# ReSci Retention Science integrations > Connect ReSci's AI retention engine to your CRM, ecommerce, and data stack to run personalized lifecycle campaigns at scale. **Canonical page:** https://tray.ai/connectors/resci-retention-science-integrations/ **Categories:** Marketing, Databases **Documentation:** https://tray.ai/documentation/connectors/service/resci-retention-science ## Overview ReSci Retention Science uses machine learning to predict customer behavior — churn risk, purchase likelihood, lifetime value — and sends personalized email campaigns at the right moment. Those predictions are more useful when they can drive actions across every channel, not just email. With tray.ai, you can sync customer data in real time, feed ReSci's models with richer signals, and route retention insights back into your CRM, analytics, and support tools. ## Use cases ### Real-Time Customer Data Sync to Power AI Predictions ReSci's predictive models are only as accurate as the data behind them. By connecting your ecommerce platform, POS system, and CRM to ReSci via tray.ai, you can continuously stream purchase events, browsing behavior, and customer attributes so predictions stay fresh and actionable. No more manual CSV exports, and your segmentation always reflects what customers are actually doing right now. - Stream real-time purchase and behavioral events into ReSci automatically, so stale data stops undermining your predictions - Enrich ReSci profiles with CRM attributes like customer tier, support history, and LTV scores - Manage data pipelines through a no-code integration layer instead of pulling in engineering every time ### Churn Prediction to Proactive Win-Back Campaigns When ReSci flags a customer as high churn risk, the response shouldn't stop at email. tray.ai can take that signal and fan out across your tools — creating a task in your CRM for a sales rep, updating a customer segment in your ad platform, or pinging a customer success manager in Slack. The whole thing runs automatically the moment ReSci updates a prediction. - Trigger multi-channel win-back workflows the moment churn risk crosses a defined threshold - Automatically assign high-value at-risk accounts to customer success reps in Salesforce or HubSpot - Stop manually watching retention dashboards — route alerts to the right team in real time instead ### Post-Purchase Lifecycle Automation Connecting your order management system to ReSci through tray.ai means every completed purchase, return, or subscription renewal immediately updates the customer's lifecycle stage. Replenishment reminders, upsell sequences, and loyalty triggers are timed to actual purchase cadence rather than static schedules that were probably wrong anyway. - Sync order events from Shopify, Magento, or custom order systems to ReSci without custom code - Trigger replenishment email sequences timed to each customer's individual purchase cycle - Update loyalty and VIP tier status in ReSci as customers hit spend thresholds in real time ### Segment Export to Paid Media and Ad Platforms ReSci's high-intent or high-LTV segments have value beyond email. With tray.ai, you can push those audiences directly into Google Ads, Facebook Custom Audiences, or programmatic platforms for lookalike targeting and suppression — so your paid media budget goes toward acquiring customers who actually look like your best retained ones. - Automatically push ReSci's predicted high-LTV segments to Facebook and Google for lookalike targeting - Suppress recently converted or active customers from paid acquisition campaigns to avoid wasted spend - Refresh audience lists on a daily or event-driven schedule without manual data exports ### Retention Analytics Synced to Your Data Warehouse Piping ReSci campaign performance data — open rates, predicted revenue attributed, conversion events — into your data warehouse or BI tool gives analysts a complete picture of retention ROI alongside other marketing channels. tray.ai can schedule or event-trigger these exports to Snowflake, BigQuery, or Redshift so reporting dashboards stay current. - Centralize ReSci performance metrics alongside paid, organic, and other email channel data in one warehouse - Schedule automated nightly syncs of campaign results into Snowflake or BigQuery for attribution modeling - Feed retention revenue data into Looker or Tableau dashboards without manual reporting ### Support Ticket Signals to Enrich ReSci Profiles A customer who recently filed multiple support tickets or received a refund is at elevated churn risk — but that context usually stays siloed in Zendesk or Intercom. With tray.ai, you can push support event signals into ReSci as custom attributes, so its models can factor in service experience when calculating retention scores. - Enrich ReSci customer profiles with support ticket counts, resolution status, and CSAT scores automatically - Let ReSci models factor in poor service experiences when ranking churn risk - Suppress promotional emails to customers with open escalated tickets before you make things worse ### New Customer Onboarding Sequence Enrollment When a new customer is created in your ecommerce or subscription platform, tray.ai can immediately enroll them in the right ReSci onboarding campaign based on acquisition source, product purchased, or customer segment. That removes the lag between signup and first communication, and makes sure every new customer enters a predictive sequence that actually fits them. - Trigger ReSci campaign assignment within seconds of account creation instead of waiting on batch jobs - Route new customers into segment-specific onboarding tracks based on acquisition channel or SKU purchased - Reduce first-purchase abandonment by making sure welcome and activation emails fire at the predicted optimal moment ## Templates ### Shopify Order → ReSci Customer Profile Sync Automatically syncs every new and updated Shopify order into ReSci as a purchase event, keeping customer purchase history and lifecycle stage current for accurate AI-driven campaign timing. Connectors used: Shopify, ReSci Retention Science ### ReSci Churn Risk Alert → Salesforce Task + Slack Notification Monitors ReSci for customers whose churn probability exceeds a defined threshold and automatically creates a follow-up task in Salesforce and posts an alert in a Slack retention channel so customer success teams can act immediately. Connectors used: ReSci Retention Science, Salesforce, Slack ### ReSci High-LTV Segment → Facebook Custom Audience Sync Exports ReSci's predicted high-LTV customer segment daily and upserts those email addresses into a Facebook Custom Audience for lookalike targeting, so ad spend goes toward customers most likely to generate long-term value. Connectors used: ReSci Retention Science, Facebook ### Zendesk Ticket Events → ReSci Custom Attribute Update Pushes customer support signals from Zendesk into ReSci as custom profile attributes whenever a ticket is created, escalated, or resolved, so ReSci's retention models can incorporate service experience into churn prediction. Connectors used: Zendesk, ReSci Retention Science ### ReSci Campaign Performance → BigQuery Nightly Export Schedules a nightly extraction of ReSci campaign performance metrics — sends, opens, clicks, conversions, and attributed revenue — and loads them into BigQuery for centralized attribution analysis alongside other marketing channel data. Connectors used: ReSci Retention Science, Google BigQuery ### New Subscription → ReSci Onboarding Campaign Enrollment Automatically enrolls new subscribers from Stripe or Recurly into the right ReSci onboarding campaign within seconds of signup, based on subscription plan or product line, to minimize time-to-first-value communication. Connectors used: Stripe, ReSci Retention Science ## Challenges Tray.ai solves ### Fragmented Customer Data Leaving ReSci Models Underinformed ReSci's predictive accuracy depends on a complete, timely view of customer behavior — but purchase history, support interactions, loyalty data, and browsing signals typically live in separate systems. Teams end up doing manual CSV uploads or relying on nightly batch jobs that leave predictions stale for hours or days. **How Tray.ai helps:** tray.ai connects all relevant data sources — Shopify, Salesforce, Zendesk, loyalty platforms — to ReSci with real-time event streaming and scheduled syncs, so prediction models always have the freshest possible customer signals without manual data wrangling. ### Retention Insights Staying Siloed in ReSci ReSci generates useful predictions — churn scores, purchase likelihood, LTV estimates — but those insights rarely flow back into the rest of the business. Sales reps don't see churn risk in Salesforce, paid teams don't suppress already-engaged customers in Google Ads, and executives don't see retention revenue in their dashboards. **How Tray.ai helps:** tray.ai reads ReSci prediction and campaign data via API and routes it to every downstream system that needs it — CRMs, ad platforms, data warehouses, BI tools — so retention intelligence actually influences decisions across the whole organization. ### Engineering Bottlenecks Building and Maintaining ReSci Integrations Custom-coded integrations between ReSci and surrounding systems take significant engineering effort to build, and even more to maintain as API versions change or business requirements shift. Retention marketers end up waiting on dev resources just to get the data connections they need. **How Tray.ai helps:** tray.ai's visual workflow builder and pre-built ReSci connector let marketing operations and retention teams build and modify integrations themselves, cutting dependency on engineering and reducing integration build time from weeks to hours. ### Lack of Multi-Channel Coordination When Churn Risk Is Detected When ReSci identifies an at-risk customer, the response is often limited to whatever is configured within ReSci's own campaign engine. Coordinating a response across email, paid retargeting, CRM task creation, and CS team alerts at the same time requires manual effort or custom scripting that falls apart at scale. **How Tray.ai helps:** tray.ai can trigger complex, multi-step workflows from a single ReSci churn signal — simultaneously updating a CRM record, pushing the customer to an ad suppression list, sending a Slack alert, and logging to a data warehouse — with full conditional logic and error handling built in. ### Difficulty Validating Retention ROI Across Channels Proving the revenue impact of ReSci-driven retention campaigns is hard when performance data is locked in ReSci's dashboard and can't be joined with paid media spend, email platform data, or revenue reporting in a unified BI layer. Attribution models stay incomplete and budget decisions rely on gut feel. **How Tray.ai helps:** tray.ai automates the export of ReSci campaign attribution data into data warehouses like Snowflake or BigQuery on a scheduled basis, so analysts can build cross-channel attribution models and give retention programs the revenue credit they've actually earned. ## Agent features ### Look Up Customer Profile (Data Source) Retrieve detailed customer profiles from Retention Science, including purchase history, engagement scores, and predicted behaviors. An agent can use this data to personalize outreach or inform decisions in other connected systems. ### Fetch Predictive Scores (Data Source) Pull AI-generated predictive scores — churn probability, lifetime value, purchase likelihood — for individual customers. An agent can use these scores to prioritize high-risk or high-value customers for targeted campaigns. ### Query Segment Membership (Data Source) Check which segments a customer belongs to in Retention Science, such as lapsed buyers, VIP customers, or win-back candidates. An agent can then tailor messaging based on where that customer actually sits in their lifecycle. ### Retrieve Campaign Performance Metrics (Data Source) Access performance data for email and retention campaigns, including open rates, click-through rates, and conversion metrics. An agent can analyze this data to recommend optimizations or trigger follow-up actions in connected marketing tools. ### List Active Campaigns (Data Source) Fetch a list of currently active retention campaigns and their configurations from Retention Science. Useful for avoiding duplicate outreach or making sure customers end up in the most relevant campaign. ### Add or Update Customer Record (Agent Tool) Create new customer profiles or update existing ones in Retention Science with the latest attributes, preferences, or contact details. This keeps the platform's predictive models fed with accurate, current data. ### Track Customer Event (Agent Tool) Send behavioral events — purchases, product views, support interactions — to Retention Science so they're factored into predictive scoring. An agent can trigger this after capturing activity in other connected systems to keep customer intelligence current. ### Enroll Customer in Campaign (Agent Tool) Add a customer to a specific retention or win-back campaign in Retention Science based on conditions the agent detects. This lets you build dynamic, rule-based enrollment triggered by real-time signals from other tools. ### Remove Customer from Campaign (Agent Tool) Unenroll a customer from an active campaign in Retention Science — for example, after they've converted or opted out. An agent can automate this to prevent over-messaging and avoid wearing out the relationship. ### Sync Audience Segment (Agent Tool) Push updated audience segment definitions or customer lists into Retention Science to keep segmentation in sync with data from CRMs, e-commerce platforms, or data warehouses. An agent can run this on a schedule or in response to specific business events. ### Trigger Retention Workflow (Agent Tool) Kick off a pre-configured retention workflow in Retention Science for one or more customers based on signals detected elsewhere — like a cancellation intent flagged in a support ticket. This makes cross-platform automation possible without rebuilding the lifecycle logic Retention Science already has. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/