Customer.io

Problem: Cart abandonment campaigns treat all visitors the same, ignoring which pricing, layout, or copy variant they saw. This leads to generic reminders that don’t address the friction each test variant is actually probing. Solution: Convert passes experiment and variation attributes (e.g., checkout_layout_A/B) into Customer.io profiles. Customer.io then segments abandoners by variation and triggers tailored flows that speak directly to the layout, offer, or messaging they experienced. Outcome: Abandonment emails feel context-aware, improving open and click rates. Teams can see which on-site variant plus which follow-up sequence drives the best recovery, and double down on the winning combination to lift overall revenue per visitor.

Problem: New users go through different signup experiments, but onboarding emails ignore this context. Everyone receives the same sequence, even if they responded to different value props or UI flows during signup. Solution: Using the Convert + Customer.io integration, experiment and variation names from signup tests are stored as attributes on each user. Customer.io uses these attributes to branch onboarding sequences, emphasizing the benefits, features, or help content that matches the variant they saw. Outcome: Onboarding becomes an extension of the winning signup experience instead of a reset. Users get more relevant guidance, activation rates increase, and teams can attribute uplift to specific experiment-variant plus onboarding-path combinations.

Problem: Homepage tests reveal which messaging or hero concepts resonate, but this insight often stays on-site. Email campaigns continue using outdated or generic messaging that doesn’t match what users just saw and responded to. Solution: Convert sends homepage experiment and variation data (e.g., hero_message_test: value_prop_X) into Customer.io. Marketers build segments for each variation and automatically send campaigns that mirror the tested messaging and creative users were exposed to. Outcome: Lifecycle messaging stays consistent with the best-performing on-site experiences. This continuity improves engagement and downstream conversions, while reporting in Customer.io shows which homepage concept plus email narrative drives the strongest funnel performance.

Problem: Post-purchase campaigns are optimized in isolation, without considering which pre-purchase experiments influenced the buyer. This hides how different on-site journeys affect responsiveness to upsell or cross-sell offers. Solution: The integration logs which Convert experiments and variations a buyer saw before purchasing. Customer.io uses these attributes to create cohorts (e.g., discount_vs_value_variant) and sends differentiated post-purchase upsell flows tailored to their pre-purchase experience. Outcome: Marketers uncover which combinations of pre-purchase variant and post-purchase messaging maximize repeat purchases and AOV. They can standardize on the best-performing pairings, turning experimentation insights into sustained revenue gains.

Problem: Some A/B tests look neutral or negative on immediate on-site KPIs, but their impact on email engagement and long-term conversions is unknown. Decisions are made without seeing the full lifecycle effect. Solution: Convert experiment and variation data flows into Customer.io as profile attributes. Teams analyze email open, click, and conversion metrics by experiment cohort, and trigger follow-up campaigns only for users exposed to specific variants. Outcome: CRO and lifecycle teams gain a holistic view of test impact across channels. They can rescue variants that drive better long-term engagement, kill those that hurt lifecycle performance, and design follow-up campaigns that offset short-term tradeoffs.

Problem: Dormant users are re-engaged with one-size-fits-all winback campaigns that ignore what they previously saw or responded to on-site. This wastes the rich experimentation history already collected. Solution: With Convert feeding experiment and variation history into Customer.io, marketers segment inactive users by the tests they experienced—such as pricing, feature emphasis, or onboarding variants—and craft winback messages aligned to those past experiences. Outcome: Reactivation emails feel highly relevant to each user’s prior journey, increasing the chances of re-engagement. Teams can compare winback performance by experiment cohort and refine both future tests and lifecycle strategies based on real reactivation results.