Every customer advocate program (CAP) measures its success in a variety of ways. There are metrics related to building out the database so that stakeholders find what they need when they need it. That’s super important because if amassing a quality database is not happening, there really isn’t a program so much as a suspect collection of potentially happy customers. This does not inspire confidence or trust.
Fast forward, you’ve built a stellar collection of references that align with your company’s growth goals. Then the questions are, do stakeholders know about this resource? Do they know how to find it? Do they know how to use it? All of these questions get to the heart of user adoption. Low or no awareness of or education on customer reference program use results in low or no user adoption.
We’ve traced every useful measurement back to one essential metric, the seed, if you will, of all long-range outcome goals:
That metric is the percentage of opportunities leveraging reference resources. If we were starting a program tomorrow, we’d need a baseline, which would be the percent of opportunities currently using some form of a reference to ultimately close a deal. It’s not a perfect science, but surveying salespeople will get you close. The question to ask?
“On average, what percentage of your opportunities require customer references in order to buy?”
Notice that “customer references” is not specific. That’s because customer content such as videos, case studies, webinar recordings, and reviews, along with reference calls/forums and site visits all leverage a reference. At the core is a customer reference/advocate.
If asked, “What percentage should we be shooting for?,” we’d answer, “there is no universal percentage. It depends on the nature of your solution or service.” How much social evidence is needed for that particular solution? If you own your space, probably not as much. If you’re blazing a new trail or a recent entrant into a crowded market, or maybe breaking into a new industry, probably a lot more.
Let’s say that your survey results show that, on average, 20% of opportunities leverage references. As a customer advocate believer, you know that the more frequently references are employed, the better the chances your sales team wins. We know it intuitively, but there are plenty of studies that support that presumption. Here’s a sample:
Now, what can you do to increase the use of customer references by your sales team to 30%, 50%, or more?
Perhaps you have thought of your role as more marketing than sales. But as you can see, to have an impact on this essential metric—the percentage of opportunities leveraging reference resources—you’ll need to put on your sales enablement/effectiveness hat and partner with that team. Improving this metric should be a shared goal. It’s awfully difficult to argue with this logic if the end goal is to win more deals.
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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