Oracle BlueKai

Problem: Marketing runs homepage tests but can’t easily retarget visitors based on which version they saw, leaving money on the table with generic remarketing. Solution: Convert passes experiment and variation IDs into Oracle BlueKai as human-readable attributes. Marketers build BlueKai segments for visitors exposed to losing or neutral variations who didn’t convert. Outcome: Retargeting becomes tightly aligned with test exposure. Teams recover missed revenue from underperforming variations and prove incremental lift from variation-aware remarketing campaigns.

Problem: Winning onsite variations are identified in Convert, but activation is limited to the website. Paid media continues using broad, non-optimized audiences. Solution: Convert syncs experiment winners into BlueKai as audience attributes. BlueKai segments visitors who saw high-performing variations and pushes these audiences to ad platforms for lookalike and suppression strategies. Outcome: Media spend focuses on high-value profiles modeled from proven winners. Acquisition efficiency improves, and teams can clearly link onsite experiments to offsite campaign performance.

Problem: Experiment data sits in a silo, disconnected from the company’s established BlueKai taxonomy, making it hard for marketers to use test results in standard workflows. Solution: Convert experiment and variation names are mapped into BlueKai’s taxonomy as structured attributes. These become reusable building blocks in existing audience definitions and data models. Outcome: Experiment insights are instantly accessible in the same environment as CRM, behavioral, and third-party data. Marketers can enrich any audience with test exposure signals without extra data engineering.

Problem: Teams can’t see how exposure to specific A/B test variations impacts long-term behaviors like repeat purchases, churn, or cross-channel engagement. Solution: Convert sends real-time experiment and variation data into BlueKai, where it’s joined with downstream behavioral and campaign data. Analysts use BlueKai reporting to slice performance by test exposure. Outcome: Marketers understand which variations drive not just immediate conversions but higher LTV and engagement. This informs smarter rollout decisions and prioritizes experiments that impact strategic KPIs.

Problem: Data discrepancies between the experimentation tool and the DMP create mistrust in results and slow down activation of test learnings. Solution: Dual tracking with Convert and the BlueKai Core Tag, plus in-BlueKai reporting, lets teams monitor ingest of experiment attributes and validate that variation data is flowing correctly. Outcome: Confidence in data quality increases. Ops teams quickly spot and fix tagging or mapping issues, reducing delays and ensuring that audience strategies are built on accurate experiment signals.

Problem: Onsite personalization is limited to session-level behavior. Marketers can’t easily tailor experiences based on a visitor’s broader BlueKai profile and past test exposure. Solution: Convert reads BlueKai-powered segments that include prior experiment participation and audience traits, then uses this to trigger targeted variations and experiences in real time. Outcome: Visitors see journeys that reflect both their historical test exposure and rich audience context. Engagement and conversion rates rise as experiences become more consistent and relevant across channels.