“It’s the Search, Stupid” is a twist on an old campaign slogan that applies to customer reference program success. If you’re old enough, you may recall a famous quip from a political advisor in 1992 concerning what he thought should be a presidential campaign messaging priority.
There were plenty of concerns and priorities back then, as there always have been, but at the end of the day, if voters didn’t feel the economy was working for them, they wouldn’t care about anything else—a sort of voter Maslow’s Hierarchy of Needs. Sometimes the priority is that straightforward and simplistic.
Similarly, many factors make a customer reference program tick or not. Our customer success team has a checklist full of best practices related to program promotion, user training, executive support, cross-functional coordination, member recruiting, and more. Yet even when all these “pistons” are firing, the stakeholder’s experience comes down to one simple thing: Can they find the reference(s) they need? It doesn’t matter whether you’re a salesperson, a PR manager, event coordinator, or executive; if the search doesn’t result in what you need, the rest doesn’t matter much.
Reading this observation, it seems pretty obvious, right? But how does that translate into how a reference database is built and maintained? It is not unusual for program managers to put out a request for customer reference candidates then gratefully accept whatever comes back in the “net.” Unfortunately, this thinking does not result in a well-built database. Imagine 100 new customers come into the database. If the primary demand is for director-level, IT references in banking and insurance using products A and B together, and only four of those 100 additions match the need, what was the value of the recruiting effort? If these 100 new customer references are touted as a major success, then, with great expectations, users go to do their first search and can’t find what they need, the program gets a black eye. Users return to their old, inefficient way of finding references: Slack, Chatter, Teams, email, etc.
This is no small misfire. Consider the consequences:
So how do you avoid such a setback?
To answer these questions, we recommend having a formal or informal advisory board for your program. Members will tell you what they need. For instance, marketing stakeholders will have calendars for press releases, events, and analyst calls. It’s good to know of any deadlines involving advocates and as far in advance as necessary. Your advisors will also ensure you understand how they need to search. If they don’t have the means to filter as needed, the data may as well not exist. For more on advisory boards, see this post.
Once you have this information, you’re ready to start recruiting, which is an art and science unto itself. The exact methods and channels used will vary based on the needs you’ve uncovered. For instance, if you found that VP and CxOs are a priority, then you’ll likely find that your company’s executives, not salespeople or CSMs, are in the best position to act as recruiters to at least initiate the conversations. For more on recruiting best practices, check out this post. Completing the described exercise helps ensure that your colleagues’ time is well-spent and produces many, many successful reference searches building confidence in the program and stakeholder support that keeps on giving in a virtuous cycle.
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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