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For subscription-based platforms, understanding where merchants cluster across pricing tiers is vital for growth and retention. This case study reveals how Scoop’s AI-driven workflow transforms a single-day shop-plan dataset into actionable business intelligence. By spotlighting plan adoption patterns and surfacing underperformance in high-value tiers, Scoop empowers leaders to design targeted conversion or migration strategies. The challenges and insights illuminated here demonstrate why rigorous, automated data analysis is now essential for any digital commerce or SaaS provider aiming to optimize product portfolios and capture new revenue opportunities.
Through Scoop’s end-to-end automation, the product team quickly confirmed that plan adoption is exceptionally top-heavy: just 25% of plans absorbed the majority of shop volume, while entry-level and premium offerings received limited traction. Key findings illuminated legacy plans as a sizable but vulnerable segment, and flagged significant gaps between the most and least popular categories. ML diagnostics revealed that current plan attributes alone do not explain shop distribution or size, pointing to the imperative for richer data collection if predictive modeling is to guide future packaging or pricing decisions. These insights anchored immediate actions to refine tier strategies and develop targeted migration campaigns.
Represents the full merchant footprint analyzed, spanning all active plans in the examined period.
Shops still on legacy plans signify over 18% of the platform—an upsell and migration opportunity for modernization efforts.
Shops still on legacy plans signify over 18% of the platform—an upsell and migration opportunity for modernization efforts.
The least adopted plan, LEGACY_ADVANCE, illustrates stark disparities in portfolio engagement and signals where consolidation may be required.
Only three plans cross the threshold of 100+ shops, underscoring a concentrated adoption profile at the upper end of the offering.
Subscription-based SaaS and marketplace platforms routinely grapple with uneven plan adoption across merchant tiers. Industry-wide, product and revenue leaders face fragmented data, making it difficult to diagnose which plan types truly support business growth or where to focus upsell and migration efforts. Traditional BI tools struggle to distill actionable insights from flat, transactional snapshots. Executives often encounter uncertainties: Where is value concentrated? Are entry-level or premium plans underperforming? What plan structures drive market penetration? Manual segmentation and limited visualization approaches ordinarily leave stakeholders with superficial answers. The pressing need is a transparent, automated solution that surfaces not only headline adoption numbers but also the nuanced composition and distribution patterns within a rapidly evolving product portfolio.
Comprehensive Dataset Scanning and Metadata Inference: Scoop identified that the data represented a snapshot of shop-plan relationships, extracting key metrics such as total shop counts, plan type breakdown, and prefix aggregations. This foundational scan set the context for subsequent automated enrichment and ensured all important business dimensions were indexed.
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Scoop’s analysis surfaced non-obvious adoption patterns hidden to traditional dashboards. Adoption is not linearly related to plan tier: mid- and high-level plans do not see stepwise increases in usage; rather, shops cluster in a handful of entry-level and older legacy plans, with sharp drop-offs for more advanced offerings. Notably, the largest plan (DISCOVER) holds 139 shops, while some premium categories capture single-digit usage. This distribution is far more concentrated than topline figures would suggest—and would be easily masked by standard surface-level KPIs.
Additionally, prefix-level rollups showed that just three plan families account for the lion’s share of deployed shops, despite a dozen available choices. This hints at latent customer inertia or legacy lock-in. Furthermore, machine learning modeling—applied agentically by Scoop—objectively revealed that plan type and prefix alone are not sufficient predictors of where shop counts land or how usage scales with plan features. The pipeline returned high misclassification rates, and models defaulted to trivial single-rule behavior, highlighting a key data limitation: operational segmentation and pricing optimization will require richer enrichment, perhaps with behavioral or demographic signals. These nuanced findings would be impractical to surface with manual exploration or static charts alone.
Armed with Scoop’s actionable summary, the platform’s product and marketing teams aligned on immediate priorities: designing targeted campaigns to migrate users off legacy plans, developing new positioning for underperforming premium tiers, and evaluating whether to consolidate or sunset plans with persistently low adoption. The lack of predictive power in current segmentation signals the need for more granular tracking of customer attributes—unlocking richer feature sets for future ML-guided decisions. Next, teams will revisit data instrumentation for new shop-level variables, while using Scoop to continually monitor the impact of plan optimization initiatives and adoption migrations. This closes the loop on extracting value from ‘snapshot’ operational data, and positions the business for iterated, AI-informed portfolio tuning.