Featured Guest: Asha May
In this episode of The CustomerX Files, host Alison Bukowski welcomes customer marketing and advocacy thought leader Asha May for a deep and practical conversation about one of the most important — yet sometimes undervalued — levers for customer program success: the partnership between Customer Marketing & Advocacy (CMA) and Customer Success (CS).
With more than two decades of experience working across both customer success and customer marketing domains, Asha brings a unique and highly strategic perspective to how these two functions can collaborate to not just support customers but also drive measurable business value. Throughout the episode, she shares real-world examples, lessons learned, and actionable guidance that CMA and CS professionals can implement to strengthen relationships, streamline collaboration, and ultimately deliver better outcomes for customers and the business.
While organizations often operate CMA and CS as separate functions, Asha and Alison challenge that status quo by highlighting why alignment between these teams is critical in today’s customer-centric business environment. Far from operating in silos, the most effective customer programs exist where success teams and marketing and advocacy professionals co-design strategies, share insights, and partner closely on execution. In this conversation, listeners gain a clear understanding of what true partnership looks like and how to get there.
One of the episode’s key themes is the shared mission between CMA and CS: ensuring customers are not only satisfied, but empowered to participate, advocate, and grow. Asha explains how customer success teams are often the first to understand customer challenges, goals, and journey dynamics, while CMA and advocacy teams are experts in amplifying customer voices, storytelling, and program engagement. When these strengths are combined, the organization can build more purposeful advocacy programs that resonate with customers and internal stakeholders alike.
Asha outlines several practical collaboration strategies that CMA and CS teams can adopt. This includes establishing regular communication cadences, co-creating customer engagement plans, and developing shared performance indicators. By aligning on goals and expectations, teams can ensure they’re working toward common outcomes — such as improved retention, stronger reference pipelines, and higher advocacy participation rates.
The discussion also dives into how to break down relationship barriers that often exist between CMA and CS teams. Asha shares thoughtful advice on building empathy across functions, understanding differing priorities, and creating shared language around customer value. These insights are valuable for leaders who want to foster a culture of partnership, not competition, across customer-facing groups.
Measurement is another topic Asha addresses thoughtfully. She emphasizes the importance of defining success metrics that reflect both operational excellence and strategic business impact. For example, this includes metrics that recognize improvements in customer health, satisfaction, reference readiness, and advocacy engagement — all of which are strengthened when CMA and CS work in harmony.
Throughout the episode, listeners will gain valuable insights into:
Whether you’re leading a customer marketing function, driving customer success initiatives, or working at the intersection of both, this episode offers perspectives and practical advice you can put into action immediately. You’ll come away with a stronger appreciation for how purposeful collaboration fuels better customer experiences, builds stronger advocacy pipelines, and elevates the impact of your entire organization.
Listen now and discover how cultivating an intentional partnership between CMA and CS can unlock new opportunities for customer connection and business growth.
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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