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Timeline event rollups: CDP use cases

Timeline event rollups enable a range of CDP use cases that employ time-based segmentation.

These use case examples illustrate how you can use timeline rollups to extend or enrich your existing BlueConic CDP use cases.

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Roll up timeline event data - such as orders, or other timeline events - to profile properties and create new dynamic customer segments.

  • Suppression use case: Exclude customers from an email re-engagement campaign if they have ordered between April 1 and April 30 this year. 
  • Abandoned cart use case: Send email to customers who have abandoned a shopping cart between April 1 and April 7.
  • Order-based use case: Create a segment of customers who have placed more than 6 orders between January 1 and June 30 this year. Example use case: Aggregating order data with timeline event rollups
  • Fine-tune your campaigns: Exclude profiles from a campaign if they have ordered during the month of May.
  • Suppression use case: Send no more than 5 emails or push notifications per week across all ESPs.


Use timeline event rollups to create better reports on profile activities.

  • Gain insight into average order values.
  • Measure customer loyalty based on purchase, page views, email clicks, etc.
  • Augment your business intelligence reports with timely order and purchasing statistics for customer segments.
  • Find the top shipment date of orders for certain products in the month of March.

Exporting data

  • Show the total order value in an email to explain to the customer why they get a certain loyalty discount reward.
  • Create customer audiences based on recent orders.
  • Export rolled up data to external systems that rely on this information using BlueConic Connections.

AI and Machine Learning

  • Enable machine learning feature engineering by supporting the data aggregation needed to train ML models. 
  • Train ML models that make predictions based on customer spending, total order spending, total emails sent, total emails opened, etc.
  • Roll up data for a next-best channel model using:
    • Total number of emails opened
    • Total number of emails clicked
    • Total number of push messages sent
    • Total number of push messages opened
  • For a model that predicts propsensity to churn/subscribe, roll up data for:
    • Number of times a subscriber consumes premium content
    • Number of report downloads
    • Number of visits to account benefits page
    • Number of views of dialogues promoting account benefits page
    • Number of renewal page views
    • Number of visits
    • Number of free newsletter clicks

Learn more about AI Workbench for applying AI and machine learning models to your first-party data.


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