Connect Reply and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Reply
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Reply and Neo4j
Enable Integrations or automations with these events of Reply and Neo4j
Actions
Mark as Finished
Marks a person as finished in the campaign (i.e., they have completed all required steps or the campaign has ended for them).
Remove Person From Campaign
Removes a person from a specific campaign in an array of campaigns.
Create Person and Push to Campaign
Creates a person and immediately adds them to a campaign.
Create Person
Creates a new person.
Add Step to Campaign
Adds a new step to an existing campaign.
List Campaigns
Get List of Campaigns
Know More About Reply and Neo4j Integrations
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Frequently Asked Questions
To start, connect both your Reply and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Reply triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Reply data is recorded in Neo4j. This includes choosing which data fields go into which fields of Neo4j, setting up custom formats, and filtering out unwanted information.
The data sync between Reply and Neo4j typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Reply and Neo4j. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Reply
Reply is a sales automation platform that puts outreach on autopilot, while still making each communication personal.
Learn MoreAbout Neo4j
Neo4j is a leading graph database platform that enables organizations to unlock the value of connections, influences, and relationships in data. It is designed to handle highly connected data and is used for a variety of applications such as fraud detection, real-time recommendations, and network management.
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