Are You a Change Junkie? Most people are not, and that should influence how you approach many aspects of your customer advocacy program.
We had a really terrific conversation this week with our Change Champion customer, Meagan McAlexander, from CentralSquare Technologies. The objective of these conversations was to capture the specific elements of Prosci’s change management model, ADKAR (Awareness, Desire, Knowledge, Ability and Reinforcement), that produced change success for advocacy program leaders, and then share those learnings with our community.
On the topic of Desire, addressing any obstacles that cause people to resist change, Meagan identified change overload as a significant culprit. We all feel the constant drumbeat of change in our personal and professional lives. Yes, the rate of change has been increasing with each passing year. Arguably, the magnitude of change is increasing as well.
How does this relate to change specific to customer advocacy programs? The largest stakeholder group of CMA programs by count are salespeople, followed by marketing and customer success. Each of these groups are bombarded by new processes, new technology, new work environments and new co-workers. There’s no way of avoiding it, it’s the new normal. Program leaders can’t change those macro conditions, but that doesn’t prohibit them from being a change management force.
What advocacy program leaders can do is manage change better than it’s being managed by other leaders in the organization. It’s a healthy competition, like the pursuit of mindshare in the form of customer engagement if you run any aspect or form of a community.
So much of competent change management begins with empathy. That can be said of pretty much every aspect of life. Putting yourself in the shoes of another gets to a level of understanding that circumvents wasted time and energy on actions and behaviors that raise defenses and objections. When we feel heard and understood so much can be accomplished. That is the basis for a good relationship. Good change management is a proven way to begin a beautiful working relationship!
If you’re introducing a new customer advocacy program to your organization, begin by meeting with samples of your stakeholder groups. Ask them questions that will help you understand how they, and their co-workers, will react to the changes you’re planning. Learn what information will be helpful to share, what past experiences—with initiatives involving change—were like; what worked and what was lacking. Anticipate, anticipate, anticipate! Recognize that your initiative is being judged, at least in part, by the successes or failures (more likely) of prior company initiatives that didn’t pan out. Studies show that over 70% of company initiatives fail due to poorly executed change management.
Change management is not a project, just as customer advocacy is not a project with a defined start and end. It is woven into everboarding, which we’ve written about previously. Program launches require broadly applied change management in terms of ADKAR to reach future state and prevent regression to the before times. But there will always be new hires and changes in the environment that will require micro-targeted application of the ADKAR principles. Training (i.e., Knowledge & Ability) may be lacking, for example. And then, people also just plain fall off the change wagon from time to time. Count on it, be vigilant.
Effective change management is crucial for the success of any initiative; customer advocacy programs are no exception. Program leaders must prioritize empathy and proactive stakeholder engagement to effectively manage the transition from the old way to the better way. By understanding and addressing the unique challenges and experiences of stakeholders, advocacy initiatives will thrive despite fluctuating environments. Be better than all those other failed initiatives littering the road behind you. For more resources on how to improve user adoption through effective change management, check out these podcasts.
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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