Mapp

Problem: Experiment results live in Convert, while behavioral and revenue metrics live in Mapp. Teams manually stitch reports together, slowing decisions and creating conflicting numbers across tools. Solution: Convert sends experiment and variation identifiers into Mapp via the custom data layer and plugin. All test exposure data becomes available inside the same Mapp dashboards used for traffic, engagement, and revenue. Outcome: Stakeholders see a single source of truth for test performance. Reporting time drops, discrepancies are reduced, and decisions about winners and rollouts are made faster and with more confidence.

Problem: Winning variations are chosen on a single primary KPI in Convert, but teams lack visibility into secondary effects like content consumption, micro-conversions, or churn risk tracked in Mapp. Solution: By syncing variation data into Mapp custom parameters, analysts can segment any Mapp report by experiment and variation, exploring long-term behavior, multi-step funnels, and engagement patterns per variant. Outcome: Teams uncover hidden trade-offs (e.g., higher signups but lower retention) and refine rollouts or follow-up tests. Optimization moves from single-metric wins to holistic customer and revenue impact.

Problem: Marketers want to target users differently based on which experiment variation they saw, but test exposure isn’t available as a segmentation dimension in Mapp. Solution: Convert passes experiment and variation names into Mapp, where they can be used as filters in segments. Marketers can define audiences like “saw social-proof variant” or “exposed to new pricing layout.” Outcome: Campaigns, on-site personalization, and remarketing can be tailored to prior test experiences. This increases relevance, improves engagement, and lets teams operationalize learnings from experiments in real time.

Problem: CRO teams struggle to prove the true revenue impact of UX changes because attribution models and revenue data live in Mapp, disconnected from experiment results in Convert. Solution: With dual tracking and the Mapp plugin, experiment and variation data flow into Mapp’s attribution and revenue reports. Analysts correlate each variation with assisted conversions, LTV proxies, and channel performance. Outcome: Experiments can be evaluated on full-funnel and multi-touch impact, not just last-click or session-level conversions. This strengthens the business case for UX investments and prioritizes tests with real ROI.

Problem: As the number of experiments grows, inconsistent naming and tracking make it hard to compare tests or reuse insights across brands, sites, or regions. Solution: Teams use the custom data layer and Mapp containers to enforce a structured schema for experiment and variation names. All tests are pushed into Mapp with consistent parameters and taxonomy. Outcome: Cross-test and cross-site analysis becomes reliable and scalable. Teams can benchmark patterns (e.g., which messaging themes win most often) and build a durable experimentation knowledge base in Mapp.

Problem: During live tests, teams jump between Convert and Mapp to monitor performance and health, increasing the risk of missing anomalies or misinterpreting early trends. Solution: Once the integration is configured, live experiment and variation data appear directly in Mapp’s real-time and standard reports. Analysts can overlay test exposure on existing KPIs and alerts. Outcome: Issues like tracking breaks, traffic imbalances, or unexpected behavior shifts are spotted quickly. Teams manage tests from the analytics environment they already trust, reducing risk and improving test governance.