Featured Guest: Shannon Howard
On this episode of The CustomerX Files, Alison is joined by Shannon Howard, the Director of Customer & Content Marketing at Intellum for a thoughtful conversation about the art and science of building strategic goals in Customer Marketing and Advocacy (CMA). Whether you’re launching your first strategic plan or refining an existing one, this discussion explores not just what goals should look like — but why they matter and how to make them truly impactful across your organization.
From the outset, Shannon and Alison dive into what makes strategic goals more than just words on a page. They tackle common questions customer marketers face: How do you create goals that align with organizational priorities? How do you ensure your goals resonate with cross-functional partners? And how do you avoid crafting goals that sound good but have little influence on outcomes? Their conversation moves beyond abstraction and into practical, experience-based insight that you can apply immediately to your planning cycle.
A major theme of the episode is the importance of clarity and alignment. Shannon explains how thoughtful goal setting begins with understanding both your organization’s strategic direction and the customer experience itself. Rather than working in isolation, CMA practitioners must ensure their goals complement broader business objectives — whether that’s revenue growth, customer retention, product adoption, or deeper community engagement. When customer marketing goals are aligned with strategic priorities, they become far more powerful tools for influencing decision-makers and gaining organizational support.
Shannon also highlights how to identify gaps both between where your organization is and where it wants to be, and between what your team can deliver and what stakeholders expect. She and Alison discuss how customer experience data, customer feedback, and cross-functional insights can reveal opportunity areas that strategic goals can then target. This process not only strengthens your strategic planning but also ensures your goals are rooted in real customer needs and business realities.
Another key takeaway is how to make goals meaningful and actionable. Shannon shares advice on structuring goals with specificity, measurable outcomes, and clear timelines and why it matters to avoid vague or overly broad goals that are impossible to evaluate. She emphasizes that goals should not only guide action but also serve as a basis for shared accountability, progress measurement, and continuous learning.
Throughout the episode, listeners will gain valuable perspectives on topics including:
Whether you’re a customer marketing leader, advocacy practitioner, or part of a cross-functional growth team, this episode offers both strategic frameworks and real stories you can use to build better goals, influence broader organizational strategy, and deliver measurable value.
Listen now to discover how intentional goal setting can bring clarity, alignment, and momentum to your customer marketing and advocacy efforts — and help you drive meaningful impact within your organization.
It's only natural that many advocacy leaders have landed on the same objective: make the program easier to use by meeting users where they're already working.
Today, that increasingly means Microsoft Copilot, ChatGPT, Claude, Gemini or whatever generative AI assistant employees happen to have open.
Imagine a salesperson simply asking AI, "Find me three German healthcare customers using product Y, willing to speak with a prospect," instead of navigating to another interface, or waiting for someone from advocacy, or elsewhere, to respond. It's easy to see the appeal. Removing friction has always been one of the fastest ways to increase adoption.
It is exactly the right instinct.
The difficult parts, arguably the reason program managers exist, occur before and after AI says, "Here are your three best matches."
The value advocacy professionals bring is the ability to operationalize and scale customer advocacy for maximum impact. Quality advocate information doesn't just appear, it's the result of a system.
Now that the user has three advocates, what should happen?
Notice what happened. The search was completed.
The next steps are just as manual as ever if AI search is the be all, end all.
Reality Check
AI can tell you who could participate. It can't tell you who should participate unless someone (or something) has been keeping score.
This is where the story starts to feel strangely familiar.
Many companies still operate their program using spreadsheets, scattered CRM fields, shared drives, email folders, and the remarkable memories of a handful of program managers.
Eventually, organizations realize they aren't managing an advocacy program at all. They're managing lists that happen to contain advocates.
But the shortcomings are real:
Purpose-built advocacy platforms emerged because advocacy is much more than a search problem.
Ironically, AI has convinced some organizations to revisit the same shortcut they worked so hard to escape.
Let's imagine two different worlds.
In the first, AI recommends an advocate for a sales call.
Months later, AI knows this customer recently participated and may deserve a break before being asked again.
Now imagine the second world.
Three months later someone asks how many customer reference contributed to the revenue this quarter.
Silence. Nobody really knows.
The advocacy happened...hopefully. The program didn't. Collectively, the organization slowly stopped feeding the very system it depended on to understand its advocacy program.
Reality Check
If AI helps facilitate twenty closed-won opportunities this quarter, but none are recorded, your executive dashboard still says zero.
One of the easiest mistakes to make in an AI-first world is assuming that successful interactions somehow become organizational knowledge on their own.
They don't.
If a customer agrees to speak with a prospect and nobody records it, the organization loses far more than a single activity.
The most valuable advocacy data isn't simply who your customers are.
It's everything they've done.
That's the story AI actually wants to read.
It's often said that AI needs good data.
That's true.
But operational history is far more valuable than static customer information.
Those aren't search results.Those are patterns.
Remove any one of those pieces and AI becomes little more than an exceptionally fast search engine.
Reality Check
Every workflow skipped today is a pattern AI won't discover tomorrow.
The AI revolution has created tremendous excitement, and rightly so. Finding the right advocate is becoming dramatically easier than it was only a few years ago.
That's worth celebrating.
Just don't confuse a better search experience with a better advocacy program. Search is only one chapter in the story.
The organizations that see the greatest return from AI won't necessarily be the ones with the most sophisticated models.
They'll be the ones with the richest operational history.
Those organizations won't use AI merely to answer the question, "Who should we ask?"
They'll use AI to answer far more valuable questions.
That's when AI stops behaving like a better Google search.
That's when it starts behaving like a strategic partner.
Finding the right advocate has always been the opening scene.
If your AI can find advocates but your program can't learn from using them, you've built a remarkable search engine instead of a remarkable advocacy program.
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