Don’t Accept Bad Data – No One Else Does

“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:

  • We don’t have reliable customer advocate data today
  • If Marketing, Sales and other advocate-dependent team members can’t find what they need in the company’s source-of-truth, they’ll go hunting across the organization
  • This is a colossal waste of time, and there’s a good chance they’ll come up empty-handed
  • Customer advocates have more influence on buyers’ decisions than anything else
  • We reduce our odds of Marketing and Sales success with bad customer data
  • The power of AI is dependent on reliable, up-to-date data across the organization. Garbage in, garbage out has never been more true.

The Mechanics

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.