Triple Whale Analytics

Problem: Ecommerce teams run many Convert experiments but can’t clearly see which variations drive actual revenue and ROAS inside their core Triple Whale dashboards. Solution: The integration sends a “Convert Experiment Viewed” event with experiment and variation names into Triple Whale for every visitor. Marketers can then filter revenue and attribution reports by variation exposure. Outcome: Teams see which experiences lift revenue, AOV, and MER, not just clicks. This enables confident scaling of winning variants, faster test iteration, and better alignment between CRO and paid growth decisions.

Problem: Media buyers can’t tell if landing page tests are helping or hurting performance across channels, making it hard to decide which experiences to send paid traffic to. Solution: Convert tags each visitor with their experiment and variation, and Triple Whale records this as a custom event. Buyers segment performance by “Convert Experiment Viewed” to compare ROAS and CPA by variation. Outcome: Paid budgets are steered toward high-ROAS experiences, while underperforming variants are quickly paused. This reduces wasted ad spend and aligns landing page optimization with acquisition efficiency.

Problem: Personalized experiences built in Convert may improve short-term conversion, but brands struggle to see their effect on long-term metrics like LTV and repeat order rate. Solution: Each time a user sees a personalized experience, Convert fires a “Convert Experiment Viewed” event into Triple Whale with the specific variation. Marketers then analyze LTV, reorder rate, and time-to-second-purchase by variation. Outcome: Teams identify which personalization strategies create more valuable customers over time, not just more first orders. They double down on experiences that grow LTV and refine those that only drive shallow gains.

Problem: CRO results live in Convert while revenue and attribution live in Triple Whale, forcing teams to manually stitch spreadsheets to understand full-funnel impact. Solution: The integration automatically pushes experiment and variation exposure into Triple Whale as custom events whenever both pixels load. No extra tagging or exports are needed. Outcome: Marketers get a unified analytics view where experiment performance, channel attribution, and revenue sit together. This saves analysis time, reduces reporting errors, and speeds up decision-making across teams.

Problem: Product and CRO teams prioritize tests based on surface metrics like CTR or add-to-cart, without visibility into downstream effects like refund rate, discount dependency, or blended MER. Solution: Convert sends experiment exposure into Triple Whale, where teams correlate each variation with deeper metrics already tracked—refunds, discounts used, margin, and blended performance. Outcome: Roadmaps are driven by experiments that improve profitable growth, not vanity lifts. Teams invest in experiences that increase healthy revenue and de-prioritize those that spike conversions but hurt margin or quality.

Problem: Sometimes a variant that wins in Convert underperforms once rolled out, and teams lack granular data to understand what changed across audiences and channels. Solution: By keeping experiment and variation exposure visible in Triple Whale, teams compare pre- and post-rollout performance by cohort, channel, and device using the “Convert Experiment Viewed” event. Outcome: Marketers quickly spot where the new experience under-delivers (e.g., specific channels or devices) and adjust targeting or creative. This reduces the risk of rolling out false positives and preserves revenue gains.