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Case Study · 120 days

B2B technology: +31% SQL volume

Measured over 120 days in a live client deployment: +31% more SQLs, 24% faster sales cycles, 17% lower CAC and 42% less reporting time. The client’s name is withheld at their request. Results are specific to this deployment and are not a benchmark, forecast or guarantee.

Context

Enterprise SaaS platform · approximately 200 employees · North America and Europe · subscription model with enterprise contracts.

Challenge

The client had healthy MQL volume but low lead quality, rising LinkedIn acquisition cost, inconsistent attribution and slow manual campaign optimization.

Applied work

The work covered lead scoring, LinkedIn and Google campaign optimization, content personalization, dashboard integration and recurring KPI recommendations.

Measured outcomes

+31% SQLs · 24% faster sales cycle · 17% lower CAC · 42% reduction in reporting time.

Evidence boundary

Results were measured over a 120-day window in one client deployment. They have not been independently audited and no additional attribution methodology is included. No broader causal or predictive claim should be inferred beyond this deployment.

Continue the evidence path

Interpret this measured outcome through the mAI Evidence Methodology, then inspect the system architecture or return to the Applied Evidence index for additional contexts.

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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