Data Types & Data Onboarding

Eddy Widerker
September 14, 2020

You probably heard about 1st, 2nd and 3rd party data. However, let's actually dive a little bit deeper and not only explain what they are but also how they work when it comes to data collection and onboarding.

First things first, let's talk about the different data types

1st Party Data

1st party data usually refers to the data that your organization collects, this can be website visitors, individuals that purchased from your store offline or data that you acquired and now own. In many instances, this data is Personal Identifiable Information (PII) such as a users first and last name, mailing address, email etc.

2nd Party Data

2nd party data assets that are owned by a partner organization that decided to share their data with you. For instance, the automotive brand Lexus is owned by the parent company Toyota Motor Corporation. If Lexus decides to share their data directly with Toyota which is also owned by the parent company, this would be considered 2nd party data sharing.

3rd Party Data

3rd party data is generated and owned by other parties and vendors. Currently there are over 75+ active data providers offering over 50,000 different segments. From frequent Starbucks visitors to users that are about to purchase a Mercedes C class based on their current online shopping behavior.

When it comes to selecting the right 3rd party segment, most brands feel very overwhelmed as the data source, data methodology and refresh cycles of a given segment are mostly unclear. Therefore making it very challenging in identifying the right data partner for a brand. Hint, this is one of the main reasons why we created ClearSegment.

Now, lets take a look at the various methods on onboarding and collecting data.

Data Onboarding

Data onboarding refers to the process of taking offline customer identifiers such as an email, phone number, address etc. and translating them into a targetable ID such as a cookie, device, Household or platform ID.

Data Onboarding Process

Data Collection

Data collection is essential for any platform, while platforms differ in capability and function, the below terms are quite often used when it comes to data collection.

Web& App Sources

This includes, site-traffic, event tracking, app users (Mobile / Desktop)

Programmatic Platforms

Impression level data, conversions, video views from DSPs, Ad Server (Google AdWords)

Marketing Platforms

Content interactions (eMail,Web, Social)

Offline Sources

Offline CRM Systems, ERP systems or Point-Of-Sale / transaction systems that are being ingested into your platform

Other Data Collectors / Vendors /Partners

Data shared with partners / affiliates or3rdParty data vendors (allowing to enrich data sets)

Collection Methods

Depending on the platform you use, there are a several ways to collect data, here are the most common ones.

JavaScript Tracking Tags

This is usually a snipped of code (Java Script or JS Script) that is placed on a website to collect browsing behavior, Cookie & Device ID's as well as any customer ID's that are associated with one single individual.

Tracking Pixel (1x1)

A tracking pixels is a HTML code snipped that is placed on a website. The tracking pixel is not able to collect as much data as a JavaScript and is now rarely used.

Mobile SDK

Mobile Software Development Kits are usually supplied by your platform. This allows you to include your SDK into a brands app, which in turn allows you the collection of data.

Offline Sources

This usually refers to sales or Personal Information Information (PII) that are being onboarded to your platform.

Data Normalization

This dependent on your platform but Data Normalization is a cleaning step when data is being ingested.

Here's a example of how a DMP would normalize data:

  • Gather IDs
  • Delete redundant or useless data
  • Transfer the source's data schema to the DMP data schema
  • Enrich data with additional data points that are associated to a an ID.

Profile Building

Profile building is an essentials part of the normalization and enrichment process, as it is responsible for transforming the collected data into events and profiles which are curtail for segmentation

Profile Building Examples

Data Taxonomy

A taxonomy refers to a system for naming and organizing information groups based on their similarities or data source. Based on the brand or organization, taxonomies might look different. Below are 3 examples from 3 different verticals Automotive, Airline and eCommerece

Data Taxonomy Examples

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