Introduction
For many brands, unlocking the full potential of customer data remains a challenge. Without a unified view across commerce, marketing, and behavioral data, companies struggle to generate insights, optimize spend, and drive meaningful engagement.
A national retailer faced this exact issue. Despite having access to robust data sources—including transactional, digital engagement, and marketing performance data—their ability to connect and activate insights was limited by siloed systems and a fragmented data architecture.
To solve this, the company partnered with Actable to develop a centralized customer intelligence framework in BigQuery, enabling seamless data integration, improved analytics, and a foundation for AI-driven marketing strategies.
The Challenge: Disconnected Data, Inefficient Insights
Like many retailers, this company had invested in a commerce-driven CDP to unify customer data, but system limitations made integration with their broader Martech stack difficult. Their core challenges included:
Siloed Data Across Platforms
Customer interactions, transactions, and marketing data were stored across different platforms, making it nearly impossible to create a cohesive customer intelligence layer for analytics and personalization. Without a unified view, marketing and analytics teams operated with fragmented insights, limited targeting capabilities, and inefficient campaign measurement.
Operational Complexity and Cost
BigQuery had been implemented as a data warehouse, but it wasn’t being used efficiently. Poorly structured data flows and a lack of clear governance led to high processing costs without delivering meaningful insights or activation opportunities.
Lack of Advanced Analytics Capabilities
Despite a wealth of customer data, the company had no structured way to apply predictive modeling or AI-driven insights. This meant that high-value use cases like purchase propensity modeling, churn prediction, and dynamic product recommendations were out of reach.
Without an integrated, scalable approach, the company was operating with an expensive data infrastructure that wasn’t generating business value.
The Solution: A Unified Customer Intelligence Layer in BigQuery
Actable deployed a BigQuery-powered customer intelligence framework, leveraging Google Cloud’s Marketing Analytics Jumpstart (MAJ+) to enhance data integration, streamline architecture, and create a more efficient analytics foundation. The solution included:
Unifying Commerce and Behavioral Data
- Integrated CDP, GA, and transactional data into a structured customer 360 dataset, breaking down data silos and providing a real-time view of customer interactions.
- Built an actionable intelligence layer to support advanced analytics and marketing activation.
Optimizing Data Efficiency and Cost
- Designed a low-cost, scalable data unification pattern that reduced processing overhead while improving data accessibility.
- Delivered a transparent reference architecture to ensure ongoing, cost-efficient use of BigQuery.
Laying the Foundation for AI and Predictive Analytics
- Developed a roadmap for machine learning adoption, enabling high-value AI-driven use cases such as:
- Purchase propensity modeling to predict buying behavior.
- Churn prediction strategies to improve retention efforts.
- Dynamic product recommendation engines to drive personalized engagement.
By implementing a scalable, future-proof data model, the company transformed their BigQuery investment from a static storage system into a powerful intelligence engine that fuels marketing and business strategy.
The Results: Smarter Data, Stronger Activation
With a structured and optimized BigQuery framework in place, the company achieved:
- A single, unified view of customer data, enabling deeper insights into marketing performance and customer behavior.
- Cost-efficient data architecture, reducing unnecessary processing costs while improving flexibility for future expansion.
- A clear roadmap for AI-driven use cases, allowing the company to activate customer intelligence through predictive modeling and advanced analytics.
By shifting from a fragmented data environment to an integrated intelligence layer, the company is now positioned to scale its data-driven marketing strategies with efficiency and precision.
Why This Matters
For retailers managing complex data ecosystems, a truly effective customer intelligence strategy requires:
- Seamless integration of commerce, marketing, and behavioral data.
- Optimized infrastructure that ensures cost-effective data processing.
- A foundation for machine learning and AI-driven activation.
By leveraging BigQuery as the central hub for customer intelligence, this company is no longer just collecting data—it’s using it to drive real business outcomes.
Conclusion: From Data Storage to Actionable Intelligence
This case demonstrates the power of a structured data strategy in unlocking real business value. By moving beyond fragmented systems and implementing a unified customer intelligence layer, the company can now:
- Generate deeper, more actionable insights.
- Reduce costs while improving data accessibility.
- Deploy predictive AI models for more personalized customer engagement.
With a future-ready data framework, the company is no longer just storing information—it’s activating it.
Is your organization ready to transform its customer data strategy? Let’s talk.
