Parse.ly

Problem: Content teams run A/B tests on headlines, layouts, or intros, but only see surface-level metrics like CTR or conversions, not how variations change actual reading and engagement behavior. Solution: Convert sends experiment and variation names into Parse.ly as `_ConvertExperiment` events. Teams use the raw data pipeline to correlate each variation with scroll depth, time on page, and engaged views. Outcome: Marketers identify which variation drives deeper content consumption, not just clicks. They prioritize winning patterns for future content, improving overall engagement quality and content ROI.

Problem: Publishers redesign article templates or move in-article modules, but can’t clearly see how each layout impacts scroll depth and where readers drop off across variations. Solution: With Convert, each layout test variation is tagged and passed into Parse.ly as a custom event. Using the raw data pipeline, analysts segment scroll depth and engagement by experiment and variation. Outcome: Teams pinpoint layouts that consistently drive readers further down the page. They standardize on high-performing layouts, increasing exposure to key content blocks, CTAs, and monetization units.

Problem: Growth teams test different paywall messages, teaser copy, or signup prompts, but struggle to connect those experiments to downstream subscriber conversions tied to specific content. Solution: Convert logs which experiment and variation each visitor saw, and pushes this into Parse.ly. Analysts join `_ConvertExperiment` events with subscription and conversion events in the raw data pipeline. Outcome: Teams see which content experiences and messages best convert readers into subscribers. They roll out winning combinations and design new tests grounded in proven subscriber behavior patterns.

Problem: Content marketers promote articles via newsletters, social, and onsite modules, but can’t easily see how A/B-tested elements (titles, images, intros) change engagement by traffic source. Solution: Convert’s experiment and variation data is captured in Parse.ly and combined with referral and campaign data. Teams build custom reports to compare engagement by variation and acquisition channel. Outcome: Marketers discover which creative and content treatments work best per channel. They tailor promotion strategies by source, improving engagement rates and lowering acquisition costs for key content.

Problem: CRO results live in one tool and content analytics in another, forcing teams to manually reconcile reports and making it hard to align experimentation with editorial strategy. Solution: The integration sends all Convert experiment exposures into Parse.ly as structured `_ConvertExperiment` events. Both CRO and editorial teams analyze tests alongside standard content metrics in one raw data pipeline. Outcome: Stakeholders share a single, experiment-aware view of content performance. Decisions about new tests, content formats, and templates are grounded in unified data, reducing silos and reporting friction.

Problem: Over time, teams run many A/B tests on content but lack a systematic way to mine historical experiments for patterns that predict high engagement or conversion. Solution: Convert continuously tags visitors with experiment and variation data, which is stored in Parse.ly’s raw data. Analysts query across past `_ConvertExperiment` events to find recurring winning attributes. Outcome: Organizations uncover durable patterns—like headline structures, content lengths, or module placements—that repeatedly win. They codify these into playbooks, accelerating future optimization and reducing guesswork.