Devoteam (Formerly Ysance)

Problem: Experiment results live in Convert, while rich customer attributes sit in Ysance. Teams struggle to connect test performance with lifecycle metrics, LTV, or offline data without manual exports and error-prone joins. Solution: The integration streams experiment and variation assignments from Convert into Ysance via semantic track events. All test exposure data becomes a native part of your customer profiles and warehouse tables. Outcome: Marketers and analysts can evaluate experiments against full-funnel KPIs, LTV, churn, and offline conversions directly in Ysance. This creates a single source of truth for experimentation and faster, more confident decisions.

Problem: Retention and CRM teams can’t easily target users who saw a specific test variation. They resort to static lists or guesswork, limiting post-test nurturing and follow-up campaigns. Solution: Convert sends experiment and variation names into Ysance, enriching each profile with exposure flags. Teams can build dynamic segments of users who saw winning or losing variants across experiments. Outcome: Lifecycle and CRM campaigns become more relevant—e.g., upsell only users who saw a high-intent variant, or re-engage those exposed to underperforming experiences. This drives higher engagement and conversion from the same traffic.

Problem: Most A/B test decisions are based on short-term metrics like clicks or immediate conversion. It’s hard to see how a variation impacts long-term revenue, repeat purchases, or subscription retention. Solution: With Convert data centralized in Ysance, analysts can join variation assignments with downstream revenue, subscription, and product usage tables. They can run cohort and LTV analyses by experiment and variant. Outcome: Teams choose winners based on true business impact, not just surface-level uplifts. This reduces the risk of shipping variants that hurt long-term value and helps prioritize experiences that drive sustainable growth.

Problem: Reporting on experiments across web, mobile, and offline touchpoints requires manual stitching of IDs and exports from multiple tools. This slows down analysis and introduces inconsistencies. Solution: The dual tracking setup ensures Convert captures experiment data and forwards it into Ysance, where it’s joined with cross-device and cross-channel identifiers already in the CDP. Outcome: Teams can build unified dashboards in Ysance that show experiment impact across channels and devices without extra data engineering. Reporting cycles shorten, and stakeholders get consistent, trustworthy views of performance.

Problem: Personalization rules are often based on static attributes or broad segments, ignoring how users actually responded to past experiments and experiences. Solution: Experiment and variation assignments from Convert are stored in Ysance as part of each customer’s profile. These signals can be used to define traits like “responds well to urgency messaging” or “prefers discount-led offers.” Outcome: Personalization engines and campaign tools connected to Ysance can trigger more nuanced experiences based on proven behavioral responses. This increases relevance and boosts conversion without guesswork.

Problem: Data teams spend hours exporting CSVs from Convert, cleaning them, and loading them into Ysance or BI tools just to run basic experiment analysis. Solution: By activating the Ysance integration in Convert, experiment and variation data flows automatically into the warehouse via semantic events, eliminating ad-hoc pipelines and manual uploads. Outcome: Analysts reclaim time for deeper insights instead of plumbing. Experiment analysis becomes repeatable and scalable, enabling more tests to be run and evaluated with the same or fewer resources.