Why models need behavioral context
An isolated page view is ambiguous. The events around it - previous searches, comparison depth, repeat visits, and next destinations - make the intent more legible.
Sequence-aware data lets models distinguish exploration, evaluation, and action. Graph structure also exposes relationships between entities that are difficult to capture in flat aggregate tables.
- Ordered event sequences
- Domain and category relationships
- Journey-stage labels
- Cohort-level temporal features
Use cases for behavioral features
The same source can be transformed differently depending on the target model. BGraph works backward from the prediction or retrieval task to define useful entities, edges, windows, and labels.
- Recommendation and next-action prediction
- Search ranking and query understanding
- Demand and trend forecasting
- Audience or account propensity modeling
Delivery shaped for model development
Teams can receive normalized events, derived features, graph edges, or aggregated cohorts. Schemas and refresh cadence are agreed before delivery so the dataset fits an existing training and evaluation pipeline.
Privacy and governance controls, together with explicit documentation, help data teams evaluate coverage, bias, and appropriate use before a model reaches production.