ECONDA now Dymatrix

Problem: The CRO team can see uplift in Convert, but finance and leadership want to know the exact revenue impact of each winning variation across products, devices, and traffic sources. Solution: Convert sends experiment and variation IDs into Econda, where existing e-commerce and revenue tracking is already in place. Teams analyze test performance in Econda with full order, product, and margin context. Outcome: Stakeholders see clear revenue and AOV impact per variation, making it easier to prioritize rollouts, justify CRO investment, and focus on experiments that move core financial KPIs—not just conversion rate.

Problem: Some tests show flat or modest uplifts overall, but the team suspects strong performance in specific customer segments that standard test reports don’t fully reveal. Solution: Convert’s experiment and variation data is synced to Econda, where analysts segment results by device, traffic source, customer type, category, and more using existing Econda dimensions. Outcome: Teams identify segment-specific winners (e.g., high-value returning users on mobile) and deploy targeted experiences instead of global rollouts, increasing ROI and reducing the risk of broad changes that hurt key segments.

Problem: Marketers waste time recreating the same goals and funnels in both Convert and Econda, leading to inconsistent definitions, tracking drift, and reporting discrepancies. Solution: Using the integration, Convert goals and experiment metadata are passed into Econda via emos3 events. Econda becomes the single source of truth for goals, funnels, and test performance. Outcome: Teams maintain one consistent goal setup, reduce implementation overhead, and trust that every experiment is evaluated against the same KPIs across both testing and analytics, speeding up test analysis and reporting.

Problem: CRO reports focus on immediate test goals (e.g., add-to-cart), but stakeholders need to understand downstream effects on checkout completion, refunds, and customer lifetime value proxies. Solution: Convert experiment and variation data flows into Econda, where analysts overlay it on existing funnel, cohort, and retention reports. Each funnel step is broken down by variation. Outcome: Teams see how each variation affects the entire journey—from landing to purchase and beyond—avoiding false positives and selecting winners that improve long-term value, not just micro-conversions.

Problem: Developers are a bottleneck for new tests because every analytics integration usually requires custom tagging, extra code, and QA cycles across tools. Solution: The integration reuses existing Convert and Econda tracking snippets (emos2.js or emos3.js). Activation happens in Convert’s interface or via a lightweight JavaScript snippet that sends IDs to Econda. Outcome: CRO teams launch and measure experiments faster without heavy engineering support. More tests reach production, learning velocity increases, and optimization becomes a continuous, low-friction process.

Problem: Merchandising and category managers want to know how experiments affect specific product lines, but test reports don’t easily connect variation performance to product- and category-level metrics. Solution: Convert variation data is synced into Econda, where it’s combined with detailed product, category, and basket information already tracked. Analysts build dashboards showing test impact per category and SKU. Outcome: Teams see which variations drive higher revenue and margin in key categories, enabling more precise merchandising decisions, targeted promotions, and category-specific optimization strategies based on real experiment data.