People Data with Postman and CSV

For this guide, we're going to assume the following you're interested in using Datafiniti's people data to develop a sales campaign. Let's say you're a growth marketer that's been tasked with the following:

1. Collect email addresses of CEOs.
2. Refine that list to a specific geographic region.

Your environment and data needs:

1. You're working with Postman.
2. You want to work with CSV data.

Here are the steps we'll take:

1. Download Postman

We recommend using Postman when accessing the Datafiniti API. It's a great client for using any sort of RESTful API and works particularly well with Datafiniti. You can download Postman here.

2. Get your API token

The next thing you'll need is your API token. The API token lets you authenticate with Datafiniti API and tells it who you are, what you have access to, and so on. Without it, you can't use the API.

To get your API token, go the Datafiniti Web Portal (https://portal.datafiniti.co), login, and click your settings in the left navigation bar. From there, you'll see a page showing your token. Your API token will be a long string of letters and numbers. Copy the API token or store it somewhere you can easily reference.

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For the rest of this document, we'll use AAAXXXXXXXXXXXX as a substitute example for your actual API token when showing example API calls.

3. Run your first search

The first thing we'll do is do a test search that will give us a sense for what sort of data might be available. Eventually we'll refine our search so that we get back the most relevant data.

Since we want CEOs, let's try a simple search that will just give us people with that title.

With Postman open, click on the Datafiniti API V4 collection. Then navigate to Secured Endpoints > Search > Search People Data. Click on "Body" in the main screen.

Enter the following in the text area:

{
	"query": "jobTitle:CEO",
	"num_records": 1
}

Click the blue "Send" button. You should get a response similar to this:

{
    "num_found": 357,
    "total_cost": 1,
    "records": [
        {
            "address": "9855 Melbourne Ave",
            "businessName": "MICHIGAN MARKETING EXTREME LLC",
            "city": "Allen Park",
            "country": "US",
            "dateAdded": "2018-02-20T00:33:45Z",
            "dateUpdated": "2018-02-20T00:33:45Z",
            "emails": [
                "[email protected]"
            ],
            "firstName": "Bill",
            "keys": [
                "billmeymichiganmarketingextremecom"
            ],
            "lastName": "Mey",
            "jobFunction": "Business Management",
            "jobLevel": "C-Team",
            "jobTitle": "CEO",
            "numEmployeesMin": 1,
            "numEmployeesMax": 19,
            "phones": [
                "7346190715"
            ],
            "postalCode": "48101",
            "province": "MI",
            "sourceURLs": [
                "https://datafiniti.co"
            ],
            "id": "AWG2azB1QH9dEUBnfEki"
        }
    ]
}

Let's break down what the API call is all about:

Now let's dive through the response the API returned:

Within the records field, you'll see a single person returned with multiple fields and the values associated with that product. The JSON response will show all fields that have a value. It won't show any fields that don't have a value.

Each product record will have multiple fields associated with it. You can see a full list of available fields in our People Data Schema.

4. Refine your search

Let's say we decided to adjust our sales campaign to only target CEOs in Austin, TX.

We'll need to refine our search to make sure we're only people that match that critera. Modify your request body to look like this:

{
	"query": "jobTitle:CEO AND province:TX AND city:Austin",
	"num_records": 10
}

This API call is different in a couple ways:

1. It adds AND province:TX AND city:Austin to narrow down results to just people working in Austin, TX.
2. It changes "num_records": 1 to "num_records": 10 so we can look at more sample matches.

Notice how Datafiniti lets you construct very refined boolean queries. In the API call above, we're using a combination of AND queries to get exactly what we want.

Update your API call in Postman and hit "Send" to see the updated results.

If you would like to narrow your search to just exact matches you can place the search term in quotation marks.

{
	"query": "jobTitle:\"Product Developer\"",
	"num_records": 10
}

The above query will only return people with the exact title of Product Developer. The quotation marks need to be escaped using back slashes since they are already within another set of quotation marks.

5. Initiate a download of the data

Once we like what we see from the sample matches, it's time to download a larger data set! To do this, we're going to further modify our request body to look like this:

{
	"query": "jobTitle:CEO AND province:TX AND city:Austin",
	"num_records": 20,
  "format": "csv",
	"download": true
}

Here's what we changed:

1. We added "format": "csv. If you don't specify format, it will default to json. Using the CSV format will make analyzing our data easier for this example.
2. We change "num_records": 10 to "num_records": 20. This will download the first 20 matching records. If we wanted to download all matching records, we would remove num_records. num_records will tell the API to default to all available records.
3. We added "download": true. This tells the API to issue a download request instead of a search request.

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If num_records is not specified, ALL of the records matching the query will be downloaded.

When you make this API call, you'll see a response similar to:

{
    "id": 288,
    "results": [],
    "user_id": 15,
    "status": "running",
    "date_started": "2018-02-21 21:58:08.0",
    "num_downloaded": 0,
    "data_type": "people",
    "query": "jobTitle:CEO AND province:TX AND city:Austin",
    "format": "csv",
    "num_records": 20,
    "total_cost": 300
}

We'll explain each of these fields in the next section.

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When using the API, you will not receive any warning if you are going past your monthly record limit. Keep a track on how many records you have left by checking your account. You are responsible for any overage fees if you go past your monthly limit.

6. Monitor the status of the download

As the download request runs, you can check on its status by using the "Get Download" call in your Postman collection. Just replace :id with the id value you saw in the previous step.

If you keep running this call, you'll see some of the values update. Once the download completes, it will look something like this:

{
    "id": 288,
    "results": [
        "https://datafiniti-downloads.s3.amazonaws.com/15/7_1.txt?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20171116T174607Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604800&X-Amz-Credential=AKIAJYCTIF46QVBTXWYA%2F2017xxxx%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ecf13f1bb4b7adfdde1a99143541afd1d12347292eb9ec3f6ed1316c64d4eekf"
    ],
    "user_id": 15,
    "status": "completed",
    "date_started": "2018-02-21 21:58:08.0",
    "num_downloaded": 20,
    "data_type": "people",
    "query": "jobTitle:CEO AND province:TX AND city:Austin",
    "format": "csv",
    "num_records": 20,
    "total_cost": 300
}

Here's what these fields mean:

7. Download the result file(s)

Once the download response shows "status": "completed", you can download the data using the URLs in the results field.

If you've requested a lot of records (i.e., over 10,000), you may see more than 1 result object shown.

To download the result files, copy each url value and paste it into your browser. Your browser will initiate a download to your computer.

8. Open the result file(s) in Excel

Navigate to the file you downloaded and open it. Since it's a CSV file, it should open in Excel automatically. It will look something like:

9. Analyze the results

Using Excel, we can easily utilize the data to build our sales campaign.