55+
years since inception
460+
employees
222 M+
In revenue
A US membership buying club with hundreds and thousands of paying members was running its marketing, membership, and merchandising operations on fragmented data systems with no shared view across departments. Leadership had no reliable way to measure performance, track member churn, or quantify the impact of promotional decisions. Inferenz built a centralised data management platform integrating all source systems into a single SQL Server warehouse, delivered cross-functional analytics via SSRS and SSAS, and leveraged its Data Science & Predictive Analytics Services to build predictive models in R that surfaced churn signals and marketing spend efficiency. The result was a 15% reduction in member churn, 10% improvement in merchandising performance, and a unified decision environment where every team works from the same numbers.
Marketing, membership, and merchandising each operated on their own data systems with no shared model and no single view of how the business was performing. Decisions were made slowly, on historical data, with no analytical layer connecting the dots across departments.
Each business function — marketing, membership, and merchandising — maintained its own data in isolation. There was no centralised warehouse, no common definitions, and no way to produce an enterprise view of performance without manually pulling and reconciling data from multiple disconnected sources.
Leadership had no unified visibility into how marketing spend was performing, which membership segments were at risk of churning, or how merchandising decisions were affecting margin. Reports existed within each department but could not be compared or consolidated into a single leadership view.
Strategic decisions on renewals, lead generation, and promotional investment were driven by historical reports rather than current signals. Without a real-time or near-real-time data layer, the business was consistently acting on information that was already out of date by the time it reached decision-makers.
The business had no model to identify members at risk of non-renewal before they lapsed, and no analytical framework to measure whether marketing spend was generating returns. Every retention and acquisition campaign was launched without a quantitative basis for targeting or expected outcome.
Inferenz built a centralized data management platform integrating marketing, membership, and merchandising data into a single SQL Server warehouse, creating a common foundation from which every department could access consistent, reliable numbers for the first time.
The architecture definition phase mapped all source systems: ERP, CRM, and internal APIs, into a unified data model designed to support a single customer view across the entire member base. SSIS pipelines automated data flows from every source, replacing manual extracts and eliminating the reconciliation overhead that had previously consumed analyst time before every review.
A business intelligence layer was built on SSRS and SSAS, delivering cross-functional visibility into purchase orders, sales, spend, and membership performance, consolidating insights that had previously lived in separate departmental reports into one governed reporting environment accessible to leadership and operations teams simultaneously.
Two data science models were built in R to address the two most critical analytical gaps in the business. A marketing spend analysis model measured the return on promotional investment by channel, membership segment, and campaign type, giving the marketing team an evidence base for budget allocation that had not previously existed. A membership renewal prediction model identified members at statistical risk of non-renewal before their subscription lapsed, enabling the retention team to intervene proactively rather than reactively.
A churn prediction model completed the data science layer, identifying behavioural signals: purchase frequency, category engagement, recency of interactionthat were statistically associated with membership cancellation. Members flagged by the model could be targeted with retention outreach before the decision to leave was made, shifting the organisation’s retention strategy from reactive to predictive for the first time.
Reduction in member churn
Predictive renewal and churn models enabled proactive retention outreach, reducing membership cancellations through data-driven targeting before members lapsed.
Improvement in merchandising performance
Optimised merchandising decisions informed by unified purchase, sales, and margin data delivered measurable uplift in merchandising outcomes.
Decision environment
Marketing, membership, and merchandising teams now operate from one governed data platform with consistent cross-functional visibility — no reconciliation, no conflicting numbers.
Across every member
One SQL Server warehouse integrating ERP, CRM, and API sources gives leadership a complete picture of member behaviour, spend, and engagement across the full base.
Whether you’re starting with data modernization or exploring AI copilots, we’re here to help.
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