CMA pros are always looking for deeper insights that lead to the best matches, and optimize advocate impact on sales and marketing activities. With AI advancing rapidly, there’s growing enthusiasm about leveraging unstructured data—like Gong conversation transcripts, free-form text notes, and emails—to get the most relevant advocates, and uncover customer advocate candidates.
At first glance, this seems like a potential goldmine. More data should mean more insights, right? Not necessarily. While unstructured data can contain valuable nuggets of information, it’s unreliable as a primary source for identifying who is actually willing to engage in customer advocacy activities—like taking sales reference calls, speaking at events, or participating in media interviews.
It’s usefulness in surfacing positive sentiment customer quotes possibly leading to richer, more compelling content (slides, website quotes, case studies), that’s a more realistic, near-term use case. This is a true gamechanger for time-challenged program managers!
I’ll refer to the collection of related advocate information as an advocate profile. In ReferenceEdge, it is designed to provide a structured, up-to-date, well-populated repository of advocate insights. And we’ve built a variety of features in ReferenceEdge addressing recruiting and data updating to ensure it stays that way. Done right, this profile data may very well be the most accurate data source in your company outside of financials. Advocate profiles are (or should be) the gold standard of customer advocate intelligence, and include, at a minimum:
Because this data is structured and quality controlled, it enables precise, fast searches—ensuring that sales and marketing teams can quickly find pre-qualified, highly matched advocates with full confidence. Trustworthy information is the name of the game. Without it users return to their highly inefficient, unreliable advocate “hunting” behavior using Slack, Teams or email, and user adoption of the program and enabling systems is undermined. Their customer advocate need becomes everyone’s problem. because entire sales and CS teams have to evaluate scatter shot messages asking for help
Unstructured data, by contrast, is anything that doesn’t fit neatly into an organized database—sales call transcripts, rep notes, customer emails, and Slack conversations. AI-powered tools can analyze these data sources for sentiment, intent, and context, but they lack the certainty and structure required for confident advocacy identification—although results are confidently returned.
The result of each of these promising insights is a separate, just-in-time recruiting effort. If the goal is efficiency, then adding the recruiting step to every request fulfillment motion is counterproductive. It is the opposite of building and maintaining a verified database of ready-to-go advocates.
Does this mean unstructured data is useless? Not at all. It might highlight advocate candidates (i.e., advocates-in-the-making), identify trending sentiment shifts, and surface opportunities for further validation.
But it’s purely a secondary layer of insight—not a replacement for structured advocate profile data. Think of it like this:
The excitement and novelty of AI has naturally led to atmospheric expectations about leveraging unstructured data within customer advocacy programs. While AI can surface insights from call transcripts, notes, and emails, advocate decisions require certainty—especially when engaging your best customers. Over-relying on unstructured data puts sales and marketing at risk by leading to mismatched advocacy requests, busy work, eroding trust in AI tools, and driving disengagement among customers who feel misaligned with outreach. The result? Sales teams lose confidence, AI adoption stalls, and customers start tuning out. AI will improve over time, but nothing—whether used by AI or humans—beats the power of reliable, structured data. Never stop tending to your advocate profile data and your AI dreams just may come true.
Contact us today to learn how we can help.
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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