Author name: Kevin Farley

Kevin Farley is a marketing and growth executive with more than 20 years of experience leading brand, digital, analytics, and experience functions across financial services, technology, and regulated industries. Most recently, Kevin served as Vice President of Marketing at United Heritage Credit Union, where he oversaw marketing, business analytics, member experience, employee engagement, and the organization’s charitable foundation. Under his leadership, the organization strengthened brand visibility, improved engagement metrics, and advanced data-driven growth strategies across multiple business lines. Throughout his career, Kevin has built and scaled high-performing teams, modernized marketing technology stacks, and implemented analytics frameworks that connect brand investment to measurable revenue outcomes. His background includes work with enterprise brands such as Dell, Hertz, Sara Lee, 7-Eleven, TD Ameritrade, and GlaxoSmithKline, as well as deep experience within the credit union and fintech ecosystem. Kevin is also researching AI-powered Generative Engine Optimization (GEO) capabilities that helps organizations measure and improve their AI search readiness, discoverability, and digital authority. His work blends marketing strategy, data science, and practical execution to help institutions compete in an increasingly AI-driven landscape. He is passionate about building systems that drive measurable growth, strengthening community institutions, and helping organizations align brand, technology, and experience to create lasting impact.

Growth & Analytics, Marketing Strategy

Attributable Demand Engines Are Built — Not Reported

Most teams say they want attributable growth.

What they actually have is channel reporting.

There’s a difference.

Reporting tells you what ran.
An attributable demand engine tells you what reliably produces pipeline.

Modern demand gen isn’t about more campaigns.

It’s about signal architecture:
• Precise ICP tiers
• Behavioral weighting
• Revenue feedback loops
• Sales-aligned signal definitions

If you’re still debating attribution models without redesigning the engine, you’re optimizing the dashboard.

AI & Discovery

How to Audit Your AI Discoverability in 30 Minutes

TL;DR If AI systems are shaping buying decisions, you need to know: You can run a basic AI discoverability audit in under 30 minutes. Most companies never do. That’s the gap. Why This Matters Now Search visibility used to be enough. If you ranked well, you were found. In 2026, discovery increasingly happens inside: These

AI & Discovery

AI and Marketing in 2026: From Optimization to Orchestration

TL;DR AI is not just improving marketing efficiency. It is restructuring marketing velocity. In 2026: The marketers who win will not be better copywriters. They will be better system designers. What Is Changing in Marketing Because of AI? When people ask: The answer is not “automation.” It is leverage. AI reduces the cost of cognition

AI & Discovery

AI in 2026: What’s Actually Changing — and What Leaders Should Do Now

TL;DR If you only read this: If you want the deeper version, keep going. Something Structural Is Shifting Every few years, technology improves. That’s normal. What’s happening with AI in 2026 does not feel like normal improvement. It feels structural. Not because headlines are louder.Not because valuations are bigger.But because workflows are changing underneath the

Growth & Analytics

What Attribution Can’t See — and Why That Matters

Attribution has become one of the most debated topics in modern marketing. Entire teams, tools, and budgets are built around answering a single question: Which channel gets the credit? When attribution models don’t give a clean answer, the instinct is to assume something is broken. The data must be wrong. The tracking must be incomplete.

Scroll to Top