Segment Customer Data in Huge Files with Selection and Aggregation Logic.

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Segment Customer Data

Rapidly Digest Big Data for Big Decisions

Challenges:

Understanding customers by demographic and purchase groups can help you:

• give customers more of what they need
• target your advertising
• improve product delivery
• make investment decisions

However, to really improve growth opportunities and make the more specific decisions about product offerings and features, companies need insight into customer needs.

For very large sets of data, customer segmentation analysis can be a slow and very difficult process. It may also expose sensitive data that requires specific, integrated protection.

Legacy approaches may not provide the audit trails from these analyses that are needed for internal and external review.

Solutions:

Going against massive extract and legacy files, the CoSort product's SortCL program gives you the ability to segment, sort, report or join results into distinct subsets, views, or filtered groups. In the same job script and pass through the data, you can create as many outputs as necessary, each containing a different section of data in custom report formats.

You can apply different selection criteria for segmentation, including:

• name, age, address or date ranges
• account or other identifying numerics
• transaction or product (SKU) IDs
• web page visits or IP addresses
• new, changed, deleted, or duplicate data

You can use SortCL's built-in, field-level encryption (or other privacy protection functions) to prevent the exposure of sensitive data on a need-to-know basis.

Each time SortCL runs, it can produce a complete XML audit log. This lets you examine the user, job and runtime parameters to provide detective control and verify compliance with privacy regulations.

IRI, The CoSort Company