Google Cloud
Customer Data Platform on Google Cloud
We build composable customer data platforms on BigQuery that unify every customer touchpoint into governed profiles, predictive audiences and real-time activation.
Know Every Customer, Act on Every Signal
Customer data is usually scattered across web analytics, mobile apps, e-commerce, CRM, support desks, loyalty programs and advertising platforms. Each holds a partial view, and marketing teams end up exporting spreadsheets to build audiences. A customer data platform on Google Cloud keeps all of it in BigQuery, which you already control, and turns it into unified profiles that analytics, marketing and product teams can use safely. Because the data stays in your own cloud project, you decide retention, consent rules and who can access what.
Hanoi Technology builds composable CDPs rather than forcing data into a closed SaaS tool. We ingest first-party data with native connectors such as the GA4 BigQuery export and the BigQuery Data Transfer Service for Google Ads, resolve identities, model profiles and events in Dataform, train propensity models with BigQuery ML or Gemini Enterprise Agent Platform, and activate audiences back to advertising, email and in-app channels. The result is a platform your team owns and can extend.
The Challenge
Why Customer Data Projects Stall
The obstacles below are common whether you start with a packaged CDP or a warehouse.
No Reliable Customer Identity
The same person appears as an anonymous cookie, an app install, an email subscriber and a loyalty member. Without deterministic and rules-based identity resolution, profiles are duplicated and segment counts are inflated. Personalization then targets the same customer several times or misses them entirely.
Consent and Privacy Complexity
Regulations such as GDPR and CCPA, plus browser and platform changes, require consent to travel with the data. Many teams cannot prove which records may be used for which purpose. That uncertainty stops legal approval of new campaigns.
Audiences Built by Hand
Marketers depend on analysts to write SQL for every new segment. Requests queue up, campaigns launch late and segments drift from one channel to another. Business users need governed self-service, not another ticket.
Insight Without Activation
Many warehouses hold excellent customer data that never reaches ad platforms, email tools or the product. Without reverse ETL or direct integrations, models remain slide-deck insights. Value only appears when the next interaction changes.
Business Value
Benefits of a Composable CDP on Google Cloud
You Own the Data and the Logic
Profiles, identity rules and models live in your BigQuery project, so there is no second copy in a vendor’s cloud and no lock-in to one tool.
Native Google Marketing Integrations
GA4 export, Google Ads, Campaign Manager 360 and Search Ads 360 data can be landed in BigQuery, and audiences can flow back for activation.
Predictive Audiences
BigQuery ML and Gemini Enterprise Agent Platform make churn, propensity and lifetime value scores part of every profile.
Privacy by Design
Policy tags, row-level security, data masking and consent attributes keep sensitive fields protected and usage auditable.
Real-Time Where It Matters
Pub/Sub and Dataflow stream events into profiles so triggers such as cart abandonment fire within minutes.
Shared Truth for Every Team
Marketing, product, service and finance work from the same customer definitions, visualized in Looker.
A CDP Is a Capability, Not a License
Packaged CDPs solve part of the problem but often duplicate data you already have in a warehouse. A warehouse-native approach treats the CDP as a set of models, pipelines and activation paths on BigQuery, which is easier to govern, cheaper to scale and fully extensible.
Our Services
Hanoi Technology’s End-to-End Customer Data Platform on Google Cloud Services
Services that take you from scattered data to activated audiences.
CDP Strategy and Use-Case Design
We map your customer journeys, data sources and priority use cases, then decide what should be warehouse-native and where a packaged tool adds value. The output is a roadmap linked to measurable marketing and service outcomes.
- Use-case backlog and KPIs
- Source and consent inventory
- Build-versus-buy recommendation
Data Ingestion and Modeling
We land web, app, CRM, commerce, support and advertising data in BigQuery and model it in Dataform into clean event, customer and transaction tables. Pipelines are tested and monitored.
- Ingestion pipelines
- Customer and event data model
- Data quality checks
Identity Resolution and Profiles
We implement deterministic matching on hashed emails, phone numbers and login IDs plus configurable rules for household and device linking. Each profile keeps a full history of how it was stitched.
- Identity graph
- Golden customer profile
- Match-rate reporting
Segmentation and Predictive Models
We build self-service segments in Looker and train churn, propensity, next-best-offer and lifetime value models with BigQuery ML or Gemini Enterprise Agent Platform.
- Segment library
- Predictive scores in profiles
- Model monitoring
Activation and Personalization
We push audiences to Google Ads, Meta, email and messaging platforms, and expose profile APIs for personalization in websites and apps.
- Audience sync jobs
- Profile API
- Campaign measurement design
Privacy, Consent and Governance
We carry consent attributes through every model, apply policy tags and masking, configure BigQuery data clean rooms for partner collaboration where needed, and document lineage.
- Consent model
- Access policies
- Data lineage documentation
Our Approach
How We Build Your CDP
Start with one high-value use case, then expand on the same foundation.
- 01
Prioritize Use Cases
We select one or two use cases, such as churn prevention or cart recovery, with clear success metrics and available data.
Deliverables: Use-case charter · Success metrics
- 02
Land and Model Data
We connect the sources those use cases need, model them in BigQuery and set data quality checks.
Deliverables: Source pipelines · Customer data model
- 03
Resolve Identity
We build the identity graph and golden profile, and review match rates and edge cases with your team.
Deliverables: Identity rules · Profile tables
- 04
Activate and Measure
We create segments and scores, sync them to channels, and design holdout tests to measure impact.
Deliverables: Live audiences · Measurement framework
- 05
Scale and Operate
We add sources and use cases on the same foundation and run the platform with monitoring and monthly reviews.
Deliverables: Expansion roadmap · Operating runbook
Use Cases
Customer Data Use Cases
Scenarios our CDP builds are designed to support.
Churn Prevention
A subscription business scores every customer weekly for churn risk and sends high-risk segments into a retention journey with tailored offers, measured against a holdout group.
Cart and Browse Abandonment
Streaming events trigger reminders within minutes when a known customer leaves items in a cart, with product recommendations drawn from their profile.
Lookalike and Suppression Audiences
High-value customer lists feed lookalike targeting in ad platforms, while recent purchasers are suppressed to stop wasted spend.
Unified Service View
Contact center agents see purchase history, open tickets and loyalty status in one panel, shortening calls and improving resolution.
Loyalty Program Analytics
A retailer combines loyalty, point-of-sale and e-commerce data to understand cross-channel behavior and redesign reward tiers.
Partner Data Collaboration
A brand and a retail partner measure campaign overlap in a BigQuery data clean room without exchanging raw customer records.
Technology
CDP Technology Stack
Google Cloud services and common integrations in our customer data platforms.
- BigQuery
- Dataform
- Pub/Sub
- Dataflow
- Cloud Composer
- BigQuery ML
- Gemini Enterprise Agent Platform
- Looker
- Google Analytics 4
- BigQuery Data Transfer Service
- Google Ads
- BigQuery data clean rooms
- Knowledge Catalog
- Cloud Run
- Sensitive Data Protection
Platforms
Deliver Customer Data Platform on Google Cloud on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · E-commerce and retail
Personalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Read the Full StoryMore Success Stories
Fintech
+162%monthly active usersAll-in-one digital banking app for a traditional bank
A traditional financial institution wanted to compete with neobanks for mobile-first customers. We replaced a set of separate banking, investing and payment apps with one product that people use every day.
Real estate
+38%lead conversion rateProperty discovery and lead conversion for a US real estate marketplace
A US marketplace connecting buyers, sellers and agents had plenty of traffic but few enquiries. We built personalized property discovery, AI valuation and a one-tap route to contact an agent.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
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Turn Customer Data into Revenue
Tell us which customer outcome matters most and we will show how a BigQuery-based CDP can deliver it. Book a free consultation today.
Customer Data Platform on Google Cloud FAQ
Should we build a composable CDP or buy a packaged one?
It depends on your team, data maturity and use cases. If your customer data already lands in BigQuery and you have engineering capacity, a composable approach avoids duplicating data and gives full control. If you need marketer-friendly journey tools quickly with limited engineering support, a packaged CDP may fit, and it can still read from BigQuery. We assess both options honestly during discovery and often recommend a hybrid in which BigQuery remains the source of truth.
How long until the first use case is live?
For a single use case with accessible data, such as churn scoring or abandoned cart triggers, the first version typically goes live in eight to twelve weeks. That includes ingestion, modeling, identity resolution, activation and a measurement design. Additional use cases are faster because they reuse the same profile and pipelines, so later phases often take weeks rather than months.
How do you handle consent and privacy regulations?
We record consent status and purpose with each identifier, propagate it through every model, and filter activation jobs by permitted purpose. Personal data is protected with policy tags, column masking and row-level access, and we hash identifiers before sending them to advertising platforms. Retention rules and deletion requests are implemented as automated jobs. We work with your legal team to map rules to regulations such as GDPR or CCPA.
What data sources can you bring in?
Common sources include GA4 and Firebase exports, Google Ads and other advertising platforms, Salesforce, HubSpot or Dynamics 365 CRM, Shopify or custom e-commerce databases, support tools such as Zendesk, and point-of-sale or loyalty systems. We use native transfers where available, managed connectors for SaaS tools and custom pipelines on Cloud Run or Dataflow for in-house systems.
What drives the cost of a Google Cloud CDP?
Build cost depends on the number of sources, identity complexity, real-time requirements and activation destinations. Running costs are mainly BigQuery storage and compute, streaming services if used, and any connector licenses. We design partitioned, incremental models and right-sized schedules so costs stay predictable, and we share a monthly cost view by pipeline so there are no surprises.
Can our marketing team use it without writing SQL?
Yes. We expose curated segment-building Explores in Looker, document every attribute in plain language, and give marketers templates for common audiences. Advanced segments and model changes remain with the data team, but routine audience creation becomes self-service. Training and office hours are part of the rollout so adoption does not stall after launch.