Accelerating Revenue with Customer Data and Automation: Emerging Technology Stacks
Amidst growing data volumes and an impending cookie-less world, Customer Data Platforms (CDPs) have emerged as the most comprehensive data solution for the end-to-end customer journey and a mission critical tool of the enterprise technology stack. However, when trying to build a customer data strategy, IT teams are inundated by a slew of tools and acronyms. What is a CDP? What are the different types of CDPs? What about my Data Management Platform (DMP) and Customer Relationship Management (CRM) tools? Where do cloud data warehouses like Snowflake and Databricks fit into all of this? To better monetize your customers through data and analytics, it is important to understand what CDPs actually are and the role these tools should play in your technology stack.
CRMs, DMPs, and the birth of CDP
To understand CDPs and the broader customer data tools landscape today, it is helpful to know its history. The first ancestors to CDP appeared when customer Rolodexes were digitized in the late 1970s as businesses began putting customer data into spreadsheets. These evolved into basic customer databases by the 1980s and 1990s, and by the turn of the millennium, moved into the cloud with Salesforce.
Simultaneously, the Internet brought about online advertising, which generated massive amounts of data for marketers to manage. This led to the aforementioned DMPs, a data warehouse designed to store and analyze third-party (cookie-based and anonymized) data. DMPs allow marketing teams to build highly granular customer segments. Companies like Snowflake and Databricks have since emerged as highly performant, flexible, and scalable evolutions of data warehousing.
In 2013, the CDP was first defined and developed with the purpose of synthesizing first-party customer data and combining it with the volumes of data generated by customers’ online activity. The CDP provided enterprises a holistic source of truth on their customers and thus an unmatched ability to optimize customer experience and targeted communications. DMPs, on the other hand, warehoused anonymized third-party customer data gathered from external sources. While CDPs also hosted third-party data, DMPs were better equipped to gather, analyze, and action data on potential customers who had yet to engage with the company. Where CDPs specialized on the existing customer, DMPs focused on new customers.
CDP 2.0 and the displacement of the DMP
Developments in machine learning technology and metadata management gave way to “CDP 2.0.” Concurrently, the deprecation of third-party cookies by Google and Apple have diminished the value of DMPs. While these tools were previously used in conjunction, going forward, we anticipate DMPs will be subsumed by CDPs.
To summarize: CDP 1.0 allowed enterprises to know who their customers are; CDP 2.0 shows us how customers behave. Many companies call themselves CDPs but are ultimately providing the original use case of CDPs without the valuable analytical customer journey offering.
Beyond just aggregating and managing a customer profile, true CDPs today can scale to collect and analyze detailed, event-level behavioral data – both known and anonymous – and thereby map the customer journey that informs how, when, and where to target customers for optimal engagement.
The tremendous value of event-level behavioral information lies in its ability to answer questions like: what are the most recent emails a customer opened, the last five SKUs they bought online, the last three calls they had with the call center? Advancements in data management and computing capabilities have given CDPs the user journeying superpower to create robust predictive persona strategies.
Concurrently, the “cookie apocalypse” continues to evolve, as Google announced their decision to remove cookies from its Chrome browser by 2024. Third-party cookies are the lifeblood of the DMP.
Enterprises will now have to make a decision – struggle to manage rising acquisition costs, or focus on driving customer lifetime value. The answer is clear. Understanding the customer, how they engage with the brand, and the journey they take from acquisition to retention will define success going forward. As marketing resources are increasingly deployed to drive lifetime value of customers (LTV), the CDP has emerged as a mission critical business tool for the enterprise at the expense of the DMP.
CDP of the future
The future of CDPs is Composable Architecture. Composable Architecture, as opposed to bundled CDPs, allows the enterprise to decide where their customer data lives. This has incredible benefits for data control, flexibility, and cost.
The norm historically has been Bundled CDP architecture, requiring teams to purchase not only the CDP’s analytical and visualization layers, but also the data storage layer. This requires data duplication, thus increasing cost and complexity and creating friction with IT teams who usually drive for a single data store for better efficiency and governance.
For instance, one of the companies I’m on the board of, ActionIQ, has made impressive headways here with its leading HybridCompute technology. HybridCompute allows clients to run ActionIQ on their existing data warehouses, data lakes, or data lakehouses, without creating a parallel data platform. This has proven to be a strong selling point for CIOs as it drives performance, efficiency, and data security.
As the ecosystem of customer data management begins to mature, what originally sounded like alphabet soup is becoming more clear. CDP 2.0s, including Composable CDPs like ActionIQ, are emerging as the solution that can finally capture the full end-to-end customer journey in unparalleled detail – and drive higher sales, higher NPS scores and higher LTV.