Creating a deep understanding of your customers and then spreading that knowledge throughout your business is a significant goal and challenge for companies that are using data to drive commerce today. Better understanding of your customers allows you to provide more value, which in turn increases your customers’ lifetime value. Data is key to achieving this understanding in the digital age, and the method for delivering this data-driven insights is often referred to as a 360-degree view of the customer. This single source of truth can lead to significant benefits, from uncovering hidden insights to running targeted marketing campaigns and supporting powerful ML use cases like personalization.
Creating a unified view of your customers is an essential objective for most businesses today. However, it is still incredibly challenging, even with modern data tools. In this article, we will take a look at the historical context and the technical difficulties behind building a customer 360.
One of the initial hurdles is aggregating all your customer’s data from various sources, including websites, applications, and cloud tools, into a single location. Traditional customer data platforms (CDPs) were designed to simplify this process and make building a customer 360 easier. However, these legacy CDPs ultimately failed to deliver on this promise, as they were built with limited integration flexibility and proprietary customer profile models, leading to additional data silos.
This left companies with the realization that building a customer 360 is primarily a data problem. As a result, there has been a shift towards Data and Engineering Teams owning the responsibility of creating a 360-degree view of the customer.
Fortunately, with the recent commoditization of customer data pipelines and the scalability of cloud data warehouses, it has become easier to centralize all customer data. However, creating a customer 360 and deriving value from it requires understanding the entire data activation lifecycle.
One of the challenges is unifying all the customer data into complete customer profiles. This involves building a table with one row per user and columns representing everything known about those users. These columns generally fall into categories such as unique identifiers, known user attributes, and computed traits.
Another significant challenge to building a customer 360 is managing user identities, especially when it comes to transitioning from anonymous to known user identities and stitching together identities associated with multiple devices. This requires maintaining an identity graph and computing transitive closure, which is non-trivial in a SQL environment.
Building semantic features from events and metadata is also a complex process. Semantic features require working with multiple dimensions and events from multiple data sources, which involve numerous complex, repetitive SQL operations.
In the end, creating a 360-degree customer view and extracting value from it requires mastering the entire data activation lifecycle, including challenges with data collection, identity stitching, and feature semantics.