Deliver Personalized CX with Unbundle CDP- Part 1

Today customers interact with a brand across a wide array of devices and expect a personalized customer experience across every touchpoint. Every touchpoint is part of an end-to-end customer journey that must provide a positive and seamless experience that brings customer satisfaction. Customer satisfaction is the main goal for an organization that wants to increase revenue, thrive and stand out in the daily market battle. The current state of personalization with the below surveys:

• 70% of millennials are frustrated with brands sending irrelevant emails - SmartHQ
• 74% of customers feel frustrated when website content is not personalized - Instapage

From the surveys, we noticed that customers often receive poor personalization and irrelevant messages with low chances to experience an end-to-end customer journey. To deliver personalized customer experience, many organizations are investing in the Customer Data Platform (CDP) that unifies customers' data from multiple channels and enables companies to deliver personalized and contextualized messages to the customers. A successful personalized customer experience is the direct link to the following four points that can be achieved by CDP:

• Unify customer data and have a holistic view of customers
• Enhance data privacy and security by complying with data privacy regulations 
• Focus on the first Party data as brand/organization footprint as a key differentiator and cannot be imitated by competitors
• Activate data as the last mile to deliver personalized messages

CDP structure and state of CDP implementation

These four points introduced a typical CDP structure with four main layers data collection, data transformation, data store and modelling, and data activation.

CDPs have been around in the market with a continuous evolution that brings a huge variety of vendors. Now, how far is the evolution? Did these organizations which implemented CDP achieve their goals? Which perception did CDP build across different teams from data engineers to marketers? Did CDP bring value to the organization and what was the time to value? No doubt that organizations and vendors have made real progress throughout these years, but if we look at some research despite a vast array of vendors, we notice that organizations struggle to implement a CDP. In fact, since March 2015, Signal Group survey stated that "only 6% of marketers have a single view of the customer, despite 90% reporting it is a top priority" until the last Gartner survey from January 2022 where “only 14% of organization have achieved a Unified Profile, despite 82% who say it is still a top goal”.

Yet, another two interesting CDP Institute surveys where the first one stated that just “23% of consumer marketers have completed their projects on time and schedule. Only 58% of companies with a deployed CDP say it delivers significant value”. The second survey where said that unified profile and CDP can be implemented independently, "although 19% of all companies have them both, another 19% have a unified database without a CDP, and 13% have a CDP without a Unified Profile".

With the mentioned survey related to the Off-the-shelf CDP, it led to a question on whether we can improve our way of onboarding and implementing CDP in order to reach our CDP's goals and deliver a personalized experience. 

The rise of Unbundle CDP approach

The difficulty of having a unified customer database and delivering a significant time-to-value during the CDP implementation by using the traditional Off-the-shelf CDP approach brings the rise of a new approach as a strong alternative - Unbundle CDP.

Unbundle CDP helps organizations to harness their customer data with Modern Data Stack components in lego-like building blocks and leverage the power of a data cloud warehouse. By implementing a best-in-class product at each layer of the CDP (data collection, data transformation, data store and modelling, and data activation), an organization can solve problems beyond the common use cases of Off-the-shelf CDP. In addition, understanding each of these components will allow teams to make the most informed architecture decisions when implementing their own Unbundle CDP.

Why Unbundle CDP

Unbundle CDP brings significant improvement at each layer of CDP in terms of time-to-value, flexibility, easier of scaling up, easier of iterating with decoupling components, more manageable to improve performance, and better data governance. In this article, I will focus on the three main enemies of a personalized customer experience such as lack of single source of truth, lack or low usage of 1st party data, and poor data transformation process from raw data to final data ready to be used by marketers and how unbundle CDP can solve them.

a) Lack of single source of truth : most of the time organizations that want to implement a CDP with the Off-the-shelf CDP approach, have already invested money, and efforts in the data warehouse by centralizing data in one place. The onboarding of Off-the-shelf CDP in the martech stack will inevitably bring a duplication of data and no single source of truth that it is the main requirement to have a unified customer database and break the CDP promise to eliminate silos architecture

Building Unbundle CDP around the data warehouse with a data warehouse-first approach, allows an organization to have a single source of truth where different teams can rally around the same dataset, no wasting of effort to align two different systems and last but not least build trust on data across teams.

b) Lack or low usage of first party data: since successful personalization messages require first party data, these data are usually imported into the data warehouse using the ETL pipeline, which the Off-the-shelf CDP cannot import most of the time or face limitations for security reason. Unbundle CDP allows data engineers and marketers to fully leverage first-party data to deliver personalized messages.

c) Poor data transformation process: most of the time, data engineers struggle to hydrate BI or data activation tools dedicated to the marketers cause of the monolith architecture and tight data model of Off-the-shelf CDP, which cause these issues:

• Not accurate ID stitching and ID resolution
• Data engineer has to adapt the company business model that has to be adapted to the rigid Off-the-shelf CDP data model
• Often Off-the-shelf CDP data model does not support complex use case or require a considerable effort to deliver them.

These issues cause the marketer to struggle and activate data, have a quick time-to-market, and get valuable insights from data since that different work on different datasets and different business definitions.

Unbundle CDP allows data engineers to have a smooth data transformation process thanks to best in class products at each layer (data collection, data transformation, and data activation) with the possibility to replace easily a component with another one if the component does meet use cases or requirements.

Reverse ETL

Unbundle CDP introduces a new concept as Reverse ETL at the data activation layer. Reverse ETL is the process of activating and sending data from a data warehouse into business applications like CRM, social media, analytics, marketing automation, etc. There are two main excellent aspects of this layer. First, a marketer can create their audiences with a no-code approach by using a customized data model and sending it over to external systems without caring about the technical aspect. The second one, the chosen modern data stack components don't own any data and the marketer can activate and send the audience as soon as the data are available in the data warehouse.

How to implement an unbundle CDP

Based on use cases, organization martech architecture, skillsets, and organization culture. An organization can adopt a different trade-off during unbundled actions. Next, I will show three possible ways to build an unbundle CDP based on a different trade-off with a data warehouse-first approach:

• Unbundle CDP is built by using different modern data stack components, one for each layer.
• Unbundle CDP by leveraging the customer data pipeline at the data collection and data activation layer and a modern data stack component at the data transformation layer.
• Unbundle CDP built on Google Cloud Platform components.

Key takeaways

• CDP helps organizations to collect, manage and interact with customers with an omnichannel approach.
• Unified Profile has always been at the top of the list for every organization but only a few of them achieved that.
• Unified Profile is the foundation to send personalized messages.
• Focus on First-Party Data as a key differentiator in the market.
• There is a lot of hype around CDP and at the same time many possibilities to deliver low value and low satisfaction.
• Some Organizations achieved Unified profiles without CDP by using developing a paralleled platform to CDP. Unbundle CDP to prevent this case with the data warehouse first approach
• Off-the-shelf CDP does not resolve siloed architecture but can introduce a new silo.
• Marketer struggles to activate data and trust data with Off-the-shelf CDP
• Off-the-shelf CDP introduces another silo platform and duplicate effort and costs
• Could Data Warehouse and CDP implement separately with consequent results having data redundancy and no single source of truth?
• Follow the Warehouse-centric approach by building CDP using a best-in-class component that covers the Off-the-shelf CDP layer.
• Clear definition of the role, the marketer can focus on data activation with a no-code Reverse ETL tool without caring about technical aspects, while Data Engineer or Data Analyst focuses on hydrating Reverse ETL tool for marketers.
• Reverse ETL doesn’t own any data. Data are only moved into Cloud Data Warehouse from Data Collection and from Cloud Data Warehouse to Destinations
• Data Transformation allows Data Engineers to support complex use cases by implementing models in cloud data warehouses.
• Escape single vendor handcuffs