The data collection layer is the foundation of a Customer Data Platform. This is where all customer data flows in from every touchpoint across your ecosystem like websites, mobile apps, CRMs, email tools, ads, transactions, and even offline systems. The goal of this layer is to bring all customer signals into one unified place, eliminating data silos and ensuring a single source of truth.
A CDP typically supports 3 modes of data collection:
- Real-time Data Collection : In this mode, data is captured instantly as customers interact with your channels. Examples include website page views, mobile app events, and product interactions. Real-time data powers use cases like cart abandonment alerts, browsing intent detection, and in-session personalization. This is usually achieved through SDKs (for web and app tracking) or streaming APIs.
- Batch Data Collection: Here, data is sent to the CDP at scheduled intervals, ie every few minutes, hourly, or daily. Examples include CRM exports, transaction logs, loyalty data, or email engagement reports. Batch collection works best for data enrichment, historical analysis, and large volume transfers. It typically relies on methods like SFTP uploads, cloud storage buckets (S3, Azure, GCP), or scheduled API calls.
- On-Demand or Pull-Based Data Collection: In this approach, data is fetched from another system only when required. For example, pulling recent CRM attributes, syncing updated customer statuses, or fetching product catalog details on demand. This method keeps customer profiles current without the overhead of continuous data ingestion.
The strength of a CDP’s intake system lies in its ability to flexibly blend these 3 modes, delivering both speed and depth in customer understanding
