When you’re starting a brand new customer advocacy program, or re-starting a program, it’s important to have realistic expectations. Nominations and Advocate Requests: these are two fundamental “motions” of an advocacy program. One correlates to building your advocate database, the other with effectively using that database: two sides of the same coin.
Remember in elementary school math how you learned to estimate? Estimating ensures you’re on the right track. If you estimate 30, but your calculation yields 62, you know something is off. So how does this apply to nominations and advocate requests?
When you seek advocate nominations from co-workers in sales, customer success, product management, professional services, etc., you can make some assumptions to arrive at an estimate. Let’s think about CSM nominations.
We’ll start with the common sense approach first.
Another way to estimate is by percentage. Some percentage of the CSM’s assigned customers are going to be advocates, or strong advocate candidates. How do you get to that percentage? The most objective way is to consider recent customer feedback.
With this information, you should expect a nomination campaign to yield somewhere between 150 and 250 nominations, assuming just one contact per unique account. Of course, it’s very possible that each account has more than one happy contact. Stick with one contact per account if you want to be conservative, or be more ambitious with an assumption that 10, 20 or 25% of accounts will have two contacts with advocate potential.
To estimate advocate request activity, you need to understand how sales opportunities work in your organization (this post provides a deeper dive). In short, you must have answers to these questions:
For example:
What you should expect in terms of request volume is of 85 closed opportunities per month, 64-67 require references. With an average of 1.7 reference accounts per opportunity, there should be 109-114 unique account requests each month. Before settling on that number, consider that some salespeople won’t look for references outside their own “back pocket” of references. The more tenured salespeople will do this more frequently, while the newer salespeople will need your help or that of their other colleagues (e.g., CSMs or AEs). This is more art than science. You’ll want to discount the 109-114 estimate by some percent to account for the “back pocket” references. Maybe the number is 10 or 15%; it’s an intuition call.
Then there are marketing activities that require advocates including demand gen campaigns, events, PR, social media, and more. Each of these functions should maintain a calendar 2-3 quarters in advance, or more. As a program manager you can be a proactive consultant and not only be ready for their needs, but identify opportunities to use advocates in ways they hadn’t considered. That’s value add! In any case, these respective calendars provide the advocate forecast you need to meet demand.
It’s important to set expectations for yourself and your leadership when it comes to nominations and requests. You don’t want to be too conservative and “smash” your numbers, with no meaningful traction to show for the program. On the other hand, you don’t want to set sky high expectations and burst everyone’s bubbles, thus losing attention/momentum. By using the estimation approaches outlined above, you’ll be able to establish goals that are attainable and impressive. If you miss your estimates, you’ll find our posts on executive support and the reference shadow market useful in planning your next steps.
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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