Maximizing the impact of customer analytics with composable CDPs

In recent years, customer data platforms (CDPs) have emerged as indispensable tools in the realm of the MarTech stack. A CDP serves as a centralized system for collecting, organizing, and analyzing customer data from diverse sources to create comprehensive customer profiles. These profiles are leveraged by various analytics, automation, CRM, and activation tools to enable personalized marketing activities, advanced data analytics, predictive capabilities, etc.

These platforms play a vital role in creating audience segments, aggregating customer data from multiple sources, and activating this data across multiple touchpoints throughout the buyer journey. Customer-driven marketing requires a CDP that integrates customer data with the full breadth of enterprise data, enabling advanced use cases and delivering superior customer experiences in a cost-efficient manner.

Building a data foundation for CDP-based customer analytics

A robust data foundation is imperative to ensure the success of CDP implementations and overcome challenges related to siloed data and privacy regulations. A scalable data foundation empowers enterprises to extract actionable insights from vast amounts of customer data, unlocking its true potential. The data foundation serves as the bedrock for modern CDPs, allowing enterprises to

Composable CDPs enable customer analytics with centralized data lakes

Composable CDPs enable more agile and effective customer analytics initiatives by focusing on the right data, integrating new data sources and tools as businesses evolve, and ensuring their data foundation remains future-proof and effective. It also connects data to activation channels, enabling the marketing/customer teams to take the right action based on customer insights.

Fig. 1. Logical CDP dataflow architecture of a composable CDP¹

Image adapted from AWS

Components of a composable CDP:

1. Data collection: Identify and ingest customer data from multiple sources and across mobile, web, analytics, and digital applications using identifiers such as emails or device IDs, as well as demographic or psychographic information. For first-party data, CDPs collect digital behavioral data (e.g., clicks, page views, etc.) in real-time. For third-party data, such as CRM, POS data, or streaming event data, products that specialize in ELT pipelines with built-in integrations across a wide range of SaaS platforms.

 

2. Identity resolution: Post data ingestion, the data needs to be cleaned and modeled correctly to minimize noise for subsequent processes. BI or data transformation tools enable data analysts to prepare tables within the data warehouses, facilitating sharing across multiple teams. A shared modeling layer simplifies the identification of each customer and resolution of duplicate entities.

 

3. Data storage: The storage and compute layer acts as the source of truth for the raw, cleansed, and curated customer data. In a composable CDP, data storage is provided by a cloud data warehouse with a customer 360, that is owned and managed by the user organization. Customer 360 stores and enables data enrichment to create a comprehensive and unified view of a customer.

 

4. Data activation: This is the stage where non-technical business teams (i.e., marketing, sales, service, support, etc.) access the stored and modeled customer data from a central repository and activate it to channels or applications used by internal teams.

 

There are many off-the-shelf CDPs available in the market to fit unique business needs such as Twilio Segment, Salesforce data cloud, SAP CDP, Adobe Realtime CDP, BlueConic, Hightouch, Action IQ, Amperity, Epsilon, etc. that can help manage and store all their data on a centralized platform and allow users to easily access data as per their usage. However, a trusted data and analytics partner can help choose the right tool or customize models to achieve various marketing goals.

CDPs are an extension of the data management strategy

Composable CDPs are not a stand-alone solution but rather a key component that integrates with your existing data infrastructure and workflows. The modular architecture approach of a composable CDP emphasizes the ability to build systems with independent, reusable data management and customer engagement components/ or services/tools. It is built to leverage the existing single source of truth in an organization’s data lakehouse/data cloud, minimize data replication, and easily put together all the customer data from multiple sources and touchpoints to create a comprehensive customer 360.

By aligning the CDP with your broader data management strategy, you can maximize the value of your customer data:

Conclusion

Customer-facing enterprises can gain breakthrough levels of customer insight from composable CDPs in a way that easily shares that insight across the enterprise, delivers granular reporting, and boosts marketing productivity. Succeeding with customer data is only possible with a robust data foundation supported by a CDP with modular architecture–allowing enterprises to choose the best tools according to specific goals, and business requirements while optimizing costs across a variety of use cases. The data foundation in the CDP empowers enterprise leaders by providing them with the right views to take correct action across the customer journey.

About the Author

Nandam is a Principal Architect at Sigmoid with more than a decade of experience as a full stack engineer and big data technologies from designing end-to-end data management solutions to delivering turnkey analytics projects for Fortune 500 companies. He is passionate about solving business problems for enterprises across CPG, Retail, QSR, and Financial services through his expertise using open-source and cloud technologies.