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Case Study · 60 days
Consumer brand: +48% social engagement
Measured over 60 days in a live client deployment: +48% social engagement, +36% repeat buyers, 32% lower cost per engagement and 40% faster content production. The client’s name is withheld at their request. Results are specific to this deployment and are not a benchmark, forecast or guarantee.
Context
Lifestyle-accessories brand serving travel, fitness and professional use cases · 15 employees · Canada · direct-to-consumer e-commerce.
Challenge
The client faced stagnant Instagram and TikTok reach, content fatigue, slow manual copy production, rising social-ad costs and fragmented cross-channel reporting.
Applied work
Brand-voice analysis, AI-assisted content generation, engagement forecasting, paid-social optimization and unified analytics across identified social, e-commerce and measurement platforms.
Measured outcomes
+48% social engagement · +36% repeat buyers from social-driven traffic · 32% lower cost per engagement versus the prior quarter · 40% faster content production.
Evidence boundary
Results were measured over a 60-day window in one client deployment. They have not been independently audited and do not include enough attribution detail to establish generalized predictive accuracy or causal performance. Statements about trend prediction, real-time signals, brand-voice replication or spend reallocation should therefore be read as deployment-specific implementation descriptions and remain subject to human review.
HOW TO READ THIS CASE
Measured results through the mAI operating model
This case reports results from one live client deployment. The stated context and measured outcomes remain the evidence boundary; the mAI operating model below provides a consistent way to inspect how the work maps to marketing operations without extending those results.
PLAN
Define the operating problem, business context, constraints, baseline and KPI contract.
EXECUTE
Apply the deployment’s workflows, channels, content, scoring or integrations under human oversight.
MEASURE
Read the measured metrics against the stated window and the stated limitations.
OPTIMIZE
Use measured signals to refine approved actions without treating deployment-specific results as universal benchmarks.
P² EVIDENCE VIEW
Productivity + Precision
Productivity signals include operating effort, reporting time, workflow speed or automation coverage when the deployment measured them. Precision signals use the business KPI measured in this case. Neither dimension should be generalized beyond the evidence provided.
HUMAN COMMAND
Governed interpretation
Strategy, audiences, budgets, brand-critical content, privacy, compliance and material policy decisions remain subject to defined human approval. Evidence classification and claim boundaries are part of that governance layer.
EVIDENCE STATUS
This page is a case study from a live client deployment. Its outcomes were measured in that deployment, and the client’s name is withheld at their request. Read the results with the stated context and boundaries, not as a forecast or guarantee.
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