# Marketo Spara supports bidirectional data syncs with Marketo. For sending data to Marketo, Spara supports: * Writing entire conversation histories * Writing structured data, ex. the answer a lead gives to a specific question Syncing data from Marketo to Spara streamlines your buyer's sales process by: * Spara never reasks a question * Spara's behavior can react to each lead based on where they are in your sales funnel * Syncs can be triggered at any point in the conversation ### Authentication {% hint style="info" %} You must be a Manager-level user to manage third party integrations. You will likely want the individual who manages your Marketo deployment to complete these instructions. {% endhint %} Click the "Organization Settings" link in the left hand navigation bar. Then click the "Integrations" tab. Click "Authorize" within the Marketo module. This will lead you through an authentication flow. There are no further steps necessary upon completing this flow. You should see that Marketo is "Enabled" within Spara's Integrations page. ### Data Syncing Spara will automatically sync all conversation data to matching `Lead` objects in Marketo. This includes the full conversation history. Spara matches data based on supplied [https://docs.spara.com/developers/spara-api/query-parameters](https://docs.spara.com/developers/spara-api/query-parameters "mention"). As soon as an email is captured in the Spara chat, Spara attempts to match the `email` field to a Marketo `Lead` object with an identical email property. Data continues to sync throughout the conversation. Additional fields may be synced to Marketo by updating the Field Mapping section of the Marketo integration. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.spara.com/integrations/crm-integrations/marketo.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.