2023-01-25 | Article | Insights
With advancing technological capabilities, the advertising industry has moved from a broadcast era with mass communication through media, such as radio and television to a precision era grounded in programmatic buying with the major capability of delivering mass personalization. Recent developments in user expectations have led to the evolution of regulations that have made the industry enter its predictive stage, in which artificial intelligence and machine learning are harnessed to fill in data gaps, as well as anticipate customer needs and interests, resulting in growing businesses.
However, there is one thing standing in the way of easily fulfilling these demands. Personalized and compliant customer experiences today are hampered by data silos, preventing a holistic view of the customer.
The root of the challenge arises naturally, due to data being collected across different channels and touchpoints. Legacy technologies and organizational silos prevent consolidation and proper analysis of data, as well as organizational barriers potentially hindering the full utilization of analysis impeding actionable results. Furthermore, legacy systems do not always integrate well with new platforms or meet the needs for improved marketing performance.
By breaking these data silos, customers' personalization demands can be met while answering the above brands' questions, ultimately aligning the interests of both sides. The introduction of a Customer Data Platform (CDP) to a business can address this while ensuring data privacy.
The Customer Data Platform Institute, which is a vendor-neutral organization dedicated to helping marketers manage customer data, defines a CDP as “a packaged software that creates a persistent, unified customer database that is accessible to other systems”3. It is a piece of technology that can help marketers address the following challenges:
To accomplish this, a CDP is involved in the process of collecting, transforming, analyzing, visualizing, and activating user data. However, as business requirements may vary for different businesses, a modern state-of-the-art CDP should be an open platform, designed specifically for your business challenges. It must be able to break down your customer data silos to enable dynamically created audience segments. At the same time, it should provide machine learning (ML) capabilities to analyze customer purchase history and browsing behavior to identify customers who have similar characteristics and behavior patterns. The goal is for CPDs to be able to operate in real-time to ultimately activate based upon the collected information.
Briefly put, in addition to Marketing Data Warehouse (MDW) functionality, CDPs include Customer ID resolution, a UI to interact with the data, an activation layer to connect to marketing platforms, and an option for packaged predictive analytics and segmentation. Compared to stand-alone marketing analytics or MDW, CDPs support a more broad range of applications.
First-party data in a CDP is highly relevant to businesses since it comes directly from consumers resulting in high-quality signals that can be leveraged in predictive analytics to be assessed and scored in order to create segments. In order to collect first-party data, consent from customers needs to be obtained in order to comply with data regulation and data privacy laws, and to also ensure data consistency and quality. In this way, high completeness, reliability, and integrity are achieved. Additionally, the data is proprietary, so it provides a distinct advantage to the business over its competitors.
Today, many brands begin to leverage their own websites to enrich their customers' first-party data. If set up properly and durable, first-party data is of the highest quality compared to the alternatives provided with second and third-party data (see figure 2) and therefore has the highest value, attracting a company's resources to further develop its capabilities. For example, this can be done by stitching your Google Analytics data with your CRM data by building an identity graph. This way, different signals are combined, accessed, scored, and finally used for audience segmentation. This process can be automated and run continuously in real time. These endeavors are foundational to building solid first-party data, which in turn is foundational to establishing a successful CDP.
The increasing demand of users for data privacy and personalization resulting in regulatory and technological changes have set the stage for the rise of CDPs. Today, we observe companies investing heavily in these technologies. CDPs are used to break data silos and introduce ML capabilities to businesses addressing current marketing challenges, such as the need for predictive analytics and audiences built and activated in real time. Moreover, first-party data is crucial to the success of CDPs. Therefore, many brands are leveraging their own websites to enrich their customers' first-party data. After establishing a CDP, data collaborations can be approached using another emerging technology - Data Clean Rooms.
Stay tuned for our next CDP article focussing on our use-cases and success stories of solutions using Google Cloud and Zeotap.