“How many customer references should I have in my program?” We routinely get this question from reference program managers, and the answer isn’t as cut and dry as one would guess. And it’s important to get it right, for these reasons.
Recruiting customers for your customer reference program requires effort in the form of clearly communicating the needs, cajoling relationship owners to nominate, vetting candidates, contacting nominees, and onboarding new members. Multiple stakeholder departments and personnel are involved. If relationship owners (e.g., customer success managers) are incentivized with cash or prizes—for quality nominations—the effort has hard costs too.
What’s the ultimate goal of all that activity? To assemble a pool of customer references that meet the needs of Sales, PR, events, digital, demand gen, and all the other stakeholders that rely on references to get their jobs done. Will just any reference customer do? Not just no, but hell no. Buyers have high expectations. They won’t settle for a client reference in a different industry, with a different use case, or even a different size organization. The customer reference database must be broad and deep enough to satisfy the reference needs that fuel the company’s specific growth goals.
When a program goal is to “increase the reference database by 100 references this year,” “have 25% of our customer base be referenceable,” or “grow our database by 20% this year”—with no connection to stakeholder demand—that goal setter is missing the point: to satisfy actual demand. Given that a program manager has a finite amount of bandwidth, and so do the co-workers asked to help identify and recruit reference candidates, this approach is simply wasteful.
When there’s always so much going on, it’s easy to lose track of the raison d’etre. One or more of the following factors causes a temporary loss of reason (“What are we doing this for again?”):
Some of your company’s growth strategy won’t translate to reference needs, but those will be the minority. If one of the goals is, for instance, to “increase Q3 revenue by 20% from mid-sized healthcare organizations in the Midwest region using the new cloud product,” you’ve got a good idea of the kind of reference requests to expect. Based on the relevant growth goals, start identifying the general categories of reference customers needed. You’ll refine these categories after completing steps 2 and 3.
The customer reference program must support the current pipeline of reference needs, as well as what will be required 60, 90, 120 days into the future. The current demand is in the data from the Sales team. Report on open opportunities and identify segments that’ll represent the bulk of reference needs based on estimated close dates. Look for patterns in the data. These could be industry, geography, product, specific channels, and of course, combinations of criteria.
For needs outside of Sales, consult with leaders of your stakeholder teams (PR, Social Media, Demand Gen, etc.). They’ll have their own objects and action plans. Some elements of these plans will require customer advocates, and a calendar to go with it. In some instances your colleagues will already have customer stories in mind (e.g., an email campaign featuring customer X, with a product Y story). There will also be opportunities for you to suggest ways to best leverage the customer references you know well. Again, look for patterns in the reference needs that emerge. Are there “stories” of a particular type that will be needed (partner success, integrations, cost savings)? What segments are most urgent (industry, product, use case, enterprise size)? What are the time frames for various reference needs? You’ll need these deadlines for new references to be of any use.
Once you’ve obtained the expressed or anticipated demand, compare those needs with the existing database. Assuming you have some pool of customer reference data to start with, you’ll need to assess whether or not the data has been maintained and how confident you are in its accuracy. There is some amount of churn within any customer reference database. Before you finalize your gap analysis, review existing data and remove any accounts/contacts that are no longer referenceable.
Consider the volume of anticipated Sales requests. How many reference customers are typically needed for each opportunity? How often can you use any given reference contact? Knowing your company’s Sales process in the context of customer references is essential to arrive at an accurate estimate.
The same exercise is needed to evaluate non-Sales reference needs. Perhaps the event team will need more VPs and CxOs for virtual events, or analyst relations is going to need 15 customers for the Forrester Wave, with specific criterion, in 30 days. Is the program prepared to meet those needs?
This data analysis will save you lots of time down the road, ensuring you build your reference database efficiently on the right criteria. Remember, you have limited time and can’t afford to cultivate reference customers no one needs. Start prioritizing what gaps you need to fill first. Not all gaps are of equal importance. After addressing the top priorities, you can move down the list.
There are many decisions to be made concerning the best ways to fill your data gaps. Here are a few considerations:
Whatever means you choose to use for your recruiting effort, be sure to communicate what you’re looking for precisely in the interest of both time and effort expended by all parties and because there’s a customer relationship aspect to consider. Imagine you’re the customer. Your CSM proposes membership in the program, explains the benefits (whatever they may be), and gets you excited to participate. Then, because your customer story isn’t in-demand (on the priority list), you don’t get a request in 3 months, 6 months, maybe ever. That’s not the message you want to send to a valued customer.
Each customer reference program manager defines a process for onboarding a new customer into the program. Some strive to meet with each new member and walk them through the various opportunities, time commitments and benefits. A live conversation establishes a direct relationship, which can pay dividends down the road. Direct connection is a good way to ensure the customer’s reference profile is complete and accurate. You might even discover they’ll do more reference activity types that you had been led to anticipate. Score! Regardless, this is the labor-intensive approach, and it may simply be impossible. At the other end of the spectrum is providing a way for customers to self-nominate (e.g., email campaign + web form). Any customer who “self-nominates” is automatically accepted into the program, and that’s the extent of the onboarding “process.” Whatever way you go, be sure to document your process and follow it consistently so that as your company grows, the program can scale accordingly.
Hopefully, after reading this post, you understand why an arbitrary count or percent growth goal picked out of thin air is meaningless. Goals of this sort will waste valuable time and energy, and in the end you’ll be in no better position to support your company’s reference needs than before. This aspect of running a program is more scientific than many imagine. Educate your manager and executive sponsors on the right way to answer the question, “How many is enough?” It is important for stakeholders to understand that it isn’t purely a number but having customers with the matching attributes. We wish you great success on your mission to have the right reference for each request and showing your executive team what a real reference program can do! Let us know if we can help.
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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