This page provides you with instructions on how to extract data from Close and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Close seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Close?
Close provides an inside sales SaaS and CRM platform that bundles calling, SMS, and email in a single platform. Users can make and receive calls and take business notes without getting on a phone or leaving the application. The software provides a single automated sales workflow system.
What is QuickSight?
Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.
Getting data out of Close
You can use Close's REST API to get data about contacts, leads, opportunities, and many more objects into your data warehouse. For example, to get a lead, you could GET /lead/{id}/.
Sample Close data
Here's an example of the kind of response you might see when querying a lead.
{
"status_id": "stat_1ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
"status_label": "Potential",
"tasks": [],
"display_name": "Wayne Enterprises (Sample Lead)",
"addresses": [],
"name": "Wayne Enterprises (Sample Lead)",
"contacts": [
{
"name": "Bruce Wayne",
"title": "The Dark Knight",
"date_updated": "2019-01-06T20:53:01.954000+00:00",
"phones": [
{
"phone": "+16503334444",
"phone_formatted": "+1 650-333-4444",
"type": "office"
}
],
"created_by": null,
"id": "cont_o0kP3Nqyq0wxr5DLWIEm8mVr6ZpI0AhonKLDG0V5Qjh",
"organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
"date_created": "2019-01-01T00:54:51.331000+00:00",
"emails": [
{
"type": "office",
"email_lower": "thedarkknight@close.io",
"email": "thedarkknight@close.io"
}
],
"updated_by": "user_04EJPREurd0b3KDozVFqXSRbt2uBjw3QfeYa7ZaGTwI"
}
],
"custom.lcf_ORxgoOQ5YH1p7lDQzFJ88b4z0j7PLLTRaG66m8bmcKv": "Website contact form",
"date_updated": "2019-01-06T20:53:01.977000+00:00",
"html_url": "https://app.close.io/lead/lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O/",
"created_by": null,
"organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
"url": null,
"opportunities": [
{
"status_id": "stat_4ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
"status_label": "Active",
"status_type": "active",
"date_won": null,
"confidence": 75,
"user_id": "user_scOgjLAQD6aBSJYBVhIeNr6FJDp8iDTug8Mv6VqYoFn",
"contact_id": null,
"updated_by": null,
"date_updated": "2019-01-01T00:54:51.337000+00:00",
"value_period": "one_time",
"created_by": null,
"note": "Bruce needs new software for the Bat Cave.",
"value": 50000,
"value_formatted": "$500",
"value_currency": "USD",
"lead_name": "Wayne Enterprises (Sample Lead)",
"organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
"date_created": "2019-01-01T00:54:51.337000+00:00",
"user_name": "P F",
"id": "oppo_8eB77gAdf8FMy6GsNHEy84f7uoeEWv55slvUjKQZpJt",
"lead_id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O"
},
{
"id": "oppo_klajsdflf8FMy6GsNHEy84f7uoeEWv55slvUjKQZpJt",
"organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
"lead_id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O",
"lead_name": "Wayne Enterprises (Sample Lead)",
"status_id": "stat_4ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
"status_label": "Active",
"status_type": "active",
"value": 5000,
"value_period": "monthly",
"value_formatted": "$50 monthly",
"value_currency": "USD",
"date_won": null,
"confidence": 75,
"note": "Bat Cave monthly maintenance cost",
"user_id": "user_scOgjLAQD6aBSJYBVhIeNr6FJDp8iDTug8Mv6VqYoFn",
"user_name": "P F",
"contact_id": null,
"created_by": null,
"updated_by": null,
"date_created": "2019-01-01T00:54:51.337000+00:00",
"date_updated": "2019-01-01T00:54:51.337000+00:00"
}
],
"updated_by": "user_04EJPREurd0b3KDozVFqXSRbt2uBjw3QfeYa7ZaGTwI",
"date_created": "2019-01-01T00:54:51.333000+00:00",
"id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O",
"description": ""
}
Loading data into QuickSight
You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.
Using data in QuickSight
QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.
Keeping Close data up to data
Now what? You've built a script that pulls data from Close and loads it into your data warehouse, but what happens tomorrow when you have new transactions?
The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Close's API results include fields like date_created that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.
From Close to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Close data in Amazon QuickSight is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Close to Redshift, Close to BigQuery, Close to Azure Synapse Analytics, Close to PostgreSQL, Close to Panoply, and Close to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Close with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Close data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Amazon QuickSight.