B2C and D2C eCommerce retailers are turning to Customer Data Platforms (CDPs) to transform how they acquire, engage, and retain customers. Their goals typically fall into three key areas:
1. Customer Acquisition & Upsell
Retailers want to build a unified, real-time customer profile by integrating data from both onsite and offsite channels. These profiles power seamless omnichannel marketing, personalized product recommendations, and tailored onsite experiences.
2. Journey Optimization
By leveraging rich first-party customer data, retailers aim to better understand the end-to-end customer journey. These insights help optimize touchpoints and enhance the overall onsite experience.
3. Machine Learning & Activation
Advanced segmentation powered by machine learning allows retailers to identify patterns linked to high-value behaviors. These segments fuel smarter A/B testing and help uncover high-performing, context-specific strategies.
Why Google Cloud BigQuery?
Let’s put cloud migration strategy aside for a moment. Here are the two biggest reasons why Google Cloud BigQuery (BQ) stands out as the foundation for modern CDPs:
- It’s fast, scalable, and cost-effective – BigQuery is one of the most powerful and user-friendly cloud data warehouse solutions on the market.
- It plays natively with Google Ads – BigQuery integrates directly with Google’s ad ecosystem (Search, Display, YouTube, etc.), making it ideal for unifying omnichannel customer data in one place.
Turning BigQuery Into a CDP Engine with Syntasa
Syntasa is an add-on solution available via the Google Cloud Marketplace that transforms your existing BigQuery environment into a fully functional CDP — giving you maximum control without overhauling your infrastructure.
Here’s how it works, in five core steps:
1. Data Ingestion
Digital data from tools like web analytics, email systems, ecommerce platforms, CRMs, and more can land in GCP Cloud Storage with minimal effort.
Syntasa’s prebuilt connectors streamline ingestion into BigQuery using a structured data model. Built-in features handle data cleansing, missing file alerts, data lineage tracing, quality reports, and complex job orchestration.
2. ID Unification
Syntasa’s ID Graph sits on top of BigQuery to unify customer identities using first-party data: cookies, consent preferences, encrypted emails, phone numbers, order history, and device IDs. The unified profiles are stored in both BigQuery and Bigtable, ready for downstream analysis without any custom plumbing.
3. Segmentation
With BigQuery’s native integration to AutoML and TensorFlow, retailers can build sophisticated ML models.
Syntasa simplifies this further with a no-code/low-code UI and prebuilt model templates — like churn propensity, product affinity, and high-value customer identification — accelerating time-to-insight and reducing manual effort.
4. Journey Analytics
While Google Cloud supports general-purpose dashboarding, Syntasa offers a purpose-built Customer Journey Dashboard.
This interface taps into your BigQuery dataset and Syntasa’s ID Graph to surface actionable insights on customer paths and friction points.
5. Activation
Syntasa turns BigQuery into the central hub for campaign orchestration. Drag-and-drop connectors to major Martech, onsite personalization, ecommerce, and enterprise platforms allow for direct activation. Syntasa handles pipeline management, data lineage, and connector maintenance.
The BigQuery Data Model for CDPs
Figure: a simplified visualisation of the most common digital IDs across digital sales and marketing channels, and offsite systems for transactions, CRM, and customer care
Traditionally, the Customer Data management challenge in the digital world was primarily about mapping web analytics with the AdTech eco-system so that individuals could be tracked and targeted across the Internet.
With the tightening of regulations and ethics around cross-site tracking and targeting, the focus of the Customer Data management needed to shift more towards curating an enterprise-grade first-party identity mapping across online and offline datasets.
ID mapping is just the beginning. These IDs evolve over time, forming complex time-series data. Effective CDPs must respect that context — because ignoring the time dimension leads to poor match rates and irrelevant insights.
FAQs
1. Can I really build a CDP using just BigQuery and Syntasa?
Yes. BigQuery acts as the core data warehouse, while Syntasa adds the necessary CDP capabilities — identity resolution, segmentation, journey analytics, and activation — all without requiring additional infrastructure.
2. How is this different from buying a traditional CDP?
Traditional CDPs are often black boxes with rigid pricing, limited customization, and long onboarding times. Syntasa on BigQuery gives you full transparency, flexibility, and control — all within your existing cloud environment.
3. What kinds of data can Syntasa ingest into BigQuery?
Syntasa supports ingestion from a wide variety of sources: web analytics platforms, marketing clouds, CRMs, ecommerce systems, email tools, offline transaction databases, and more.
4. Does Syntasa require a data science team to use?
Not necessarily. While it integrates with tools like TensorFlow and AutoML for advanced modeling, Syntasa also provides low-code and no-code tools for segmentation and activation, making it accessible to marketing and analytics teams alike.
5. How long does it take to get up and running?
Implementation time depends on your data complexity, but most teams can start ingesting data and building unified customer profiles in just a few weeks.
Elevate Your Retail Strategy
Experience firsthand how Syntasa can revolutionize your customer data management. Book a demo today and transform your BigQuery environment into a powerful, retailer-centric CDP.