“Our customer data is crappy.” We hear these words all too often from big companies, small companies; new companies, old companies. It seems to be the great equalizer. But what a crazy thing to just “accept” as a lost cause.
If the payroll data were “crappy,” would that remain so for very long? How about sales data? Contract data? When is the last time you heard about a clean-up project surrounding one of these data sets that was not considered a top priority?
Customers are at the center of our existence as businesses. Why would we think, if one database can slide, it’s the one that informs us about our customers; our bread and butter?
The challenge for Customer Marketing is knowing all the changes occurring on a monthly—heck—weekly basis in that customer data. Our customer advocate rockstars move on to new roles and new companies on a fairly regular basis. To maintain current and accurate data it takes a village. That village, most importantly, includes Customer Success, Account Management, and Sales: customer facing relationship managers. An operationally mature organization will include data maintenance as a performance measurement criterion for those with essential relationship insights.
For customer marketers, it’s not necessarily all customer data, but the information specifically about advocates that the program lives or dies by. This is a less daunting task as this portion is probably no more than 20% of the total, despite, it seems, our best efforts to increase that number by double or more.
Leadership Buy-In
Like most cross-functional processes in companies, leaders need to put their heads together, decide on mutually agreeable objectives, and communicate a common message to all the essential participants. As a first step, leadership needs a framework to get behind. Here’s the gist:
Ideally, the moment a relationship manager, such as a CSM, learns of a change within an advocate account, they record that information on the account, contact, or both. But in the absence of that level of data diligence, a periodic review reminder is needed. The more frequent, the fewer the number of changes that will be required. The list of updates is longer if those reminders extend quarterly or beyond. Additionally, there’s a greater chance that a user attempts to access outdated information and loses confidence in the data as a whole. We think every 2 months is the maximum cadence.
The most efficient customer marketing programs establish automation that use the most current data from the CSMs and update the customer advocate accounts and contacts in real-time. This is great for the relationship owners as they don’t have to go to multiple systems to update the data. They can stay in their own environments such as LinkedIn, ChurnZero, or Totango, benefitting from a steady feed of reliable information. Imagine one of your top advocates suddenly experiencing a health score drop. You don’t want anyone to find that account and ask them to advocate if they aren’t in a good place. Automation can save a lot of misfires like this.
The Takeaway
If you’re solely responsible for up-to-date advocate data, that will drive you insane. Recruit leadership and peer managers from the departments that have account visibility and educate them on the cost of having crappy data. The good news is that smart executives will soon realize that they will not be able to show the board how they are capitalizing on AI—enhanced customer experience, operational efficiency—unless data quality improves across the board. It really must become a shared corporate responsibility, maybe for the first time. AI is only as good as the data in it’s data model. The winds of change should sync your data quality objectives with the larger initiative reinforced by leadership muscle. Solving this common issue will lay the foundation for success when it comes to user adoption, advocate influence on lead gen and revenue, and at the end of the day, your company’s competitive position. Contact us today to learn how we can help protect and strengthen your advocacy program.
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.
.svg){kind=icon}
