all transactions that contain this item)
Average Lock Interval Per Retail Transaction For Item
Total Line Items Scanned For Item (This attribute is the total number of times this item is scanned)
Average Line Items Scanned Per Units Sold For Item
Total Line Items Keyed For Item (This attribute is the total number of times this item is keyed)
Average Line Items Keyed Per Units Sold
Total Key Department Count For Item (This attribute is the total number of times this item is keyed by the department)
Average Key Department Count Per Units Sold
Total Service Charge For Item
Average Service Charge Per Retail Transaction For Item
Total Tax Amount For Item
Average Tax Amount Per Retail Transaction For Item
Total Number of Voided Transactions For Item
Average Number of Voided Transactions Per Retail Transaction For Item
Total Amount of Voided Transactions For Item
Average Amount of Voided Transactions Per Retail Transaction For Item
Average Amount of Voided Transaction as Percentage of Total Retail Transaction Amount For Item
Total Number of Discount Line Items For Item
Average Number of Discount Line Items Per Retail Transaction For Item
Total Amount of Discount Line Items For Item
Average Amount of Discount Line Items Per Retail Transaction For Item
Average Amount of Discount Line Items as Percentage of Total Retail Transaction Amount For Item
Total Number of Return Line Items For Item
Average Number of Return Line Items Per Retail Transaction For Item
Total Amount of Return Line Items For Item
Average Amount of Return Line Items Per Retail Transaction For Item
Average Amount of Return Line Items as Percentage of Total Retail Transaction Amount For Item
Total Number of Miscellaneous Fee Line Items For Item
Average Number of Miscellaneous Fee Line Items Per Retail Transaction For Item
Total Amount of Miscellaneous Fee Line Items For Item
Average Amount of Miscellaneous Fee Line Items Per Retail Transaction For Item
Average Amount of Miscellaneous Fee Line Items as Percentage of Total Retail Transaction Amount For Item
Total Number of Promotional Line Items For Item
Average Number of Promotional Line Items Per Retail Transaction For Item
Total Amount of Promotional Line Items For Item
Average Amount of Promotional Line Items Per Retail Transaction For Item
Average Amount of Promotional Line Items as Percentage of Total Retail Transaction Amount For Item
Total Number of Deposit Redemption Line Items For Item
Average Number of Deposit Redemption Line Items Per Retail Transaction For Item
Total Amount of Deposit Redemption Line Items For Item
Average Amount of Deposit Redemption Line Items Per Retail Transaction For Item
Average Amount of Deposit Redemption Line Items as Percentage of Total Retail Transaction Amount For Item
Control Transaction Attributes
Total Tax Exempt Transaction Count For Item
Average Tax Exempt Transaction Count Per Retail Transaction For Item
Tax Exempt Total Amount For Item
Average Tax Exempt Amount Per Retail Transaction For Item
Tax Exempt Total Amount as a Percentage of Total Retail Transaction Amount For
Item
Total Number of Store Coupons For Item
Average Number of Store Coupons Per Retail Transaction For Item
Average Number of Store Coupons Per Retail Transaction For Item
Total Amount of Store Coupons For Item
Average Amount of Store Coupons Per Retail Transaction For Item
Average Amount of Store Coupons as Percentage of Total Retail Transaction
Amount For Item
Total Markdown Count For Item
Average Markdown Count per Retail Transaction For Item
Markdown Total Amount For Item
Average Markdown Amount Per Retail Transaction For Item
Average Markdown Amount as a Percentage of Total Retail Transaction Amount
For Item
Total Employee Discount Count For Item
Average Employee Discount Per Retail Transaction For Item
Total Employee Discount Amount For Item
Average Employee Discount Amount Per Retail Transaction For Item
Average Employee Discount Amount as a Percentage of Retail Transaction
Amount For Item
Total Weighed Line Item Count For Item
Average Weighed Line Item Count Per Retail Transaction For Item
Total Weighed Line Item Amount For Item
Average Weighed Line Item Amount Per Retail Transaction For Item
Average Weighed Line Item Amount as a Percentage of Total Retail Transaction
Amount For Item
Total Layaway Payments Collected Count For Item
Average Layaway Payments Collected Count Per Retail Transaction For Item
Total Layaway Payments Collected Amount For Item
Average Layaway Payments Collected Amount Per Retail Transaction For Item
Average Layaway Payments Collected Amount as a Percentage of Total Retail
Transaction Amount For Item
Total Container Deposit Count For Item
Average Container Deposit Count Per Retail Transaction For Item
Total Container Deposit Amount For Item
Average Container Deposit Amount Per Retail Transaction For Item
Average Container Deposit Amount as a Percentage of Total Retail Transaction
Amount For Item
Total Redeemed Container Deposit Count For Item
Average Redeemed Container Deposit Count Per Retail Transaction For Item
Total Redeemed Container Deposit Amount For Item
Average Redeemed Container Deposit Amount Per Retail Transaction For Item
Average Redeemed Container Deposit Amount as a Percentage of Total Retail
Transaction Amount For Item
Total Cash Tender Count For Item
Average Cash Tender Count Per Retail Transaction For Item
Total Cash Tender Amount For Item
Average Cash Tender Amount Per Retail Transaction For Item
Average Cash Tender Amount as a Percentage of Total Retail Transaction Amount
For Item
Total Check Tender Count For Item
Average Check Tender Count Per Retail Transaction For Item
Total Check Tender Amount For Item
Average Check Tender Amount Per Retail Transaction For Item
Average Check Tender Amount as a Percentage of Total Retail Transaction
Amount For Item
Total Credit Card Tender Count For Item
Average Credit Card Tender Count Per Retail Transaction For Item
Total Credit Card Tender Amount For Item
Average Credit Card Tender Amount Per Retail Transaction For Item
Average Credit Card Tender Amount as a Percentage of Total Retail Transaction
Amount For Item
Total Debit Card Tender Count For Item
Average Debit Card Tender Count Per Retail Transaction For Item
Total Debit Card Tender Amount For Item
Average Debit Card Tender Amount Per Retail Transaction For Item
Average Debit Card Tender Amount as a Percentage of Total Retail Transaction
Amount For Item
Total Customer Account Tender Count For Item
Average Customer Account Tender Count Per Retail Transaction For Item
Total Customer Account Tender Amount For Item
Average Customer Account Tender Amount Per Retail Transaction
Average Customer Account Tender Amount as a Percentage of Total Retail
Transaction Amount
Total Gift Certificate Tender Count For Item
Average Gift Certificate Tender Count Per Retail Transaction For Item
Total Gift Certificate Tender Amount For Item
Average Gift Certificate Tender Amount Per Retail Transaction For Item
Amount For Item
Total Coupon Tender Count For Item
Average Coupon Tender Count Per Retail Transaction For Item
Total Coupon Tender Amount For Item
Average Coupon Tender Amount Per Retail Transaction For Item
Average Coupon Tender Amount as a Percentage of Total Retail Transaction
Amount For Item
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Store ID (PK)
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of detecting patterns in the flow of items, transactions, and amount across individual points of sale during different time periods.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF NUMBER_OF_HOUSEHOLDS IS<3000 AND STORE LOCATION TYPE IS 'Free Standing' AND PER CAPITA INCOME IS<3000 THEN Total Return Items Count Quartile IS THE LOWEST
IF NUMBER_OF_HOUSEHOLDS IS>5000 AND AVERAGE_DRIVE_TIME_MIN IS<15 AND (STORE_USAGE IS 'Store Within a Store' OR STORE_USAGE IS 'Department') AND STORE_OPEN_HOURS IS>=16 THEN Total Sale Transactions Count Quartile IS THE HIGHEST
The discovered rules describe the influence of trade area demographic characteristics and store characteristics on the amount of POS traffic at individual workstations over different periods throughout the day.
This model mines the various attributes of Store and Location. It takes the binned variables one at a time for Transaction Type, Transaction Count, and Transaction Amount (Sales) as the target variable of an ABN model and DT model with a single feature and discovers rules described in terms of Store, Location, and State Demographic attributes.
The rules are generated from the historical data across all time periods and store workstations. A total of twenty-seven ABN and twenty-seven DT models with a single feature are created using the following variables as targets in this order:
Total Sale Transactions Count Quartile (TSTCQR)
Total Sale Transactions Count Quintile (TSTCQN)
Total Sale Transactions Count Decile (TSTCDE)
Total Return Transactions Count Quartile (TRTCQR)
Total Return Transactions Count Quintile (TRTCQN)
Total Return Transactions Count Decile (TRTCDE)
Total Void Transactions Count Quartile (TVTCQR)
Total Void Transactions Count Quintile (TVTCQN)
Total Void Transactions Count Decile (TVTCDE)
Total Sale Items Count Quartile (TSICQR)
Total Sale Items Count Quintile (TSICQN)
Total Sale Items Count Decile (TSICDE)
Total Return Items Count Quartile (TRICQR)
Total Return Items Count Quintile (TRICQN)
Total Return Items Count Decile (TRICDE)
Total Void Items Count Quartile (TVICQR)
Total Void Items Count Quintile (TVICQN)
Total Void Items Count Decile (TVICDE)
Total Sale Amount Quartile (TSAQR)
Total Sale Amount Quintile (TSAQN)
Total Sale Amount Decile (TSADE)
Total Return Amount Quartile (TRAQR)
Total Return Amount Quintile (TRAQN)
Total Return Amount Decile (TRADE)
Total Void Amount Quartile (TVAQR)
Total Void Amount Quintile (TVAQN)
Total Void Amount Decile (TVADE)
The following attributes of store workstation and time periods are identified from the data warehouse tables as source variables for the ABN and DT models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Store Workstation ID
Workstation Name
POS Type
Equipment Type
Manufacturer Name
Terminal Model Number
Terminal Type (POS register, Goods receipt terminal, and others)
Outside Indicator
Store Name
Store Manager Name
Store Usage (Store, Store within a store, Department, Kiosk, and others)
Store Status (Under construction, New, and others)
Total Open Hours
Store Location Type (Free standing, Shopping Center, CBD, SBD, NBD, and others)
Primary Trade Area Code
Trade Area Coverage
Market Area Code
Market Area Type (Urban, Suburban, Rural, and others)
Market Area Population
Pull Factor
Total Commuter Population
Peak Season Population
Tourist Population
Average Drive Time
Number of Households
Average Household Size
Average Family Size
Per Capita Income
Average Number of Vehicles per Household
Shopping Center Type (Strip Center, Mall, and others)
Store Concept (Convenience, General Merchandise, Fashion oriented, and others)
Terrain (Mountain, Inland, Desert)
Total Built-up Area
Total Super Built-up Area
Number of Functional Months
Usable Area
Inventory Area
Selling Area
New Store Indicator
Store Price Index
Number of Levels of Floors
Number of Window Displays
Area of Window Displays
Fitting Rooms Available
Number of External Signs
Rest Rooms Available
Type Of Parking
Distance to Nearest Cross
Distance from Market Area Center
Store County or District
Store City
Store State or Province
State Population
State Sales
Store Country
Store World Region
Date Time Key
Hour of Day (1-24)
Calendar Day of Week (Sun-Sat)
Calendar Day of Month (1-30 or 31)
Holiday Indicator
Weekend Indicator
Calendar Week Number
Fiscal Week Number
Advertising Week Number
Planning Week Number
Calendar Half Month Number
Fiscal Half Month Number
Calendar Month Number
Fiscal Month Number
Advertising Period Number
Planning Period Number
Calendar Quarter Number
Fiscal Quarter Number
Advertising Quarter Number
Planning Quarter Number
Calendar Half Year Number
Fiscal Half Year Number
Calendar Year Number
Fiscal Year Number
Advertising Year Number
Planning Year Number
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
This model addresses the business problem of building a profile of organization (store) characteristics regarding shrinkage.
The KPIs are converted into categorical variables using standard database binning operations. The categorical variables are modeled as a classification model to identify or predict the impact of various independent variables (attributes) on the dependent target variable (KPI - categorical).
Using Oracle Data Mining, the KPIs are modeled using two popular Classification Algorithms - Adaptive Bayes Network (ABN) and Decision Tree (DT).
Adaptive Bayes Network (ABN) algorithm is used to build a fast scalable model with scalable rules whereas the Decision Tree (DT) algorithm is used when explicit rules explaining predictions are needed.
This section provides examples of the desired rules.
Note:
Discovery rules are parsed to make them easier to read (replacing the Column or Attribute names with descriptions), removing keywords or phrases like "isIn" with =, "AND" with "and", and so on.IF STORE IS NEW and NUMBER OF WINDOW DISPLAYS IS (4 - 5) and STORE DEPARTMENT IS (RETURN) THEN STORE THEFT AMOUNT IS THE HIGHEST
IF STORE IS NEW and NUMBER OF WINDOW DISPLAYS IS (4 - 5) and STORE DEPARTMENT IS (GIFT) THEN STORE THEFT AMOUNT IS THE LOWEST
The discovered rules describe correlations between shrinkage and store characteristics.
This analysis identifies the extent to which key store characteristics influence shrinkage and theft. This model mines the various attributes of stores. It takes the binned variables one at a time for Shrink and Thefts as the target variable of an ABN model and DT model with a single feature and discovers rules described in terms of store attributes.
The rules are designed to be generated monthly. Therefore, eighteen ABN and eighteen DT models are created every month across all stores using the following variables as targets in this order:
Total Shrink Count Quartile (TSCQR)
Total Shrink Count Quintile (TSCQN)
Total Shrink Count Decile (TSCDE)
Total Shrink Amount Quartile (TSAQR)
Total Shrink Amount Quintile (TSAQN)
Total Shrink Amount Decile (TSADE)
Shrink as a percentage of Sales Quartile (STSQR)
Shrink as a percentage of Sales Quintile (STSQN)
Shrink as a percentage of Sales Decile (STSDE)
Total Theft Count Quartile (TTCQR)
Total Theft Count Quintile (TTCQN)
Total Theft Count Decile (TTCDE)
Total Theft Amount Quartile (TTAQR)
Total Theft Amount Quintile (TTAQN)
Total Theft Amount Decile (TTADE)
Theft as a percentage of Sales Quartile (TTSQR)
Theft as a percentage of Sales Quintile (TTSQN)
Theft as a percentage of Sales Decile (TTSDE)
The following attributes of associates are identified from the data warehouse tables as source variables for the ABN and DT models (note that a few of these variables are unique identifiers and are treated as supplementary variables):
Case Id Alt (PK)
Year
Month
Store ID
Store Name
Store Manager Name
Store Usage (Store, Store within a store, Department, Kiosk, and others)
Store Status (Under construction, New, and others)
Total Open Hours
Store Location Type (Free standing, Shopping Center, CBD, SBD, NBD, and others)
Primary Trade Area Code
Trade Area Coverage
Market Area Code
Market Area Type (Urban, Suburban, Rural, and others)
Market Area Population
Pull Factor
Total Commuter Population
Peak Season Population
Tourist Population
Average Drive Time
Number of Households
Average Household Size
Average Family Size
Per Capita Income
Average Number of Vehicles per Household
Shopping Center Type (Strip Center, Mall, and others)
Store Concept (Convenience, General Merchandise, Fashion oriented, and others)
Terrain (Mountain, Inland, Desert)
Total Built-up Area
Total Super Built-up Area
Number of Functional Months
Usable Area
Inventory Area
Selling Area
New Store Indicator
Store Price Index
Number of Levels of Floors
Number of Window Displays
Area of Window Displays
Fitting Rooms Available
Number of External Signs
Rest Rooms Available
Type Of Parking
Distance to Nearest Cross
Distance from Market Area Center
Store County or District
Store City
Store State or Province
State Population
State Sales
Store Country
Store World Region
The mined patterns and rules are visible through a target view with the following columns and can be displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Rule ID (PK)
Performance Measure
Measure Value
Associate Profile
Prediction Count
Record Count
Support
Confidence
Rule Display Order
A new target view representing the Model Signature outlining the attribute structure of the model (built using an ABN or DT algorithm) is also available.
The Model Signature Target View contains the following columns and is also displayed in an OBIEE report:
Analysis Name (PK)
Analysis Desc
Year (PK)
Month (PK)
Model Type
Model Type Desc
Model Name (PK)
Performance Measure
Attribute Name (PK)
Attribute Type
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