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MessageGears today announced new segmentation capabilities designed to give enterprise marketers greater autonomy and precision when targeting audiences from complex datasets.
Built for brands with large, dynamic datasets, the enhancements include dynamic data variables and nested data filters, two features that remove many engineering dependencies and enable marketing teams to build sophisticated segments without needing detailed schema knowledge.
What’s New
Dynamic data variables
Instead of static lists that quickly become outdated, dynamic data variables allow marketers to use real-time, continuously refreshed data fields such as inventory levels, subscription tiers or campaign codes directly in segmentation logic. This means segments are always current and relevant.
Nested data filters
Marketers can now apply complex filtering logic to structured data elements such as purchase history, multi-layer behavioural criteria or nested attributes without needing to flatten data tables or rely on engineering to build specific views. For example: “customers who booked flights over $1,000 in business class in the last 30 days but have not added a hotel stay” can be targeted directly.
Together, these enhancements are aimed at reducing manual effort, shortening time-to-campaign and empowering marketing teams to act faster and more precisely. As MessageGears noted, “We’re turning advanced targeting strategies into realistic, everyday tools for teams with limited time, resources, and budgets.”
Why This Matters for Enterprise Marketers
- Less engineering bottleneck: Many segmentation tools require heavy support from data or engineering teams to build or update logic. These new features lighten that burden.
- Timeliness and relevance: With dynamic data variables, segments reflect current conditions so marketing messages are aligned with what customers are doing now, not what they did weeks ago.
- Deeper targeting: Nested data filters allow for nuanced logic marketers can tap into sophisticated behaviours and attributes instead of relying on broad groups.
- Operational efficiency: Fewer manual list updates, less waiting, fewer errors marketing teams can move quicker and focus on strategy rather than maintenance.
- Cross-industry applicability: The features lend themselves to use cases across retail, travel, healthcare, telecom and more, enabling brands to tailor campaigns based on real-time behavior and complex patterns.
Considerations for Adoption
- Data quality and readiness: To fully leverage dynamic variables and nested filters, organisations should ensure their data warehouses, attribute schemas and event streams are well-managed.
- Training and change management: Marketing users may require training to understand new segmentation possibilities and avoid unintended exclusions or oversights in logic.
- Integration with activation workflows: Segments are only as useful as the campaigns they feed brands should ensure that activation systems (email, SMS, ad channels) can utilise the new segments effectively.
- Measurement and iteration: With more agile segmentation, brands should monitor performance closely and iterate logic based on outcomes (rather than relying on static assumptions).
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