Marketing automation is an essential tool to accelerate advocate recruiting. We’re 100% in favor of leveraging every possible channel to uncover customer references for your program. And not just any customer, the ones that help Sales and Marketing reach their goals,and not coincidentally, your company’s growth goals—the ones by which your CEO and the rest of the leadership team are measured.
The classic approach would be to ask Sales and Customer Success to nominate those customers who have the desired stories, use cases, geography, and product mix. This method can be very successful in many companies. In others, though, something doesn’t click. Maybe leadership didn’t communicate the importance of growing the reference database. Perhaps there are no benefits for the nominators or consequences for not being a team player. Regardless, the program must go on!!
In these circumstances, think about recruiting customers directly. Where to begin? With a customer list from your most recent customer satisfaction survey (NPS or other). Build a report to filter out the neutrals or detractors (in NPS terminology). The customers left in the list are the highest probability, most positive sentiment “leads” for your customer reference program. If you’ve got the time and staff, the ideal next step would be a phone call, followed by an email introducing the customer to your program. Include descriptions of the different advocacy activities and related levels of effort for each. For example, video interviews are a bigger ask than a written quote. Be sure to clearly communicate what’s in it for them. There aren’t many programs out there with bandwidth to take on this high-touch recruiting approach, although outsourcing to one of a few providers in our domain is an option.
Your alternative to personal calls is leveraging your existing marketing automation app. Nearly every company uses a marketing automation tool such as Marketo or Eloqua. Not only can these products support the initial email campaign introducing the program to those customers you’ve identified as “low hanging fruit,” they can capture responses that ultimately comprise the customers’ advocate profiles. Profiles include:
What’s really interesting about the “direct-to-customer” approach is how well and why it works. Even your peers who own the relationship are often surprised by the results. Sales and Customer Success folks aren’t always comfortable asking for favors from customers. They may read a minor kerfuffle as a clear sign that the customer isn’t happy enough to be a reference. But all of us take into account more than the latest nit when assessing our overall satisfaction. Taking that relationship owner out of the equation can cut to genuine sentiment.
Before you set goals like adding 100, 200, or 500 reference customers from this type of campaign, think about your stakeholders’ needs. Use their criteria to build your campaign list. It will do you no good to recruit an impressive number of new advocates that actually provide no value to the organization.
Our product, ReferenceEdge, is 100% installed in Salesforce CRM. If your marketing automation platform, like Marketo and Eloqua, is also integrated with Salesforce CRM, it will record form data from landing pages in the Salesforce Campaign object. That makes it very straightforward to leverage the data in ReferenceEdge records such as nominations or reference profiles. But even if you don’t have ReferenceEdge but do have Salesforce, the data will be in Salesforce, which makes exporting to your favorite spreadsheet tool, or reporting using Salesforce reports and dashboards that much easier.
Leveraging your marketing automation tool is worth adding to your toolbox, whether your Sales or CS colleagues simply aren’t motivated (and management isn’t helping) to contribute advocates, or you have a “you can never have too many” mentality about.
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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