Telecommunications industry data model ERD

The following entity relationship diagram (ERD) represents a standardized data model for the telecom industry. The ERD is intentionally presented in a de-normalized fashion and with consideration for how data is stored in Adobe Experience Platform.

NOTE

The ERD as described is a recommendation for how you should model your data for this industry use case. To make use of this data model in Platform, you must construct the recommended schemas and their relationships yourself. See the guides on managing schemas and relationships in the UI for more information.

Use the following legend to interpret this ERD:

  • Each entity shown in is based on an underlying Experience Data Model (XDM) class.
  • For a given entity, each row marked in bold represents a field group or a data type, with the relevant fields it provides listed below in unbolded text.
  • The most important fields for a given entity are highlighted in red.
  • All the properties that could be used to identify individual customers are marked as “identity”, with one of these properties marked as a “primary identity”.
  • Entity relationships are marked as non-dependent, since cookie-based events often cannot determine the person or individual who did the transaction.

NOTE

The Experience Event entity includes an “_ID” field, which represents the unique identifier (_id) attribute provided by the XDM ExperienceEvent class. See the reference document on XDM ExperienceEvent for more details on what is expected for this value.

Telecommunications use cases

The following table outlines the recommended classes and schema field groups for several common use cases for the telecom industry.

Use case Recommended classes and field groups
Understand customers who are good candidates for upsell or cross-sell opportunities based on their current holdings and their browsing behavior.
Retarget cart abandoners through relevant ads and automated personalized emails. Suppress ads when they convert.
When a customer is marked as likely to churn (based on an employee interaction or an automated machine-learning algorithm), send the customer details to digital and non-digital channels.

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