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In the highly competitive world of trade and construction media, maximizing subscriber engagement and retention is essential for sustained growth. With surging digital acquisition via social channels and varying patterns across trade specialties, teams face complex questions: Which audiences engage most? Where is content misaligned? And what predicts income or risk of churn? This case study shows how agentic AI—automating granular subscriber analysis from raw data to business insight—equips leaders to address these challenges, unlock monetization opportunities, and future-proof their subscriber strategy.
Equipped with predictive and diagnostic insights, the marketing team gained clarity on the levers of subscription engagement and income stratification. End-to-end automation surfaced how age, trade, state, and business type interacted to shape outcomes. These findings empowered targeted content strategies, improved risk assessments, and underpinned monetization planning:
Audience was no longer seen as monolithic. Older, high-earning subscribers in specialty trades emerged as high-value segments, while DIYers and certain employed professionals flagged as high churn risk. Acquisition channels (API, Facebook, Instagram) could be mapped to distinct behaviors and modeled for future growth.
At the business level, teams could finally forecast the impact of content optimizations and channel mix adjustments with confidence, shifting from descriptive metrics to action-guiding, prescriptive analytics.
Despite sector churn pressures, 94.6% of subscribers remain active, confirming strong platform value and an effective initial engagement funnel.
A record 2,852 net new subscribers joined in one month, powered by social acquisition—highlighting virality but also risk of audience heterogeneity.
A record 2,852 net new subscribers joined in one month, powered by social acquisition—highlighting virality but also risk of audience heterogeneity.
33.1% of all users were classified as highly engaged, led by Carpenters and specialty trades with 35–50% open rate advantages over generalist segments.
71.5% of subscribers fell below active engagement benchmarks, presenting a clear opportunity for targeted reactivation.
Audience development in trade and construction media is marked by data fragmentation and diverse professional backgrounds—from carpenters and electricians to DIY enthusiasts and managers. Traditional BI tools often struggle to surface actionable engagement drivers and subscriber risk segments due to multidimensional data: channel attribution, income, geography, and shifting behavioral signals (opens, clicks). Despite an above-industry retention rate of 94.6% and strong subscriber growth (notably in February 2025), engagement metrics revealed pain points—open rates dropped 25% month-over-month and 71.5% of users showed low or no interaction with email content. Content fatigue, channel misalignment, and unclear monetization levers were challenging to identify or address without an end-to-end, ML-powered approach. Stakeholders needed answer to critical questions: What drives high engagement by trade, age, and ownership type? Who is at risk of unsubscribing, and what role do acquisition channels or business structure play? How does professional evolution affect both engagement and eventual income?
Scoop ingested, interpreted, and analyzed a rich, longitudinal subscriber database comprising 4,806 individuals from Australia’s construction and trade ecosystem, spanning demographic, professional, channel, and behavioral fields. The data included: subscriber duration and cohort patterns from 2023–2025, over 10 core columns (trade type, business type, age, income bracket, region, open/click rates, acquisition source), and thousands of records detailing the full customer journey.
Scoop ingested, interpreted, and analyzed a rich, longitudinal subscriber database comprising 4,806 individuals from Australia’s construction and trade ecosystem, spanning demographic, professional, channel, and behavioral fields. The data included: subscriber duration and cohort patterns from 2023–2025, over 10 core columns (trade type, business type, age, income bracket, region, open/click rates, acquisition source), and thousands of records detailing the full customer journey.
Scoop’s agentic modelling revealed several non-obvious, business-critical patterns that traditional dashboards failed to detect:
Professional status and business structure had non-linear effects: Sole traders and small business owners expressed higher, more consistent engagement within certain trades, while employed professionals and apprentices lagged except in electrical and plumbing (where even entry-level roles offered high income and engagement).
Engagement was powerfully shaped by geography—not only at the state level but by regional trade economics. For example, builders in Queensland outperformed those in New South Wales on engagement, while Victorian trade specialists (e.g., Glaziers, Scaffolds) emerged as persistent content advocates. Age added further complexity, with younger apprentices more engaged in the east but not the north, and a U-shaped engagement curve for sheet metal workers across career arcs.
Acquisition channel analysis challenged prior assumptions: Direct website signups and social media referrals triggered different click propensities and long-term churn risk. Instagram and Facebook unlocked rapid growth but sometimes delivered segments prone to disengagement or brief curiosity-driven unsubscribes.
Perhaps most counterintuitive was the finding that high-frequency clickers—especially among employed professionals and managers—were actually at elevated risk of future unsubscribing when not supported by regular open rates. Meanwhile, passive consumers (never clickers) tended to remain subscribed longer than interactive, but dissatisfied, users.
Finally, income prediction models surfaced career pathing: Electrical and management professionals achieved high income regardless of age or location, while laborers and DIYers typically remained in lower income bands even after years in the system.
Following Scoop’s recommendations, the marketing team is implementing segmented content calendars—prioritizing trade specialties and owners with proven engagement, and reconsidering campaign cadence for churn-risk segments like DIYers or Facebook-acquired subscribers. A/B testing will focus on revitalizing content for lower-engaged age/workforce segments and exploring new acquisition efforts targeting underrepresented groups.
Additional machine learning analysis is slated for Q2 to further personalize experience and pilot tailored conversion offers for high-income, specialty professionals. The data-driven approach, grounded in agentic AI insights, will inform broader monetization—opening new product lines for advanced trades and refactoring lapsed user campaigns. Finally, ongoing monitoring will automatically flag emerging engagement dips or channel performance changes, closing the loop from data to action.