In the world of data-driven marketing, precision and efficiency are essential. For business users, creating and managing the customer segments that power their campaigns is a foundational task, however it’s often more complex than it needs to be, with manual analysis, and time-consuming troubleshooting.
Addressing the gaps in segmentation
The task of creating a segment for a campaign looks easy, but can be challenging. First, building the segment itself sometimes requires understanding the underlying data model, like choosing which Data Model Objects (DMOs) hold the proper attributes . Then once a segment is built, new questions arise: does this segment audience exist already in some other segment? Why did a specific member not qualify? Why did the segment fail to run? Did the segment contain the members I am looking for?
Previously, answering these questions required navigating the data model, creating manual SQL queries, comparisons in Data Explorer or Query builder. This process was slow, inefficient, and created a barrier between the business user and the data-driven insights they needed. This uncertainty often resulted in lack of efficiency, redundant marketing, and a lack of confidence in the segment results being correct.
The solution: a suite of AI-powered segment agent actions
To address these challenges, Data 360(fka Data Cloud) is introducing a powerful suite of new AI-driven agent actions. These agent actions transform the segmentation experience, moving from a more rigid, UI click-based process to an intuitive, conversational workflow. This new approach abstracts away technical complexity and empowers users to build, analyze, and manage segments in an easier manner.
A new experience with agent actions
1. Segment rules creation agent action
With the new Data 36O agent, a marketing manager can create segment rules with the new Segment rules creation agent action using natural language; upon being on the segment builder and opening Agentforce, they simply ask to create a rule with a set of desired attributes and behaviors. This AI-driven agent, which will replace the legacy Einstein segmentation, handles the technical selection of DMOs and attributes, boosting productivity.
2. Segment overlap agent action
After creating their new segment, the manager wonders if they are targeting the same people as an existing segment part of the same campaign. Instead of trying to compare with other created assets, they turn to the Segment overlap agent action. They can ask to compare multiple segments, and the agent will immediately return the percentage of common profiles, showing how many profiles are shared. This insight allows them to either eliminate a redundant segment or create a new, highly specific upsell opportunity from the overlap.
3. Segment member verification
Now, the manager wants to validate the results. Using the Segment member verification agent action, they can ask a direct question like, “Is profile ID 123 in this segment?” . This simple check builds confidence before the segment is ever activated. But they notice one of their test profiles is missing. The Segment member absence check agent action can answer why, explaining exactly which rule excluded that individual, preempting a potential support case and building trust in the segment’s logic .
4. Segment inspector agent
Later, a different, more complex segment times out. In the past, this would be a black box. Now, the Segment inspector agent action provides a self-service solution. It surfaces actionable information, explaining why the segment failed, such as data skew, and provides meaningful recommendations to fix it .
5. Segment Schedule Recommendation Agent Action
Finally, to ensure their segments run smoothly, with the segment schedule recommendation agent action, the Marketer can choose one of the optimal times that meets the campaign goals from the suggestions given by the agent action.
Key benefits of an agent-driven approach
This interconnected suite of agents delivers a shift in efficiency and usability.Switching to a conversational interface enhances ease of use for all business users. Instead of navigating a complex UI, they can now get the answers they need simply by asking in a natural, conversational way. This self-service model for troubleshooting and analysis, from checking member absence to inspecting failures, builds confidence and trust in the data . The result is a more productive, efficient, and reliable segmentation process that empowers users to make smarter, data-driven decisions.
In summary
This new set of agent actions is part of the D36O Agent Pilot and requires the Agentforce SKU. These segment agent actions represent a significant step forward in making Data 36O more intelligent, accessible, and powerful. By embedding conversational AI directly into the segmentation workflow, we are empowering business users with greater control and confidence, transforming a complex technical task into a seamless, strategic, and efficient process.
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