The title, “Yes, Sales Needs Customer Reference Training,” sounds crazy, doesn’t it? The people who always seem to need customer references, and often at the 11th hour, may not be following best practices. How could that be?
Think about how many companies you’ve been at where customer reference training was part of Sales onboarding/orientation. One? None? The result of omitting customer reference from sales training is that junior salespeople learn by watching what the Sales veterans do, who learned from salespeople they emulated when they were junior, and so on and so on and so on. And often, none of the salespeople in that training chain were doing it right.
In an earlier post, Why Sales Teams Need Customer Reference Training from SMEs, I broached the notion that reference program managers are the SMEs, rather than sales, as one might expect. In this post, I’ll provide what would make sense to include in customer reference training. The goal of offering, even requiring this training, is to close more deals and increase Sales. Here’s how you might organize the class.
Most Sales and Marketing pros would agree that references should not be provided after, say, the first call with a prospect. That’s clearly too early. Consider the following:
How often does a cry for references include “ASAP” and “URGENT” in the first line? I’d venture to guess that most of these requests result from putting off the reference search until it becomes an emergency. It’s crucial to teach salespeople that reference customers aren’t waiting by their phones. They take vacations, have family emergencies, travel for business, and attend conferences. Leave a reasonable amount of time to secure references. It’s a matter of courtesy to their peers and the customers. Reference requests need not be an emergency that ends up with an “I’ll take anyone at this point” situation.
It’s hard to imagine this needs to be said, but reference customers and prospects should be comparable in terms of accounts and contacts. Your organization might include any number of the following considerations and some that are unique to your ideal reference profile: revenue size, number of employees, geography, using the same solution of interest, industry, use case, and contact seniority or persona. It’s not unusual to compromise on one or more of those criteria, but the initial request should start with the ideal reference profile. Salespeople would be wise to factor the following into their choices:
Every company’s reference management processes and practices are specific to that organization to some extent, but whatever your established practices are, communicate them clearly. Deviations from those prescribed practices should not be supported by peers, program managers (if applicable), or managers. Typically, the reference account is identified, the relationship owner (e.g., customer success manager) is consulted, a customer contact is identified and secured, and the salesperson coordinates the call.
That doesn’t seem like an unreasonable approach, yet we still hear stories of salespeople trolling through the CRM system, identifying desirable references, and using their contact information to reach out directly. That’s going “rogue” and a real novice move. Put an end to this behavior.
We’re biased, of course, but this process, start to finish, ought to be in a system, tracked and quantifiable. That’s the logical way to ensure repeatable best practices and the most favorable outcomes of reference use.
Salespeople need to know when a reference activity, such as a reference call or site visit, occurs, and they should follow up with both parties for a debrief. Here are some key reasons to debrief and track reference activity:
Consider building your customer reference training module and adding company-specific elements that your salespeople relate to. I’ll bet just about every salesperson can tell stories about poorly executed reference practices (maybe not their own, but someone they know :-)). You can use those stories to further flesh out your training. There’s nothing like storytelling to reinforce learning. New hire training is the obvious point to introduce this material, but don’t forget about the existing team. When you take on this training responsibility, you become a more strategic advocate consultant, and executives notice.
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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