A fast growing retail brand was scaling acquisition across digital and partner channels, but customer data was fragmented, profitability was unclear, and personalization stopped at the channel edge.
We built the foundation, a composable customer data platform and layered CLV intelligence on top, so every marketing dollar was spent against a single view of the customer and their predicted lifetime profit.
Customer data lived in silos across web, marketplace, CRM, and marketing platforms. The same customer appeared as multiple identities. Marketing optimized on CAC, not lifetime value, and campaigns were channel-specific, unable to leverage a holistic view.
The result: inefficient spend, slow decisions, and no way to answer the questions that mattered, which customers are truly profitable, and which channels bring more of them.
We designed and deployed a composable Customer Data Platform on the client's existing cloud stack.
Scalable pipelines unifying transactional, web, marketplace, and marketing data into one Delta Lake.
Deterministic and probabilistic matching to stitch fragmented records into a single customer.
One unified profile per user, stored in a graph based structure with lineage for compliance as well.
Real time segmentation on attributes, RFM, engagement, and behavior, self serve for marketing.
Sync ready audiences pushed to ad platforms, CRM, and automation tools without engineering handoffs.
With a single source of truth in place, we built a Customer Lifetime Value model combining transaction history, revenue components, and churn signals, turning the CDP from a reporting layer into a decision layer.
Forecast the future profitability of each customer from their earliest behavior patterns.
Aggregate CLV by acquisition channel to reveal true "predicted profit per customer" by source.
Surface the cohorts that drive long term margin, not just the ones that convert first.
Flag positive CLV customers at risk of churn as priority targets for reactivation.
The CDP's activation layer piped CLV, ranked audiences directly into the tools the marketing team already used, turning a prediction into an ad set, a budget shift, or a reactivation flow.
| Application area | What we activated |
|---|---|
| Google & Meta | High CLV cohorts exported as seed audiences for lookalike modeling on both ad platforms. |
| Audience targeting | CLV-based segments replaced generic first-party lists across paid social and search. |
| Budget allocation | Spend reallocated toward channels with early profitable cohorts (<9 months payback). |
| Retention | Positive CLV customers predicted to churn were prioritized for reactivation campaigns. |
| Marketing automation | Non technical teams self-served CLV tiered lists into CRM and lifecycle tooling. |
With unified data as the base and CLV as the lens, marketing spend was measured against future profit, not just first quarter revenue.
The composable CDP gave the client one trustworthy view of the customer. CLV intelligence turned that view into a decision engine, telling marketing not just who the customer is, but which ones are worth acquiring, keeping, and winning back.
The result is smarter spending, stronger customer relationships, and a scalable model for long term financial sustainability, owned by the client, extensible over time.