Drip to Looker

This page provides you with instructions on how to extract data from Drip and analyze it in Looker. (If the mechanics of extracting data from Drip 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 Drip?

Drip is an online marketing automation platform.

What is Looker?

Looker is a powerful, modern business intelligence platform that has become the new standard for how modern enterprises analyze their data. From large corporations to agile startups, savvy companies can leverage Looker's analysis capabilities to monitor the health of their businesses and make more data-driven decisions.

Looker is differentiated from other BI and analysis platforms for a number of reasons. Most notable is the use of LookML, a proprietary language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. LookML enables organizations to abstract the query logic behind their analyses from the content of their reports, making their analytics easy to manage, evolve, and scale.

Getting data out of Drip

You can collect data from Drip’s servers using webhooks and user-defined HTTP callbacks. Set up the webhook in your Drip account, and define a URL that your script listens to and from which it can collect the data.

Sample Drip data

Once you've set up webhooks and HTTP endpoints, Drip will begin sending data via the POST request method. Data will be enclosed in the body of the request in JSON format. Here's a sample of what that data might look like.

{
  "id": "z1togz2hcjrkpp5treip",
  "status": "active",
  "email": "john@acme.com",
  "custom_fields": {
    "name": "John Doe"
  },
  "tags": ["Customer", "SEO"],
  "time_zone": "America/Los_Angeles",
  "utc_offset": -440,
  "created_at": "2017-06-21T10:31:58Z"
  "ip_address": "123.123.123.123",
  "user_agent": "Mozilla/5.0",
  "lifetime_value": 2000,
  "original_referrer": "https://google.com/search",
  "landing_url": "https://www.drip.co/landing",
  "prospect": true,
  "base_lead_score": 30,
  "lead_score": 65,
  "user_id": "123"
}

Preparing Drip data

You need to map all the data fields in the JSON data from your webhook into a schema that can be inserted into your database. For each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Loading data into Looker

To perform its analyses, Looker connects to your company's database or data warehouse, where the data you want to analyze is stored. Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake.

Looker's documentation offers instructions on how to configure and connect your data warehouse. In most cases, it's simply a matter of creating and copying access credentials, which may include a username, password, and server information. You can then move data from your various data sources into your data warehouse for Looker to use.

Analyzing data in Looker

Once your data warehouse is connected to Looker, you can build constructs known as explores, each of which is a SQL view containing a specific set of data for analysis. An example might be "orders" or "customers."

Once you've selected any given explore, you can filter data based on any column available in the view, group data based on certain fields in the view (known as dimensions), calculate outputs such as sums and counts (known as measures), and pick a visualization type such as a bar chart, pie chart, map, or bubble chart.

Beyond this simple use case, Looker offers a broad universe of functionality that allows you to conduct analyses and share them with your organization. You can get started with this walkthrough in Looker's documentation.

Keeping Drip data up to date

Once you’ve coded up a script or written a program to get the data you want and move it into your data warehouse, you’re going to have to maintain it. If Drip modifies its webhook implementation, or sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Drip to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Drip data in Looker 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 Drip to Redshift, Drip to BigQuery, and Drip to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Drip data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Looker.