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5 Customer Segmentation
This chapter describes the Customer Segmentation module.
Introduction
Customer segmentation is an enterprise-specific solution that uses data mining to group customers based on customer attributes and customer transactions. The retailer can use this information to describe and predict customer behavior. It provides the retailer with a vehicle to target customers with offers, pricing, assortment, and experience.
Retailers understand that shoppers are heterogeneous in nature, that they possess different wants and needs, and that it is impossible to satisfy them all. Retailers can differentiate themselves from their competitors by specializing and offering goods and services that are tailored to one or more market segments.
Customer Segmentation can be used to group customers and to discover hidden customer segments based on the contents of customer shopping baskets and the number of shopping trips they make. Loyalty card data is used to determine if these customer segments differ in terms of socio-demographic or lifestyle characteristics and whether these characteristics can be used to target different customer segments with more relevant product offers.
Retailers can create localized assortments and use customer insights to determine which products to offer by location or channel. This provides insights into the importance of a product to key customer segments and helps when making drop or keep decisions about products within an assortment.
This science-driven tool helps to automate segmentation in a repeatable process and bridges the gap between targeted marketing to targeted assortments.
Features
The key features of Customer Segmentation include:
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Scenario-based segment generation, based on customer attributes, customer behavior, and transactions.
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Attribute importance and correlation mining to identify significant attributes and their associations.
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The ability to generate granular customer segments via the UI. These customer segments include departments such as men, women, and children, health and beauty, or groceries.
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A three-step segment-generation process.
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What-if capabilities that can be used to create multiple segmentation scenarios and then measure them against one another. This can help ensure that the most appropriate segments are used by the applicable planning and execution processes.
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Automatic ranking of segment scenarios to support what-if comparisons.
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Recommendations for the optimal segment scenario and number of segments.
Table 5-1 Cluster Criteria Overview Tab
| Field | Description |
|---|---|
| Name | The criteria ID and user-assigned name of the segment. |
| Segment By | The Segment By option used for the segment. |
| Created By | The name of the user who created the segment. |
| Last Updated By | The name of the user who most recently updated the segment. |
| Last Updated On | The date when the segment criteria were most recently updated. |
| Status | The most recent, up-to-date status across the scenarios for the segment criteria. Value include Created, Ready for Preview, Ready for Approval, Approved, and Rejected. |
| Period Count | The number of calendar nodes defined for the criteria. Hover over the count in order to see a list of the calendar keys associated with the criteria. |
| Merchandise Count | The number of merchandise nodes defined for the criteria. Hover over the count in order to see a list of the merchandise keys associated with the criteria. |
| Location Count | The number of location nodes defined for the criteria. Hover over the count in order to see a list of the location keys associated with the criteria. |
Segmentation Criteria
The following segmentation criteria are supported by default:
Customer Demographics
This descriptive segmentation technique leverages customer loyalty programs and demographic information (such as residence, profession, age, gender, ethnicity, marital status, and education) about customers to generate demographics based on customer segments.
RFM and Customer Behavior
Segmentation based on purchase behavior aims at discovering groups of customers who exhibit similar purchasing behavior. However, the definition of behavior in this context includes many factors. For example, retailers may want to distinguish between light and heavy users, regular stock-up shoppers versus emergency top-up shoppers, lunchtime shoppers versus evening shoppers, home and daytime shoppers versus work and weekend shoppers, or fastcheckout customers versus regular checkout customers. The two important behavioral dimensions for understanding customer motivations are visit behavior (identified by the time of day and the day of week that the visits take place) and shopping behavior (identified by the customer’s spend dispersion across categories purchased during the trip).
Category Purchase Behavior
Another type of behavior segmentation aims at segmenting the customers who seek similar benefits when evaluating and choosing or purchasing products. These benefits can be measures such as economical price, bulk products, durability, or free shipping. Here, the segment process considers factors that capture customer sensitivity to price and promotions for each category. This can help retailers to segment customers by distinguishing predicted customer responses to the targeted or general promotion of products.
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Execute version. This is available when the setup is complete.
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Create a customer segment using an existing version. This opens the Generate Customer Segment tab. The New Segment Criteria pop-up is displayed, and the default values are filled in. (See the defaults that are selected on selection of the version in segment criteria.) This option is only available if the version has a status of Ready for Use.
Table 5-2 Version Details
| Name | Description |
|---|---|
| Name | The name of the version. |
| Criteria Count | The number of criteria associated with the version. When you click the link, a pop-up is displayed that lists the segment criteria details that are associated with the version. |
| Last Updated By | The name of the user who updated the version most recently. |
| Last Updated On | The date when the version was last updated. |
| Created By | The name of the user who created the version. |
| Status | The current status of the version. Values include Version Setup Complete, Filtering Completed, Sampling Completed Successfully, Attribute Mining Completed Successfully, Version Ready for Use. |
| Merchandise | The merchandise node defined for the version. |
| Location | The location node defined for the version. |
| System Generated Version | This column provides a flag indicating whether or not the version has been created using a batch process and is set up with a default configuration. |
Version Criteria Pop-up
This section describes the Version Criteria pop-up, shown in Figure 5-4.
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override these top categories further while defining versions for merchandise. By default, the top categories in the selected merchandise are driven based on the sales share for each category. You can remove categories to reduce processing time and storage, using the user interface and adding emerging to the top category list.
Version Status
The version status has one of the following values.
Table 5-4 Version Status
| Status | Description |
|---|---|
| Version Setup Complete | Indicates that version setup is complete. |
| Filtering Completed with Errors | Indicates that version filtering has completed with errors. |
| Filtering Execution in Progress | Indicates that version filtering is in progress. |
| Filtering Completed Successfully | Indicates that version filtering has completed successfully. |
| Sampling Completed with Errors | Indicates that sampling has completed with errors. |
| Sampling Execution in Progress | Indicates that sampling execution is in progress. |
| Sampling Completed Successfully | Indicates that sampling has completed successfully. |
| Attribute Mining Completed with Errors | Indicates that attribute mining has completed with errors. |
| Attribute Mining Execution in Progress | Indicates that attribute mining execution is in progress. |
| Attribute Mining Completed Successfully | Indicates that attribute mining has completed successfully. |
| Version Ready for Use | Indicates that all three execution phases (filtering, sampling, and attribute mining) have completed successfully. Once the version is ready for use, it is ready for the creation of segment criteria. |
Generate Customer Segments Tab
The Generate Customer Segments tab is used to create segments and then model the segments with various scenarios in order to determine the best set of segments. It consists of three stages: Segment Criteria, Segment Results, and Segment Insights.
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Table 5-5 (Cont.) Pop-Up Details
| Field Name | Description |
|---|---|
| Scenario Executed | The number of scenarios executed for the segment. |
All Segment Criteria Scenario List
This displays the scenarios for the selected segment criteria in the All Segment Criteria tree.
Table 5-6 Scenario List
| Field Name | Description |
|---|---|
| Name | The name assigned to each scenario that has been created for the segment. |
| Status | Created, Ready for Preview, Ready for Approval, Completed with Errors, Approved, Rejected. |
| Rank | The system-calculated rank for the segment. |
| Optimal # of Segments | The system-calculated optimal number of segment centers. |
| User Preferred | Indicates whether or not the user prefers the segment. |
| System Preferred | Indicates whether or not the system prefers the segment. |
| # of Attributes | The number of attributes that were used in the segment. |
| Max. # of Segments | A user-provided value for the maximum segment centers that the segmenting process should consider. |
| Min. # of Segments | A user-provided value for the minimum segments centers that the segmenting process should consider. |
Segment Criteria
In this pop-up, you define the initial segmenting parameters for the segment criteria of a new segment. Note that the hierarchy type supported can be configured at the time of deployment.
Figure 5-10 illustrates how to use a simple approach to segmenting by selecting attributes from a Segment by. For example, you can select the RFM and Customer Behavior Segment by and generate segments using the total number of trips and the amount spent by customer.
Table 5-8 Effective Period
| Option | Description |
|---|---|
| Fiscal Period | If you select this option, choose the period and the subdivisions of that period from the drop-down lists. |
| Planning Period | Select from the range of values provided for the period. Planning periods are user-defined buying periods for a season or a season subset. |
| Select Dates | If you select this option, choose the start and end dates using the calendar pop-up. |
Summarization
Data summarization is available by default and set to either Category or Sub Category. It is applied to Category Purchase Driven Segment by. The segmentation process considers the top selected categories and their attributes and groups customers based on their sales patterns.
Source Time Period
Source time periods are selected based on the version selected for the segment criteria.
Table 5-9 Source Time Period
| Field | Description |
|---|---|
| Period Level | Select from Fiscal Year, Fiscal Quarter, Fiscal Period, or Fiscal Week. |
| Start Period | Once you select the Period Level, you select the starting subdivision within that period. |
| End Period | Once you select the Period Level, you select the ending subdivision within that period. |
Contextual Area
When you are creating new segment criteria, you can see details about the following parameters that can help you understand the segment you are creating.
Segment By Hierarchy
The following information is displayed when you select a template or use the icon to select the Segment by.
Table 5-10 Template Display
| Property | Description |
|---|---|
| Template Name | Name of template configured during deployment. |
| Description | Description of template. |
Table 5-10 (Cont.) Template Display
| Property | Description |
|---|---|
| Segment By | A predefined group of attributes that include Customer Demographics, RFM and Customer Behavior, Category Purchase Driven. These criteria types are sets of attributes. For example, customer |
| demographics are the properties of a customer. These properties can include ethnicity, income, and age. |
Segment By Primary Scenario
You see this when you select Segment by in the Criteria panel when you are setting the segment criteria parameters or when you select Segment by in the contextual area for the hierarchy.
The system displays the primary scenario, its preconfigured properties, and the significant attributes identified during the attribute importance process for each segment by.
The following information is displayed.
Table 5-11 Primary Scenario
| Property | Description |
|---|---|
| Name | The name of the primary scenario. |
| Status | Created, Ready for Preview, Ready for Approval, Completed with Errors, Approved, Rejected. |
| Maximum # segments | The maximum number of segments. The default value is 20. This is used for analyzing the segments. |
| Minimum # segments | The minimum number of segments. The default value is 1. This is used for analyzing the segments. |
| Attribute | A list of the attributes configured during segmentation. |
| Attribute importance | The weighted average across attributes and importance index for each attribute. |
Planning Period
This list displays the time period you selected for the segment criteria. This information is available only for planning periods, where it provides the start and end dates of the planning period. This content changes whenever the planning period is selected in Effective Period when you are setting segment criteria parameters.
Explore Data
Use the Explore Data pop-up to examine data for the segment criteria you defined. You can view the customer and attribute summary that provides input into the segmentation process.
Process
In this pop-up you can view criteria and attribute summaries as well as their related contextual BIs.
Summary
The Summary lists the criteria you initially selected to define the segment.
Table 5-12 Explore Data: Summary
| Field | Description |
|---|---|
| Name | The name you provided for the segment in the Segment Criteria stage. |
| Segment By | A predefined group of attributes that include Customer Demographics, RFM and Customer Behavior, Category Purchase Driven. These criteria types are sets of attributes. For example, customer demographics are the properties of a customer. These properties can include ethnicity, income, and age. |
| Merchandise | The merchandise level and nodes for the segment. |
| Location | The location level and nodes for the segment. |
| Fiscal Period | The time period for the segment. |
| Merchandise Hierarchy Type | Details about which type of hierarchy the segment criteria have been created for. |
Attribute Mining
This screen provides you with insights about the attribute mining process, which lists the attributes’ significance and their correlations. The attributes required for the customer segmentation process come from different dimensions such as customers, their households, demographics, and purchasing behaviors. This process helps to eliminate redundant attributes and to identify the attributes that may have the most influence on generating customer segment. The attribute summary displays information about data availability and data quality by providing the attributes’ distinct values, percentage of nulls, and statistics summaries, such as mean, median, and standard deviation.
Attribute Importance
Along with attribute summaries, the system generates an attribute importance index that sums up data quality, data distribution, and its representation of each attribute in the data. See Figure 5-11.
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Correlation Matrix
The correlation matrix displays attributes, attributes values (in the case of discrete attributes), and correlations between customer and products. Each cell in the matrix provides a visual indication of how attributes are correlated, along with the correlation value. Various colors indicate the strength of the correlation among attributes, with -1 and 1 indicating strong association.
Table 5-15 shows the strength of the correlation for the different ranges of values for the correlation coefficient.
Table 5-15 Correlation Coefficient Values
| Correlation Coefficient Value | Strength of Relationship |
|---|---|
| 1.0 to 0.5 | Strong |
| 0.3 to 0.5 | Moderate |
| 0.1 to 0.3 | Weak |
| -0.1 to 0.1 | None or very weak |
| -0.3 to -0.1 | Weak |
| -0.5 to -0.3 | Moderate |
| -1.0 to -0.5 | Strong |
Contextual Area
This area provides graphical illustrations of the detailed data distribution about the customers and their attribute importance.
Analyze Customers
In Explore Data, the BI displays the data distribution of the customers by each participating attribute as well as other configured informational attributes. Customer Segmentation identifies the bins based on the underlying sample data and displays the histograms. It provides the percentage of customers that are present in a selected location. For example, a company may have ten percent of premium customers who are high spenders and who shop frequently.
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Table 5-17 Attributes
| Field Name | Description |
|---|---|
| Participating | A check in this column indicates that the attribute participates in the segment criteria. |
| Attribute Group | A logical grouping of attributes such as demographics or purchase behavior. |
| Attributes | Attributes that are potential candidates for generating segments. |
| Importance | System-generated attribute importance index that indicates the significance of each attribute. |
The Attributes toolbar includes the following functionality:
Figure 5-18 Attribute Toolbar
Table 5-18 Attribute Toolbar
| Function | Description |
|---|---|
| Action menu | Resets the attribute selection to the default selection that system identified using attribute importance thresholds. |
| Include or exclude attributes | Any attribute beyond a certain threshold is not included in the segmentation process. |
Contextual Area
The contextual business intelligence lists a set of attributes that the current scenario includes as the participating attributes for the segmentation process.
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Table 5-25 Scenario Compare
| Field Name | Description |
|---|---|
| Max. # of Segments | The value used for the maximum in the scenario execution, if this option used. |
| Min. # of Segments | The value used for the minimum in the scenario execution, if this option used. |
| Optimal # of Segments | The value used for the optimal number of segments in the scenario execution, if this option used. |
| Rank | The value for the rank. |
| Is System Preferred | Indicates whether the scenario is the one the application prefers. |
| Is User Preferred | Indicates whether the scenario is the one the user prefers. |
| Smallest Segment Size | The size of the smallest segment. |
| Largest Segment Size | The size of the largest segment. |
| Has Outlier | Indicates a segment with the number of customers below a threshold. For example, the number of customers is below a certain percentage of the number of customers in a segment. |
| Attributes | A list of relevant attributes. |
Scenario System Recommendations
The application provides the following recommendations at the scenario (segment set), segment, and customer levels.
Scenario Optimality
This graph indicates how the system identifies the best number of segments for a given data set. It starts with a small number of segment centers and searches for the number beyond which there is minimal dispersion. At this point, increasing the number of segment centers any more only reduces dispersion by a small amount, and the marginal improvement is small.
In this guide
- Guide: AI Foundation User Guide
- Previous: 4 Advanced Clustering
- Next: 6 Attribute Extraction