Join Maestro’s Head of Revenue Operations, Kenn Miller, as he shares his “ops chops” and helps you sharpen your own. From new tools to helpful tips, Kenn keeps us in-the-know in the world of revenue operations.
June 14, 2023
By Kenn Miller
My job at Maestro is often to conduct sales-operations audits for our clients. This involves reviewing an organization’s CRM, analyzing its technology stack, and looking at what kinds of reports are getting run and how easy it is to pull the information needed.
I then make recommendations as to how an organization’s sales-ops strategy can better align with its sales goals. I suggest which CRM tool will best meet their needs, which other tools would best round out their tech stack, and I provide roadmaps for teams to better align their sales-ops strategy with their sales goals.
Tools are great (I love them). But poor revenue-operations oversight can’t be addressed by choosing the right tools. The problem is the information being fed to these tools on the front end. The biggest sales-ops challenge our clients face is dirty data.
Sales-ops people like me throw around terms such as “data hygiene” and “data cleanliness,” but what does that really mean? And why does it matter? We all hear about how valuable data has become, but the truth is that data on its own has no value. It’s only what can be done with the data that produces true value.
The data being entered into your CRM isn’t valuable until your organization uses it to make informed decisions. What can the data tell us about where we lose sales, what characteristics our best clients have in common, or which messages got the highest response rates? The problem is that to get quality insights, you have to start with quality data.
I often tell people that “garbage in equals garbage out.” If your data is lousy, your insights will be just as lousy. So, what kinds of things taint data? Duplicate records, incomplete records, old email addresses, misspellings, unsubscribed users, and corrupted entries are all examples of poor data hygiene.
Think of your organization’s data as your house. Parts are clean and parts are dirty (unless you have a baby like I do, in which case the whole thing is likely dirty). Unfortunately, also much like your house, you can’t clean your data once and then be done forever. Cleaning is something that must be done periodically to maintain a healthy environment.
We all know how houses get dirty (babies!), but what about data? While there are a number of things that can lead to poor data hygiene, the biggest offender that we see is a lack of an established process and process controls (a topic for a future blog). To get clean data, establish rules and standards that apply to everyone.
Is your sales process codified? If not, how do people know when a deal should be progressed to the next stage? That might not seem like a critical detail but if you’re trying to find out where deals are getting lost, and everyone is defining stages differently, pinpointing the issue becomes difficult.
Have you standardized how people enter data? Are you spelling out the word “street” in an address or using an abbreviation? Again, while it might not seem critical, it can easily lead to poor insights. Your data shows that most of your clients have more than one business location, causing you to alter your ideal customer profile to larger businesses with more than one location. But in truth, most of your clients have more than one location because “Green Street,” “Green St,” and “Green St.” show up as three different addresses.
Business units updating siloed data, poor access control, and unclear ownership can also contribute to data uncleanliness. Organizations with good data hygiene rely on clearly articulated roles and processes that are consistent across the company.
When data on clients and prospects is wrong, properly segmenting clients and prospects gets harder. Improper segmentation leads to sending people information that’s irrelevant to them, which leads to decreased open and engagement rates.
How can you optimize your sales process if you don’t know what happens at which stage? How can you properly perform lead scoring? Assessing marketing efforts, refining your marketing strategies, and proper segmentation all become suspect when your data is dirty.
Perhaps worst of all is that when your data isn’t clean, your CRM users won’t trust it anymore. Marketing will stop using it to make decisions at all, and sales will feel like entering data is a waste of their time. So, if you’re one of the many organizations who needs to do some data clean-up, where should you start?
All is not lost. Just as my kitchen will not always be a mess of bottles and pureed food (I hope), data can be cleaned, and processes can ensure cleaner data in the future.
Your first step is to clean up the data that you already have:
The next step is to put consistent processes into place:
One of the most exciting aspects about cleaning up your data is that there are now so many tools out there that can help you! But that’s a topic for another blog.
Need more help with your sales ops processes before the next Ops Chops blog comes out? Contact us to learn more about Maestro’s sales ops audits and assessments at mastery@maestrogroup.co.
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