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Marketing teams have been under pressure for years to quantify their value. Prove ROI. Show the numbers. Demonstrate exactly how their work drives revenue.
On the surface, it sounds noble—even scientific. But when the customer marketing and advocacy function lives within marketing, the quest for quantification can lead to some unintended consequences, especially when it comes to proving the value of customer advocates.
When known, organized, and searchable, customer advocates are rocket fuel for sales, marketing, and customer success. Their real-world stories carry a level of credibility no campaign, pitch, or product sheet can match—when used thoughtfully and consistently.
But here’s the million-dollar question: how do you connect advocate activity directly to revenue?
At first glance, attribution models seem like the perfect answer.
In marketing, attribution is the practice of assigning credit to different touchpoints along the buyer’s journey—email campaigns, website visits, events, conversations, and more—to understand which activities influence purchase decisions.
Sounds logical. Sounds data-driven. What could go wrong? Plenty.
The problem: the percentages assigned to each touchpoint aren’t discovered through some magical formula. They’re a judgment call—Unless every buyer submits to a post-purchase survey and ranks all the touchpoints, then all those rankings are averaged on a monthly or quarterly basis (to account for changing conditions), there really isn’t any data relevant to this exercise.
Fact: Every buyer, every deal, and every context is different.
Let’s play this out:
By the time a prospect connects with a salesperson, the buyer is already over 60% through their journey. They’ve clicked the emails, browsed the site, maybe attended your webinar or stopped by your booth. They’ve heard your pitch, seen the demos, gotten the quote.
And now? They want proof.
Unless your solution is the undisputed leader in the space, the buyer will want to talk to real customers—people who’ve stood where they’re standing, made the choice, and lived with the solution.
These conversations happen late in the journey, when impressions are mostly formed, but confidence isn’t yet locked in. And they’re incredibly influential.
Now imagine you don’t have any advocates to provide.
How much would that impact the deal?
In many cases, the deal is lost entirely. No proof, no trust, no sale. That’s 100% attribution right there.
Or picture this: you offer advocates, but they’re poorly matched—a small business advocate for a large enterprise buyer. Does that devalue the entire advocate conversation? Probably. Does it always tank the deal? Not necessarily. Maybe this less than stellar advocate interaction gets 5% attribution.
Let’s say your in a pitched battle with one or two competitors to win a deal. The buyer says you all have pros and cons, but each could probably meet the minimum requirements. Your salesperson quickly finds well-qualified advocates to address lingering doubts. Those conversations win the deal. Do advocates get 100% attribution because without them the deal was lost? Great question!
And herein lies the rub: there’s no universal percentage to assign in these scenarios. Every situation is unique.
So how do you credibly discuss advocate impact with executives looking for numbers that correlate to revenue growth?
Stop trying to play the percentages. Instead, track revenue influenced—the full deal size whenever advocates are used in the sales cycle.
Influenced is intentionally relative, because there are too many variations to model with surgical precision. Any attempt to assign neat percentages to advocate impact falls apart under scrutiny—especially if an executive team drills into the details. In the real world percentages fluctuate from opportunity to opportunity—sometimes wildly—and in the end it’s a wash.
A few things you can say with great confidence:
In between, the numbers are all over the place.
Customer advocates can be the difference between winning and losing. They’re the human proof points buyers crave at the moment of highest stakes.
Attribution models might look neat on paper, but they oversimplify messy, human decisions. When it comes to advocates, the smartest move isn’t slicing percentages—it’s recognizing their strategic power and measuring revenue influence, not rigid attribution. Contact us today to learn more on how ReferenceEdge can help you prove your customer advocates are the difference makers.
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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