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Retail and eCommerce organizations operate in highly dynamic markets where understanding evolving customer segments is critical for sustained growth. This case highlights how teams, facing shifting purchase patterns and regional variations, used Scoop’s end-to-end AI automation to diagnose retention strengths, demographic shifts, and acquisition patterns in real time. With demographic fragmentation and legacy BI holding teams back, Scoop’s advanced pipeline delivered structured insights that would typically require extensive manual analysis—empowering leaders to respond proactively to changing customer behaviors.
Scoop’s AI-driven pipeline delivered targeted, prioritized insights that drove immediate changes in segmentation and marketing strategy, well beyond what was possible through legacy dashboards. The analysis revealed that while overall retention and product-market fit remained strong, there were urgent needs to address demographic imbalances and a downward trend in transaction volumes. Critically, automated pattern detection uncovered evolving gender dynamics post-2017 and distinct regional preferences, arming the business with specific, data-driven recommendations.
The customer base is predominantly female, highlighting a key market strength but also a potential opportunity for more balanced targeting.
Year-over-year transaction volume dropped 26% from 2015 to 2017, flagging emerging retention and/or demand challenges.
Year-over-year transaction volume dropped 26% from 2015 to 2017, flagging emerging retention and/or demand challenges.
80% of customers are between 25 and 44 years old, pinpointing a concentrated age segment driving business value.
The United States generated 480 transactions—more than Europe combined—demonstrating higher purchasing activity or frequency and serving as a benchmark for international strategy.
Retail and eCommerce brands must continuously refine their understanding of customer segments as preferences and behaviors shift across markets and channels. Traditionally, teams rely on fragmented BI tools that struggle to synthesize demographic, geographic, and behavioral data in a unified way, making it challenging to identify subtle shifts in customer engagement, retention, or acquisition trends. In this scenario, the business was confronted with a consistently female-skewed customer base (76% female), strong retention (57% multi-purchase), but a concerning 26% decline in transaction volume from 2015 to 2017. Regional disparities further complicated strategy: the US generated the highest transaction volume and more repeat purchases, while Europe showed signs of younger customer engagement, and Great Britain maintained an unusually stable demographic profile. The team needed to quickly make sense of complex, cross-cutting relationships—especially shifts in gender distribution, age targeting, and regional effectiveness—that traditional dashboards and manual queries could not untangle.
Dataset Scanning & Metadata Inference: Scoop automatically profiled all variables, inferred data types, and detected the presence of repeated customer IDs. This rapid structural understanding enabled downstream automation and highlighted retention rates not visible from transaction totals alone.
Scoop’s agentic ML models went far beyond surface-level demographic breakdowns to uncover nuanced, non-linear trends frequently missed in dashboards. For example, gender distribution was not static: in Great Britain, near-total female dominance (~93%) persisted across all segments, but in the US and France, gender balance shifted sharply by age and transaction year—especially beginning in 2017, where previously female-dominated segments transitioned towards male in specific age brackets. This reflected changes in targeting or acquisition channels unseen in traditional BI. ML-driven rules further clarified that customer ID ranges (a proxy for acquisition timing) tightly correlated with age group and transaction year, exposing up-market or youth-focused campaigns rolling out in phases and by geography. Patterns such as young customers in France shifting from 35-44 to under 25 between 2016 and 2017, and the clustering of older customers (55+) exclusively in Great Britain, provided granular guidance for region-tailored offers. These findings, grounded in ML explainability, could not have been surfaced through manual pivoting or static reports alone—demonstrating how agentic pipelines unlock layers of understanding previously out of reach without data scientists.
Armed with these insights, the business refined its segmentation and campaign playbooks region by region. Marketing teams rapidly developed targeted messaging for key growth segments—such as under-25s in France post-2017 and 25-34 year olds in the US. The leadership team prioritized diversifying acquisition channels to address the gender skew, running pilots aimed at underrepresented male customers in markets where recent trends showed potential. Plans are underway to closely monitor year-over-year transaction trends in upcoming cycles and continuously refresh the data pipeline, leveraging Scoop’s automation for rapid, scalable iteration. Next steps include A/B testing campaign variants by age and gender and expanding successful US retention strategies to European markets.