Case Studies

Case Study 1: Smarter Attribution Modeling for a Retail Brand

A global eCommerce retailer struggled to understand which marketing channels truly drove conversions. Their existing reports relied on last-click attribution, leading to underinvestment in top-of-funnel campaigns.

Solution: Using BigQuery, we unified GA4, Google Ads, and Meta Ads data into a single warehouse and built a data-driven attribution model.

Impact: The retailer discovered that display and paid social campaigns generated 40% more conversions than previously measured, resulting in optimized budget allocation and a 20% increase in ROAS.

A subscription-based SaaS company wanted to identify high-value customers early but lacked the ability to forecast LTV beyond the first month.

Solution: We leveraged BigQuery ML to create predictive LTV models, combining transaction data, engagement metrics, and marketing touchpoints.

Impact: This helped the company segment new signups by predicted value, allowing marketing teams to invest more in retaining high-LTV cohorts. As a result, churn dropped by 15%, and customer retention revenue increased by $1.2M annually.

Case Study 2: Predictive Lifetime Value (LTV) Modeling for a Subscription Business

Case Study 3: Cross-Channel Marketing Analysis for a D2C Brand

A direct-to-consumer (D2C) beauty brand struggled with siloed data across GA4, Facebook Ads, and CRM systems, making it difficult to measure campaign effectiveness.

Solution: We integrated all marketing and CRM data into BigQuery, creating real-time dashboards that tracked CAC, ROAS, and cohort performance across channels.

Impact: The brand discovered underperforming campaigns, reallocated spend, and optimized creatives, resulting in a 30% reduction in CAC and a 25% boost in overall revenue within 3 months.

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