Based on the nature of the data collected, data can be divided into four major types:
- Identity Data refers to data that helps to identify a user. For example, First and last names, age, gender, email id, social media ids, and so on
- Descriptive data refers to data that reveals more information about the person and their lifestyle, for example, their income, hobbies, type of house, type of car, relationship status, nature of the job, and so on.
- Behavioral data refers to the data that reveals how a person behaves with brands, for example, purchase history, cart abandonment, ads liked, ads clicked, interaction on the app and website, and so on.
- Qualitative data refers to the data that provides more information about customer preferences and opinions, for example, product rating, service rating, reasons for a particular brand preference, and so on.
These four types of data can come from first-party, second-party, or third-party sources. Identity data comes from a first-party or second-party source, the rest can come from any source.
The job of a CDP is to take the different sources of data and their different data types and bring them to a single platform where we can stitch the data. Next, we will discuss the concept of data stitching and focus on identity stitching which is used to create unique customer profiles.
Now that we know that CDPs bring in different types of data, we need to understand how they manage to make sense of it. Customer data comes from different devices, user ids, session ids, and so on. To create unique customer profiles, we need to uniquely identify the user irrespective of the device, IP addresses, and so on. We also need to consider privacy regulations like the GDPR. The job is easier said than done, but we will try to understand the challenges here and propose solutions later in the book.
To put it simply, identity resolution is the process of uniquely identifying the user who is using different channels to interact with the brand. For example, you may visit the website of a brand, and add a product to the cart. But to checkout, you need to log in. After you log in to that website, you would still want that product to be in your cart, rather than having to again add the product to the cart. Now it is the job of the identity resolution system to identify the previously not logged-in user as the same as the logged-in user. Once it does that, you will be able to see that the cart already has the product which you added when you were browsing without logging in. This provides a better customer experience.
The preceding example was a relatively easy scenario. Let us look at another scenario. Let’s say you are browsing an e-commerce website without logging in from your laptop. Then you add a few products to the cart. But you don’t check out from the laptop. Later, you open the same website on your mobile. There, you log in and can see the products in your cart. This was possible because of identity resolution.
Identity resolution is the ability to persistently recognize a unique entity across all digital and physical channels to build a rich profile to personalize the customer journey.
Identity resolution makes sense of the distributed customer data and turns it into a concrete data point useful for business use cases. It is a challenging process when you have to also consider privacy restrictions. For users who don’t give consent to use the data for personalization, we need to make sure we are not storing any data that can be used to uniquely identify that user.