Because personalization is now a competitive differentiator, there is widespread agreement in the value of customer data, that it holds the secret to generating hyper-personalized customer experiences that reflect a deep understanding of the customer. Yet despite the shared belief that becoming more data-driven with a focus on customer-centricity is the way forward, there is less accord over the proper role of enterprise technology in orienting the business around the customer.
There are competing visions for how to best extract insight from customer data, and then how to use a deeper understanding to achieve better outcomes through more personalized experiences. Most agree that it is necessary to bring all customer data together in the form of a single customer view, and to then activate the data by sending it to various end channels. Within this framework, however, there are different viewpoints for how to go about it. Some consider the collection of data in a data warehouse or a data lake as sufficient for the creation of a single view. Some tout reverse ETL as the main requirement for moving data through the system to activation.
It seems that every vendor has a different idea for how best to bring data together better, faster, and easier. What tends to get lost in all the noise about which tool can do what, however, is what marketers and business users care about most, which is using technology to deliver the best possible customer experience. Most solutions fall short of this ideal because they focus on doing one thing well (e.g., reverse ETL) rather than make sure that customer data is always ready for business use, from ingestion through activation.
Customer Data Technology: The Right Powerhouse to Drive Outcomes
What is clear is that enterprise-grade customer data technology is key to delivering personalized customer experiences at scale based on a deep understanding of a customer by focusing first and foremost on data. What is less clear is what form that technology takes, whether as a CDP, a customer data management software, data quality & hygiene technology, identity resolution technology, or a verticalized solution.
There are dozens of customer data technology vendors, and each may highlight a different strength, but the good ones recognize that, in the end, the purpose is to provide the best data to create the best segments, audiences and personalized downstream actions.
Enterprise-grade customer data technology is key to delivering personalized customer experiences at scale based on a deep understanding of a customer by focusing first and foremost on data.
To accomplish this, certain core capabilities are required. Those are to ingest and fix messy customer data, to resolve identities at individual and relationship levels, to create an accurate and real-time unified customer profile, to build reusable segments (ideally without code) and to activate those segments against any marketing or CX use case.
In this context, enterprise-grade customer data technology is more than a marketing tool. It may primarily be used by marketers, but making data ready for business use as it flows through the enterprise is ultimately mission critical for the business. Trust in data results in better marketing campaigns, but also better programs, initiatives and results for any use case that rests on having a better understanding of the customer.
The core capabilities of enterprise-grade customer data technology are distinguishable from other systems that work primarily with their own data, store limited details for limited periods, do not resolve underlying data quality issues and/or do not maintain a persistent profile useable in real-time for direct customer engagement.
Customer Data Basics: Build a Unified Customer Profile
Some of the market confusion about how to understand customer data technology stems from vendors offering definitions tailored to include their systems – and to exclude competitors. This creates a wide gap, where any system that assembles customer data might call itself customer data technology, while others claim that the title is reserved for a system with specific activation capabilities such as campaign definition, journey orchestration, and message delivery.
The unfortunate result of competing claims about what’s most important about customer data technology is an inability to understand what a particular product actually does, which leads some to devalue customer data technology altogether. But there should be no confusion about the basic function of any underlying customer data technology foundation – to assemble data from all sources to build a single customer view (also known as a golden record or unified customer profile) that is available to any system that needs it.
Accepting that this is the ultimate purpose, any company considering customer data technology to deliver personalized experiences should determine how it will meet the company’s specific business and technology requirements. Adopting a use-case driven approach to selecting this technology, i.e., determining an ROI for hyper-personalized experiences, will help ensure that the technology can grow with the company as use cases evolve.
A use-case driven approach also helps an organization pare down unwanted or unnecessary features and functionality, and avoid duplications. It is also critical to map use cases to where the data resides. Many innovations have been made with SaaS solutions, but many use cases require the data to remain in place, within an organization’s security perimeter particularly where the use of customer data is tightly regulated as it is in healthcare and financial services.
The good news is that some technology innovations are empowering enterprises to break the cost vs. control tradeoff that has been difficult to navigate to-date with SaaS solutions. That tradeoff generally held that a SaaS offered a more cost-friendly option with less control, whereas an on-premises environment offered more control but with a higher cost to operate and maintain the software.
There are now a variety of self-hosted options available that break the traditional cost vs. control trade-off between a SaaS or a self-hosted (on-premises/private cloud) environment. In a self-hosted environment using modern data cloud technology like Snowflake, organizations maintain control over their data within their own security perimeter, and software companies can manage the software within that perimeter. The same holds true for on-premises or private cloud deployments.
A cost calculation considers data ops and system ops. In a SaaS the software vendor will run both, in a self-hosted/data cloud environment the client will typically run the data ops and the software vendor the system ops, and in a private cloud or on-premises, the client will run both. (See Figure 1)
Figure 1: Tailor the Redpoint CDP to your data and operational requirements
Composability and Customer Data Technology
In the CDP space vs. customer data technology at large, one trending topic is the composable CDP. A composable CDP aligns with a use-case approach by offering customers greater choice in terms of features and functions based on the specific needs of the business. Composability refers to designing a CDP with interchangeable and interoperable components, allowing for greater flexibility and adaptability. Composability entails a modular approach where buyers have the freedom of choice to obtain best-of-breed components that complement existing investments. More than just assembly, composability is also an enterprise approach that prioritizes agility.
One factor to consider whether a composable CDP is right for your business is how important it is to quickly reach untapped business value. A composable approach allows a business to be more nimble, easily pivoting to rapid business and consumer changes. A customized toolset also is more dialed in to help the business achieve specific business goals and unique processes. The full value of a composable CDP is reached when each component creates value as it applies to a business function.
The right customer data technology will be composable in the sense that each part can be used to create value independent of the other parts (See Figure 2). In this sense, customer data technology requires two essential pieces:
- Componentized software that can be used to create, improve and continually update customer profiles
- Organizations can create their own end-to-end data pipelines as required using customer data management software.
Figure 2: Each part of the Redpoint CDP creates value.
Composable customer data technology with a prime focus on data quality will ensure that all components of the composable stack will work better with the highest quality data and more accurate profiles used across the value chain. Embedded analytics and AI across every component help unlock deep insights from first-party customer data that produce data-driven personalization. Consider as an example a company that has a reverse ETL application that moves data from a data warehouse or data lake and activates it to customer-facing end channels. In some cases, this reverse ETL tool might even call itself a CDP. With a composable approach, this functionality may be augmented by adding an application that focuses on data management, addressing data quality, identity resolution and other data management tasks that a reverse ETL tool either ignores or downplays.
It is also important to note that there will be different users for different pieces of customer data technology, but to be effective the inputs and outputs need to be integrated in a way that streamlines workflow for any one function, and ultimately turns raw customer data into tangible business outcomes. (See Figure 3)
Figure 3: Different components and pieces of customer data technology will have different users. Integrated inputs and outputs will streamline the workflow for any one function, helping users move toward their intended business outcome.
Coming Next: The Importance of Data Quality in Customer Data
The growing complexity of customer data and the increasing demand for hyper-personalization make it clear that robust, enterprise-grade customer data technology is essential for delivering exceptional customer experiences. While we’ve explored the foundational capabilities and the importance of aligning them with business use cases, there’s one critical factor that underpins the success of all customer data: data quality. Without clean, accurate, and actionable data, even the most advanced technology will fall short of its potential.
In our next part of this series, we’ll delve deeper into the role of data quality in powering your use cases, exploring why it’s the cornerstone of creating unified customer profiles and driving meaningful personalization at scale.
