Welcome to Dear Wiz, an advice column by sales operations folks for sales operations folks. Each month, we select a “Wiz” to answer your burning questions about the best ways to work, learn, and grow in this quickly expanding field. Have a question? Ask it below.
This month, we went to Trailblazers from the online community Wiz Ops to find out: What’s the best way to clean up customer relationship management (CRM) data?
Jeff Ignacio, the head of revenue and growth operations at UpKeep, says one of the first lessons sales ops professionals learn is: the only good data is clean CRM data. Yet it’s hard to maintain data that’s accessible, comprehensive, timely, and – above all – accurate in your CRM.
What should you do if you just started a new sales ops role and your company’s data is a complete mess? It can be the biggest hurdle at B2B organizations. It also costs money. According to a report from MIT, bad data costs most companies a revenue loss of 15% to 25% per year. Ignacio will show how you can get the right data in the right places.
What is dirty CRM data?
Dirty or bad CRM data is a record that’s irrelevant, outdated, incomplete, duplicate, or simply inaccurate. For example:
- Irrelevant or outdated data could be records about a company that has since been acquired or about a person who has since changed their title or role.
- Incomplete or partial data is where key fields are missing, like industry type or company size.
- Duplicate data, or what I like to call confetti data, involves similar data being in a bunch of different places when it doesn’t have to be.
- And inaccurate data is when fields are filled in improperly or with the wrong information like when someone puts a fake email on a web form.
To sum it up, bad data creates a lot of static with no clear signal. Now let’s dive into how it decreases productivity.
How does dirty data slow you down when it comes to scaling up?
Dirty data has plenty of bad implications for scaling up your sales processes, from productivity to forecasting.
Consider the sales forecasting side. Dirty data can cause you to overestimate or underestimate significantly. For example, if you have an opportunity pipeline with tons of deals with inaccurate close dates, you’ll believe opportunities will close in the wrong time frame. Then sales leaders can’t forecast how the team will perform this month or this quarter.
If you can’t forecast, you can’t plan for capacity. If you get the data wrong, a lot of different things start to go sideways. Messy data has a tendency to compound like that. Pipeline management will also be negatively impacted. Not only will you miss out on correcting risks, you could miss out on new growth opportunities. Each quarter and each year, you’ll see your growth trajectory decelerate.
For the productivity piece, let’s say you’re in sales operations at an enterprise SaaS (software as a service) company. In your role, you want to help sales reps make a higher volume of effective cold calls.
In a rep’s day to day, data can remove a lot of the guesswork, like figuring out the best phone number to reach someone or knowing what to say when you do. Reps need good data to quickly identify the most valuable leads and segment customers into different categories that allow them to personalize their pitches. When there isn’t good data, a rep will end up chasing someone who’s no longer at the company or approach them with the wrong background info about their organization – leading to a poor interaction and, more importantly, no progress on a deal.
I like to use the analogy of silt in a river (silt is sediment in otherwise clear waters, for those who aren’t so outdoorsy). Good data governance and system stewardship are like trees lining the river bank. They prevent erosion from coming into the river and silting it up, helping prospects flow through the pipeline unencumbered – and keeping data clean.
But if you don’t have good data stewardship in place, more and more bad data gets into the river and slows everything down. You incur technical debt, leading to smaller pipelines, decreased capacity, and limited top-of-funnel leads. Ultimately that translates into reduced productivity – and fewer closed deals.
‘Garbage in, garbage out’ is one of the first lessons a sales ops pro learns, and for good reason.
What’s the best way to give dirty CRM data the boot?
So you’re starting a new gig, you walk into the room, and there’s already an established CRM. How do you assess the data’s cleanliness and start fixing problems? Here’s what I recommend:
- Map out the current data process
The first thing you want to do is build a data dictionary to identify what all these fields mean in the system. This is also referred to as mapping. You put the company’s current fields in one column, what the fields should be in a second column, and then possible resolutions in a third column.
- Complete a data assessment
Once you’ve gotten a bit more familiar with the business, do a data assessment and find out which fields are actually meaningful to the broader team and which ones are just collecting dust in your CRM. You’ll need to ask questions like: Can I report on this? Can I cleanse this? Are sales reps using this? Does it drive our desired business outcome?
- Share recommendations with stakeholders
After that, start giving your recommendations to your leaders for fields to add, tweak, or delete. Then training starts, teach all your stakeholders how to maintain the data (whether manual or automated) in their day-to-day roles. You might need to make instructional materials from scratch and host a series of hands-on learning sessions. You’ll also probably need to get buy-in from sales leadership so you have an easier time with the reps. Once reps start to see the value of the new process (better lead scores, more accurate reports), they’ll update more frequently, which will help with overall data hygiene.
Ahh, now you’ve achieved all of the steps above. Your CRM is as clean as a freshly washed dinner plate. How do you make sure it stays that way? Choose your metrics of success and use them to create a bespoke data quality index. If your focus is on getting data that’s more complete, for example, then create a benchmark for that. If you start to waver from that benchmark, revisit the processes you’ve built and see where the breakdown is happening.
“Garbage in, garbage out” is one of the first lessons a sales ops pro learns and for good reason. When you have unreliable input, how can you trust the end results? You can’t. The CRM is an incredible tool, but it can only process what it’s given. Your time spent on data hygiene will be time saved when you’re attempting to scale up your sales processes.
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Have a question about sales ops you want answered? Submit it to Anita Little.