Event-driven data

As explained in First-Party Data Activation

Detailed records of individual user or customer interactions with marketing channels and digital properties.

A customer profile can show who someone is; event-driven data adds what they did and when. These timestamped interaction records include engagement across marketing systems, websites, apps, and products. section 4.2.2

Unlike customer attributes or purchase records, activity records capture interactions such as clicks and visits. Combined with profile and transaction data, they can support targeting and personalization. section 4.2.2

Following a customer journey

Recording an email click, a paid-media impression, an SMS interaction, and a website visit creates touchpoints for journey analysis. Linking campaign and transaction data can connect those interactions to revenue. section 7.7.5

Figure 4.3 Customer data model: attributes, transactions, activities

The diagram distinguishes activity datasets from customer attributes and transactional datasets in a flexible customer data model. section 4.2.2

How First-Party Data Activation covers it

How deep it goes

Defines activity records and connects touchpoints to attribution, but does not detail event schemas or implementation.

Examples

Includes email clicks, paid-media impressions, SMS interactions, website visits, product sessions, and other timestamped activity.

What it assumes

Assumes consistent tracking IDs and a customer identifier for linking touchpoints, audiences, campaigns, and transactions.

What you can do after

Evaluate campaign and audience performance by connecting customer touchpoints and transactions to campaigns.

How it connects

Links customer profiles and transactions with campaign measurement, multi-touch attribution, and customer journey analysis.

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Related concepts

• Kafka Streamsused by · Kafka Streams is a client library for building applications that continuously process data from and write data to Apache Kafka topics.
• processor topologyused by · A processor topology is a directed graph of source, processor, and sink nodes that defines how records flow through a stream-processing application.
• event-driven architectureproduced by · Event-driven architecture is a software architectural style in which components communicate or initiate processing by producing and responding to events.

This explainer was generated by Manning's knowledge graph from the text of First-Party Data Activation. The section links point to the parts of the book it draws on.