See Scoop in action
Bring your data to life with AI-powered presentations—start your free trial of Scoop.
The only AI that does real data science. It finds patterns, segments, and predictors in your data — Try it now →
.jpg)
Today’s marketing leaders face volatile channel performance and fast-evolving market conditions, making it difficult to sustain return on investment. This case study shows how marketing teams used Scoop’s agentic AI to automate deep-dive analysis of year-over-year spend, pipeline revenue, and efficiency across their core channels. Automated machine learning quickly flagged pervasive ROI declines and surfaced actionable counterintuitive insights—guiding leadership to rethink budget distribution and attribution. The result: a rapid pivot toward proven, efficient investments amid shifting external dynamics. For executives committed to maximizing every marketing dollar, the lessons in this story are essential.
Scoop’s automated analysis delivered a comprehensive view into shifting marketing dynamics that would have taken weeks of manual effort. By revealing declining efficiency across almost all major channels—even where spend increased—it catalyzed a data-driven shift in strategy. The actionable insights included a counterintuitive pattern: largest spend allocations didn’t yield the best returns, and a secondary, lower-budget channel quietly outperformed on efficiency. These findings equipped leadership to target budgets with precision, realign expectations on past performers, and support an urgent review of effectiveness thresholds and attribution models.
Year-over-year, budget allocation to Google Ads surged, making it the dominant channel in 2024 Q2.
A smaller budget channel achieved the highest efficiency, outpacing all others in return on spend.
A smaller budget channel achieved the highest efficiency, outpacing all others in return on spend.
ROAS for LinkedIn dropped to zero, marking a complete loss of efficiency and raising concerns about ongoing investment.
Machine learning analysis classified 3 of 4 channels as low efficiency, identifying a pervasive decline across the marketing mix.
In dynamic B2B marketing environments, teams struggle to make high-stakes budget decisions using fragmented historical data and siloed reporting from disparate channels. Traditional business intelligence tools often fail to detect non-obvious patterns or adapt quickly to shifting performance baselines. Over the past year, channels that were once top performers experienced sharp declines in both spend efficiency and pipeline generation. Channel allocations shifted dramatically—yet leadership lacked confidence in which reallocation would deliver resilient returns. Meanwhile, mounting external pressures and market uncertainty muddled classic metrics like ROAS, making past trends a poor predictor of future success. The core business challenge: how to automate the detection of true efficiency signals amidst noise, and execute actionable, data-driven pivots faster than human-driven analysis or dashboards alone can deliver.
Automated dataset scanning and metadata inference: Scoop instantly recognized financial, attribution, and categorical fields, enabling zero-setup ingestion regardless of pre-existing schema. This eliminated manual mapping delays and ensured data quality from the outset.
.jpg)
Scoop’s agentic ML modeling surfaced several patterns not visible in conventional dashboards or ad-hoc analysis. Most significantly, it flagged that declining efficiency was not isolated but systemic—spanning both high- and low-investment channels, regardless of historic performance. This non-obvious uniformity in underperformance suggested either shifting external dynamics or misaligned attribution models, a nuance easily obscured by traditional channel-by-channel reporting.
Another counterintuitive finding: increased investment in key channels (notably Google Ads and Bark) sometimes correlated with improved efficiency, contradicting the assumption that cutbacks would naturally yield better ROI. Surprisingly, the highest-ROI spend was found not among maximal or minimal allocations, but at 'medium' investment levels—challenging industry assumptions around spend scaling. Additionally, the model did not surface differentiating features (such as channel type or past performance trends) predictive of future marketing effectiveness. This lack of signal—quickly and conclusively surfaced—enabled leadership to shift their focus from channel optimization to holistic strategy and attribution review, actions unlikely to result from dashboard monitoring or simple historical benchmarking.
Armed with these automated insights, the marketing team began a comprehensive review of budget allocation and attribution methodology. Low-efficiency patterns prompted immediate scrutiny of underperforming channels and spurred a pivot towards evidence-backed, moderate investment strategies. Leadership initiated efforts to revise segmentation and target 'medium spend' efficiency bands, while deprioritizing legacy channel biases. Plans are in place to recalibrate ROI and performance thresholds, ensure future budget shifts are modeled against current market realities, and to institutionalize automated, agentic analysis for ongoing campaign review. The shift is from reactive, surface-level reporting to proactive, ML-driven operational steering—maximizing returns in an increasingly unpredictable environment.