ReSci Retention Science connector
Smarter Customer Retention, Automated with ReSci Integrations
Connect ReSci's AI retention engine to your CRM, ecommerce, and data stack to run personalized lifecycle campaigns at scale.
What can you do with the ReSci Retention Science connector?
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.
Automate & integrate ReSci Retention Science
Automating ReSci Retention Science business process or integrating ReSci Retention Science data is made easy with tray.ai
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Build ReSci Retention Science Agents
Give agents secure and governed access to ReSci Retention Science through Agent Builder and Agent Gateway for MCP.
Data Source
Look Up Customer Profile
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.
Data Source
Fetch Predictive Scores
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.
Data Source
Query Segment Membership
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.
Data Source
Retrieve Campaign Performance Metrics
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.
Data Source
List Active Campaigns
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.
Agent Tool
Add or Update Customer Record
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.
Agent Tool
Track Customer Event
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.
Agent Tool
Enroll Customer in Campaign
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.
Agent Tool
Remove Customer from Campaign
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.
Agent Tool
Sync Audience Segment
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.
Agent Tool
Trigger Retention Workflow
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.
Get started with our ReSci Retention Science connector today
If you would like to get started with the tray.ai ReSci Retention Science connector today then speak to one of our team.
ReSci Retention Science Challenges
What challenges are there when working with ReSci Retention Science and how will using Tray.ai help?
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
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Start using our pre-built ReSci Retention Science templates today
Start from scratch or use one of our pre-built ReSci Retention Science templates to quickly solve your most common use cases.
ReSci Retention Science Templates
Find pre-built ReSci Retention Science solutions for common use cases
Template
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.
Steps:
- Trigger on Shopify order created or updated webhook event
- Transform order payload to map customer email, product SKUs, order value, and timestamp to ReSci event schema
- POST purchase event to ReSci API to update customer profile and trigger lifecycle model recalculation
Connectors Used: Shopify, ReSci Retention Science
Template
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.
Steps:
- Poll ReSci API on a scheduled interval or receive webhook for customers with churn score above threshold
- Create or update a task in Salesforce assigned to the account owner with churn risk score and last purchase date
- Post a formatted Slack message to the #retention-alerts channel with customer details and a link to the Salesforce record
Connectors Used: ReSci Retention Science, Salesforce, Slack
Template
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.
Steps:
- On a daily schedule, query ReSci API for customers in the high-LTV predicted segment
- Hash customer email addresses to SHA-256 per Facebook requirements
- Upsert hashed emails into the designated Facebook Custom Audience via Marketing API
Connectors Used: ReSci Retention Science, Facebook
Template
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.
Steps:
- Trigger on Zendesk ticket created, updated, or resolved webhook
- Extract customer email, ticket status, and satisfaction score from the Zendesk payload
- PATCH ReSci customer profile via API to update custom attributes for open ticket count, last ticket date, and CSAT score
Connectors Used: Zendesk, ReSci Retention Science
Template
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.
Steps:
- On a nightly schedule, query ReSci API for campaign performance data for the prior 24-hour window
- Transform API response into a flattened schema matching the BigQuery target table structure
- Stream insert or batch load records into the BigQuery marketing_performance dataset
Connectors Used: ReSci Retention Science, Google BigQuery
Template
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.
Steps:
- Trigger on Stripe customer.subscription.created webhook event
- Map subscription plan ID to the corresponding ReSci campaign ID using a lookup table in tray.ai
- POST to ReSci API to enroll the customer in the mapped onboarding campaign with subscription metadata as custom attributes
Connectors Used: Stripe, ReSci Retention Science