Prebuilt Connectors

Push Marigold-generated audience insights into AWS Datalake for machine learning applications.

Sync data from BigQuery (Google’s cloud data warehouse) into Marigold to enrich customer profiles with offline or aggregated data; import curated datasets from BigQuery into Marigold to improve segmentation and personalization.

Query and fetch data via Dremio from a data lake, then send the results into Marigold to update marketing data – for example, use Dremio to aggregate user behavior from log files and load that summary into Marigold to adjust campaign targeting. (This is a specialized scenario since Dremio is a data virtualization tool.)

Stream customer data from DynamoDB (NoSQL database) into Marigold to keep profiles up-to-date – for instance, if you have user preferences or behavior logs in DynamoDB, replicating them to Marigold enables more personalized campaigns; use DynamoDB as a source for event data (e.g. game/app events) that Marigold can act on for messaging triggers.

Sync Marigold user interactions to DynamoDB for instant access by applications.

Backup customer lists and campaign reports from Marigold for compliance and external processing.

Update Firestore records with Marigold segmentation data for mobile personalization.

Trigger real-time customer workflows by sending Marigold event data to Google PubSub.

Extract business data from Microsoft Dataverse (used in Power Platform/Dynamics 365) into Marigold to enrich the marketing database – for example, bring over customer entities or engagement insights stored in Dataverse into Marigold for more informed segmentation; keep Marigold in sync with Dataverse so that contacts and attributes remain consistent across sales, service, and marketing.

Update customer profiles in MongoDB based on Marigold campaign interactions.

Sync Marigold audience lists with an MSSQL database for internal marketing teams.

Import relevant customer data from an Oracle operational database into Marigold to combine legacy system data with marketing profiles; for instance, load loyalty program data or offline customer records stored in Oracle into Marigold so those contacts can receive targeted campaigns.

Bring content recommendation and metrics from Outbrain into Marigold to assess user engagement across channels; for example, if certain users consistently click Outbrain content ads, add them into a Marigold segment for follow-up content via email.

Sync data from Amazon Redshift (cloud data warehouse) into Marigold to supplement customer profiles with analytics data or large-scale records; for example, export a segment of high LTV customers from Redshift and import into Marigold to target them with a special campaign.

Ingest unified customer event data from Segment (a customer data platform​) into Marigold to drive personalized campaigns with comprehensive user profiles; use Segment to gather multi-source behavioral data into Marigold for more precise marketing activation.

Sync consolidated customer data from Snowflake (cloud data warehouse) into Marigold to enrich profiles with analytics or offline data; pull segmentation lists or aggregated metrics from Snowflake into Marigold to improve targeting and personalization in campaigns.

Store Marigold campaign performance and customer engagement data for advanced analytics.

Load customer data files (CSVs, etc.) from GCS into Marigold to update or augment contact lists on a scheduled basis; use GCS as a staging area for exporting large data sets (user attributes, transaction logs) and importing them into Marigold for campaign use.

Send Marigold customer segmentation data to Snowflake to enable cross-channel marketing analysis.

Export email and customer activity logs from Marigold to Redshift for BI and reporting purposes.

Continuously import user data from a MongoDB database (often used for product/customer data stores) into Marigold to keep marketing profiles updated with the latest customer information; use MongoDB as a source of event data (e.g. app events) that Marigold can ingest and trigger campaigns from.

Pull customer or order records from a MySQL database into Marigold so that marketing has the latest information from the company’s primary app/website database; use MySQL data (such as a loyalty status flag stored in a user table) to create or update segments in Marigold for targeted messaging.

Sync data from a PostgreSQL database (for example, an internal user database or order system) into Marigold, ensuring that new or updated customer records are reflected for marketing use; utilize Postgres as a source for custom events or attributes (like a user’s last login date) that Marigold can use in campaign logic.

Sync Marigold contact records with Postgres for integration with internal marketing tools.

Import large datasets (e.g. nightly customer data exports) from Azure Blob Storage into Marigold to update contacts or campaign data in bulk; use Blob Storage as an intermediate stage to transfer on-premise or external data (like a list of new customers) into Marigold for marketing activation.

Use Marigold engagement metrics to train predictive models in Databricks.

Use Google Sheets as a lightweight database for marketing: new lead info or subscription lists maintained in Sheets can be pulled into Marigold (instead of manual CSV export) to update campaigns; automate syncing of a Google Sheet (e.g. containing event sign-ups or webinar attendees) into Marigold so those contacts immediately flow into the appropriate email workflows.

Import customer data from an MSSQL database into Marigold, similar to MySQL/Postgres use cases – ensure that any data stored in corporate MS SQL databases (like user info, order history) is regularly synced to Marigold for campaign usage; for example, nightly sync of new customers from an MSSQL ERP into Marigold to receive welcome emails.

Use Amazon S3 to store data exports (e.g. daily user sign-ups, or large historical data dumps) and have Airbyte move those into Marigold in bulk; for example, an S3 bucket with nightly user data can feed Marigold so the marketing team always has the latest customer information available for segmentation.

Migrate customer profiles and multi-channel engagement data from Braze (a customer engagement platform for multichannel marketing​) into Marigold to maintain continuity in messaging; import historical Braze campaign results into Marigold’s database to analyze past interactions and optimize future marketing strategies on the new platform.

Migrate email contact lists and campaign history from Brevo (Sendinblue) into Marigold to consolidate marketing efforts; use Brevo’s email engagement data (opens/clicks) in Marigold to ensure continuity in segmentation and avoid messaging users who already responded via the previous platform.

Sync meeting booking data from Calendly into Marigold to send automated confirmation, reminder, and follow-up emails via Marigold (e.g. after a demo is scheduled, enter the prospect into a nurture flow); add prospects who schedule meetings through Calendly to Marigold contact lists for future marketing campaigns (such as newsletters or product updates).

Import call tracking data from CallRail into Marigold to integrate offline engagement (phone calls) into customer profiles; for example, if a contact calls a tracked number and inquires about a product, Marigold can log that and trigger a follow-up email or add them to a call follow-up segment.

Migrate subscriber lists and email automation sequences from ConvertKit (an email marketing platform for creators) into Marigold to unify all marketing communications; use ConvertKit tag/segment information in Marigold to maintain how subscribers were categorized (e.g. import a “newsletter vs customer” tag to continue appropriate targeting in Marigold).

Migrate event-triggered messaging data from Customer.io (an automated messaging platform) into Marigold – bring over user event streams or segment membership to replicate Customer.io’s logic in Marigold; ensure users who were in certain Customer.io nurture tracks are imported and placed into equivalent tracks in Marigold, preserving continuity of multi-channel messaging.

Import identified company website visitors from Leadfeeder into Marigold as new B2B leads to nurture via email; leverage Leadfeeder’s firmographic data (company name, industry from site visits) in Marigold to segment and tailor B2B marketing campaigns to those prospects.

Import email sending events from Mailgun (transactional email service) into Marigold to log those sends on contact timelines (e.g. if Mailgun sent a password reset, have Marigold log it to avoid overlapping messaging); migrate mailing lists from Mailgun to Marigold if consolidating email platforms.

Migrate email marketing data from Omnisend (e-commerce marketing platform) into Marigold—bring over subscriber lists and their engagement metrics to continue targeting them; use Omnisend campaign results in Marigold to adjust segmentation (e.g. flag customers who responded to past promotions) after switching platforms.

Bring content recommendation and metrics from Outbrain into Marigold to assess user engagement across channels; for example, if certain users consistently click Outbrain content ads, add them into a Marigold segment for follow-up content via email.

Migrate B2B marketing leads and their engagement data from Pardot (Salesforce’s marketing automation platform) into Marigold as part of transitioning platforms; use Pardot scoring or segmentation data in Marigold to maintain continuity (for example, carry over a lead’s score or list membership so Marigold can continue the appropriate nurture track).

Import email delivery events from Postmark (transactional email service) into Marigold to consolidate messaging logs – for example, log password reset emails (sent by Postmark) on the user’s profile in Marigold; if migrating, move transactional email recipients from Postmark to Marigold to handle those emails from one platform.

Import NPS survey results from Retently into Marigold to segment customers by satisfaction (promoters vs detractors); trigger targeted follow-up campaigns in Marigold – for example, send referral request emails to promoters, and win-back or support escalation emails to detractors based on Retently feedback.

Import customer feedback collected via Survicate (e.g. website surveys) into Marigold to identify segments like “interested in feature A” or “unhappy with service” and use that for targeted messaging; follow up Survicate respondents in Marigold with emails addressing their specific feedback or asking for a review.

Pull content ad campaign performance data from Taboola into Marigold to gauge how content marketing is influencing customer behavior; use Taboola audience engagement data in Marigold to identify segments for follow-up email campaigns or retargeting.

Import customer review ratings and feedback from Trustpilot (a global review platform​) into Marigold to enrich customer profiles with sentiment data; leverage Trustpilot scores to segment contacts in Marigold (e.g. send referral incentive emails to highly-rated customers, and service recovery campaigns to those who left low ratings).

Pull advertising performance data from Google Ads into Marigold to measure cross-channel campaign ROI; use audience insights from Google Ads within Marigold to refine segmentation and retarget users with personalized messaging.

Sync contact and lead data from HubSpot (a CRM platform for inbound marketing) into Marigold to enrich profiles for targeted campaigns; integrate sales lifecycle insights from HubSpot to tailor Marigold email outreach and improve lead nurturing.

Migrate customer profiles and engagement history from Iterable (a cross-channel marketing platform) into Marigold to consolidate campaigns onto one platform; ingest Iterable’s event data into Marigold for a unified view of customer interactions during a platform migration.

Migrate subscriber lists and email engagement data from Mailchimp (a marketing automation platform) into Marigold to unify email campaigns; import Mailchimp campaign history into Marigold to maintain customer touchpoint continuity and analytics after transitioning platforms.

Import email delivery and engagement events from Sendgrid into Marigold to update contact profiles with open/click data; use Sendgrid suppression or bounce data in Marigold to maintain clean lists and improve campaign targeting.

Import communication logs (SMS sends, delivery statuses) from Twilio into Marigold to unify messaging history across channels; use Twilio SMS engagement data in Marigold to trigger complementary email campaigns or adjust messaging frequency for customers.

Retrieve Facebook Ads campaign metrics and audience data into Marigold to compare ad engagement with email engagement in one place; use Facebook Custom Audience insights in Marigold to inform segmentation (e.g. create email segments of users who clicked Facebook ads for a follow-up nurture sequence).

Pull Instagram business account metrics (followers, post engagement) into Marigold to identify highly engaged users for targeted outreach; combine Instagram ads audience data with Marigold email lists to coordinate cross-channel campaigns (e.g. send emails to users who interacted with Instagram content).

Import Amazon Advertising campaign performance data into Marigold to measure conversions and sales driven by ads alongside email campaigns; use Amazon Ads audience segments in Marigold (if available) to retarget customers via email who showed interest in specific products.

Pull LinkedIn Ads lead generation form submissions into Marigold to quickly add new B2B leads to email nurture sequences; import LinkedIn ad campaign performance data into Marigold to evaluate and segment leads by their ad engagement (for example, targeting email content differently for leads from a specific campaign).

Migrate marketing leads, contacts, and their engagement history from Marketo (a marketing automation SaaS platform) into Marigold to centralize all campaigns; import Marketo email/open/click data into Marigold to maintain segmentation and ensure no loss of personalization after switching platforms.

Pull advertising metrics from Microsoft (Bing) Ads into Marigold to analyze how search ad clicks convert into customer actions captured by Marigold; use Bing Ads audience or keyword data in Marigold to segment users (for example, target email content based on interest inferred from search ad interaction).

Pull Snapchat Ads performance data into Marigold to compare ad-driven engagement with email engagement; if using Snapchat’s lead-gen ads, send those leads from Snapchat into Marigold in real-time so they can receive welcome or drip emails promptly after interacting with the ad.

Import survey responses and respondent details from SurveyMonkey into Marigold to enrich customer profiles with feedback data; segment and target contacts in Marigold based on their SurveyMonkey feedback (for example, send different follow-up content to customers who indicated high satisfaction vs. those who gave lower ratings).

Bring TikTok Ads data (campaign impressions, clicks) into Marigold to evaluate cross-channel impact on conversions; if TikTok lead generation campaigns are used, automatically feed those new leads into Marigold so they can receive follow-up emails or SMS through Marigold.

Automatically send new lead submissions from Typeform forms into Marigold to kick off email nurture campaigns; use answers from Typeform surveys or quizzes in Marigold to personalize follow-up content (e.g. segment users in Marigold based on a quiz outcome and send tailored messaging to each segment).

Migrate e-commerce customer lists and personalized segments from Klaviyo (an email marketing platform for online stores​) into Marigold to centralize messaging; import Klaviyo event data (like recent purchases or browse abandonment tracked in Klaviyo) into Marigold so ongoing campaigns (welcome series, cart recovery, etc.) continue without interruption on the new platform.

Import e-commerce data from Cart.com (orders, customers) into Marigold to drive personalized campaigns (similar to Shopify use case); for instance, use Cart.com purchase history in Marigold to trigger post-purchase thank-you emails or to segment customers for product recommendation emails.

Import payment event data from GoCardless into Marigold (e.g. notification of a completed or failed direct-debit payment) to trigger customer emails via Marigold (such as payment confirmations or failure alerts); use GoCardless subscription info in Marigold to segment customers by plan or payment status for targeted retention campaigns.

Import customer and order data from NetSuite (which combines ERP/CRM) into Marigold to drive marketing communications – e.g., if a new customer is created in NetSuite or a sale is recorded, have that information flow into Marigold to trigger appropriate welcome or upsell campaigns; use NetSuite’s customer classifications in Marigold to segment email outreach (such as distinguishing retail customers from wholesale in messaging).

Bring content recommendation ad metrics from Outbrain into Marigold to assess user engagement across channels; for example, if certain users consistently click Outbrain content ads, add them into a Marigold segment for follow-up content via email.

Import transaction notifications from Paystack (online payments) into Marigold to update customer purchase histories and trigger receipts or follow-up emails; use Paystack payment failure events to prompt Marigold to send payment retry or support emails to affected customers.

Import subscription billing data from Recurly (similar to Chargebee) into Marigold to drive renewal and upsell campaigns; for instance, when a subscription is about to expire or a trial is ending (events from Recurly), trigger Marigold to send reminder emails or promotional offers.

Import form submissions or sign-ups from a Webflow website into Marigold to capture new leads from your Webflow marketing site; use Webflow e-commerce order data (if Webflow’s e-com is used) in Marigold similar to Shopify/WooCommerce to drive personalized post-purchase or re-engagement emails.

Import customer orders and purchase history from Shopify (an e-commerce platform​) into Marigold to enable personalized marketing based on shopping behavior; trigger targeted post-purchase campaigns in Marigold (e.g. abandoned cart emails or re-engagement offers) using Shopify data.

Bring order history and customer details from WooCommerce (an open-source e-commerce plugin for WordPress​) into Marigold to drive personalized campaigns based on purchase behavior; trigger Marigold email workflows (like re-engagement for cart abandonment or product review requests) using WooCommerce data.

Import subscription and billing data from Chargebee into Marigold to enable campaigns based on subscription status (e.g. upsell campaigns to active subscribers or win-back emails when a subscription is about to expire); trigger transactional messaging from Marigold (like renewal reminders) using Chargebee’s subscription events.

Import subscription and billing data from Chargebee into Marigold to enable campaigns based on subscription status (e.g. upsell campaigns to active subscribers or win-back emails when a subscription is about to expire); trigger transactional messaging from Marigold (like renewal reminders) using Chargebee’s subscription events.

Import PayPal transaction records into Marigold to update purchase history for customers (e.g. when a customer completes a PayPal purchase, reflect that in Marigold contact profile); trigger post-purchase email receipts or feedback requests from Marigold when new PayPal transactions are detected.

Import payment events from Stripe into Marigold to enrich customer profiles with purchase values and subscription statuses; use Stripe data (e.g. a failed payment or a high-value purchase) to trigger Marigold email workflows like dunning emails for failed charges or VIP appreciation messages.

Pull Amazon Marketplace sales and customer data via the Seller Partner API into Marigold to incorporate Amazon customers into your direct marketing (where allowed); for instance, after an Amazon order, add whatever customer contact info is available to Marigold to send shipping updates or product care tips (noting Amazon’s contact info policies).

Import subscription analytics from ChartMogul into Marigold to inform marketing – e.g. bring MRR or LTV metrics per customer into Marigold to segment high-value customers for VIP campaigns; use churn analytics from ChartMogul to trigger win-back email flows in Marigold for recently churned subscribers.

Use monitoring/analytics data from Datadog (e.g. user experience metrics) in Marigold if applicable – for example, if Datadog tracks user actions as logs, those could be ingested to trigger technical alert emails; however, this is uncommon, and Datadog’s system metrics aren’t typically used in Marigold for marketing.

Query and fetch data via Dremio from a data lake, then send the results into Marigold to update marketing data – for example, use Dremio to aggregate user behavior from log files and load that summary into Marigold to adjust campaign targeting. (This is a specialized scenario since Dremio is a data virtualization tool.)

Import product usage metrics and health scores from Gainsight PX into Marigold to trigger customer success campaigns (e.g. upsell highly engaged users or re-engage users whose usage dropped); use Gainsight PX’s customer engagement data in Marigold to segment users by adoption level and send tailored educational or retention emails.

Bring content recommendation ad metrics from Outbrain into Marigold to assess user engagement across channels; for example, if certain users consistently click Outbrain content ads, add them into a Marigold segment for follow-up content via email.

Import in-app user behavior and feedback data from Pendo into Marigold to trigger lifecycle emails (e.g. onboarding or feature adoption campaigns) based on product usage; use Pendo’s user segments (like “feature X not used yet”) in Marigold to send targeted educational content encouraging users to try that feature.

Use web analytics from Plausible (lightweight analytics) in Marigold similarly to GA4: for example, import goal conversion events or traffic metrics to segment contacts by website behavior; trigger Marigold emails when Plausible signals certain events (though this would likely be a manual setup).

Send Marigold event data to Timeplus for up to date marketing insights.

Store Marigold campaign performance and customer engagement data for advanced analytics.

Leverage behavioral analytics from GA4 in Marigold to trigger personalized campaigns based on website/app actions; import aggregated engagement metrics from GA4 into Marigold to enhance audience segmentation with web interaction data.

Store vectorized Marigold customer insights in Milvus for AI-driven recommendations.

Send Marigold audience embeddings to PGVector for advanced machine learning models.

Export Marigold segmentation embeddings into Pinecone for personalized recommendations.

Send Marigold insights to Snowflake Cortex for AI-powered predictions.

Sync contacts and leads from Close.com (a sales CRM) into Marigold so marketing can nurture those leads via email; use Close.com opportunity stages in Marigold to tailor campaigns (e.g. if a deal is in negotiation, avoid sending generic promos and instead send focused case studies).

Sync contacts, leads, and deals from Freshsales (CRM) into Marigold to ensure marketing has the latest customer data; use Freshsales stages or tags in Marigold to tailor email campaigns (for example, when a deal is marked “Closed Won” in Freshsales, trigger a welcome email series from Marigold).

Extract business data from Microsoft Dataverse (used in Power Platform/Dynamics 365) into Marigold to enrich the marketing database – for example, bring over customer entities or engagement insights stored in Dataverse into Marigold for more informed segmentation; keep Marigold in sync with Dataverse so that contacts and attributes remain consistent across sales, service, and marketing.

Import customer and order data from NetSuite (which combines ERP/CRM) into Marigold to drive marketing communications – e.g., if a new customer is created in NetSuite or a sale is recorded, have that information flow into Marigold to trigger appropriate welcome or upsell campaigns; use NetSuite’s customer classifications in Marigold to segment email outreach (such as distinguishing retail customers from wholesale in messaging).

Import contact and company records from Nutshell (CRM) into Marigold so that email campaigns can reach the same prospects tracked by sales; use Nutshell opportunity status to segment email communications (e.g. prospects vs customers) in Marigold.

Bring content recommendation and metrics from Outbrain into Marigold to assess user engagement across channels; for example, if certain users consistently click Outbrain content ads, add them into a Marigold segment for follow-up content via email.

Sync leads and contacts from Zoho CRM into Marigold to unify sales and marketing efforts (e.g. ensure that new leads entered in Zoho are automatically added to Marigold email campaigns); use ZohoCRM deal status or contact properties in Marigold to tailor marketing messages (such as sending different content to “Prospect” vs “Customer”).

Sync accounts and contacts from Salesforce (CRM) into Marigold to ensure marketing has up-to-date customer info for segmentation; use Salesforce sales data (e.g. lead status or purchase history) in Marigold to trigger targeted marketing campaigns aligned with the sales funnel.

Import form submissions or sign-ups from a Webflow website into Marigold to capture new leads from your Webflow marketing site; use Webflow e-commerce order data (if Webflow’s e-com is used) in Marigold similar to Shopify/WooCommerce to drive personalized post-purchase or re-engagement emails.

Import customer orders and purchase history from Shopify (an e-commerce platform​) into Marigold to enable personalized marketing based on shopping behavior; trigger targeted post-purchase campaigns in Marigold (e.g. abandoned cart emails or re-engagement offers) using Shopify data.