mParticle

Problem: Experiment results live in Convert, while downstream engagement and revenue data sit in tools connected through mParticle, making it hard to see the full impact of each variation on the customer journey. Solution: Convert sends experiment and variation IDs into mParticle as custom events for every bucketed user. mParticle then stitches this with behavioral, demographic, and channel data from all connected tools. Outcome: Teams get a single, unified view of how each variation influences activation, retention, and LTV across web, mobile, email, and ads—enabling better test prioritization and more confident rollout decisions.

Problem: Marketers can’t easily target users who saw a specific A/B test variation with tailored follow-ups, limiting their ability to reinforce winning experiences or recover users exposed to underperforming variants. Solution: Convert logs experiment and variation exposure as custom events in mParticle, which can be used to create dynamic audiences based on test participation and variant assignment. Outcome: Growth teams can instantly sync these audiences to email, push, and ad platforms to nurture high-performing cohorts, run win-back campaigns, or exclude test groups from conflicting campaigns.

Problem: Experiments may improve on-site metrics, but tying those lifts to downstream conversions, subscriptions, or in-app purchases across systems is difficult and often manual. Solution: With Convert experiment data flowing into mParticle, every purchase, subscription, or upgrade event in connected tools can be analyzed by experiment and variation exposure. Outcome: Analytics teams can quantify true business impact—revenue, ARPU, and LTV—by variant, improving experiment ROI calculations, informing rollout decisions, and sharpening future test hypotheses.

Problem: Personalization engines often ignore past experiment exposure, missing a rich signal about user preferences and behaviors revealed through A/B testing. Solution: Convert’s experiment and variation events are added to user profiles in mParticle and combined with behavioral and demographic attributes for richer segmentation. Outcome: Product and marketing teams can trigger personalized content, offers, and UX based on how users responded to previous tests, driving more relevant experiences and higher conversion rates at scale.

Problem: Data teams lack visibility into whether experiment tracking is correctly implemented, risking broken reports, misattributed results, and delayed launches. Solution: The integration surfaces Convert experiment and variation events directly in mParticle’s Live Stream, allowing real-time inspection of payloads, IDs, and timing. Outcome: Teams quickly confirm that events are firing as expected, catch implementation issues early, and reduce the risk of running tests on bad data or needing costly re-runs.

Problem: Running experiments in isolation from lifecycle and acquisition campaigns can cause message clashes, skewed results, and inconsistent user experiences across channels. Solution: By streaming Convert experiment data into mParticle, marketers can build segments that reflect current test exposure and sync them to all connected engagement tools. Outcome: Campaigns can be automatically aligned with active tests—pausing conflicting messages, tailoring creatives by variation, and ensuring cleaner experiment reads and more coherent customer experiences.