Everyone claims to be “data-driven.” Heck, it’s hard to find a resumé or LinkedIn bio these days that doesn’t contain this phrase.
But it’s the quality of the critical business data steering your decisions that determines if you’re driving in the right direction—or off a cliff.
When organizations don’t show their CRM data the love and attention it deserves (by keeping it clean and up-to-date), they’re left with low-quality data that results in even lower-quality business outcomes.
But you don’t have to suffer these consequences. Let us help by sharing poor data quality examples so you can more easily spot them in your CRM. We’ll also explain how to implement better data hygiene practices so that your team is only working with high quality data.
Keeping an eye on your customer data quality and conducting the necessary upkeep will ensure you target the right customer with the right message at the right time. There are many ways that poor quality data may creep into your CRM, and you need to know what it looks like.
Here are just a few examples of poor data quality in business CRMs:
Duplicate data happens when a customer appears multiple times in your CRM, potentially with different information stored under each profile and different reps assigned to each “different” account.
Or, when multiple customers have the same contact phone number or email address. You need to merge all duplicate customer profiles and details to ensure you’re working from a shared set of data and are not reaching out to a customer multiple times, or failing to use their complete details when contacting them.
Sometimes your data is wrong simply because your representatives are human and make mistakes when logging information. Or, data becomes stale as your customers change jobs, companies merge, and people move or change numbers.
Incorrect data (sometimes called expired data) can have a significant impact on your team’s productivity and tarnish your customer relationships if you fail to update it. Look out for misspelled customer names—Erica vs Erika, for example—and outdated or mistyped email addresses, phone numbers, and addresses.
Customers expect (and deserve) a highly personalized experience when communicating with your brand. Sometimes data is missing because customers forget to fill out a specific line on a form, or you never asked for certain information.
If your CRM is missing important information, like where a customer is located, you may be sending emails—or, even worse, making phone calls—incredibly early or late in their day. Missing data can also cause issues with your sales or marketing automation tools, which may leave placeholders in emails because the tool is unable to pull the necessary information from your CRM.
High quality data should be the standard, but we’ve found that brands unfortunately set the bar far too low when it comes to data quality.
In our State of CRM Data Management 2022 report, more than half of the CRM admins we surveyed rated their CRM accuracy and completeness at less than 80 percent. Many also blamed their data for a wide range of negative business outcomes (we’ll get to those soon).
Clearly, organizations need to hold their data—and those who manage it—to a higher standard and strive for cleaner data. If not, you can pay a steep price for neglecting your data quality and letting poor quality data overrun your CRM.
“What are the business costs or risks of poor data quality,” you ask? These are the top five ways CRM data quality issues could be sabotaging your business:
Poor quality data can have a major impact on team productivity. For example, take the stat we mentioned earlier: the quality and accuracy of the average CRM is less than 80 percent.
Now, let’s do some basic math:
That’s only for one SDR. Take this number and multiply it across other users and departments, and the time investment and productivity loss becomes staggering.
Depending on how your CRM data is managed, it can be a goldmine or a money pit.
Sometimes it’s hard to draw a straight line from data quality back to revenue. As such, it’s easy for leadership to push data quality far down on their list of priorities.
However, 44 percent of study respondents estimated their company loses over 10 percent of their annual revenue due to poor quality data in their CRM—as seen through lost customers, blown new business deals, and delayed revenue-driving initiatives like marketing and brand awareness campaigns.
Don’t let this direct cost of poor data quality hold your company back.
In this climate, employees have limited patience for working with dirty data—especially when it stops them from meeting their goals.
Every member of the team relies on different data points, from a sales representative using up-to-date contact information to reach a prospect, to a marketing professional looking for a customer’s address to create a segmented email list.
A database riddled with duplicates, incomplete, missing, incorrect, or expired data can make it nearly impossible for employees to perform well. For some employees, these stressors might be the push they need to walk out the door, and rightfully so.
If your organization won’t invest in data quality improvements, it’s easier than ever for your employees to find an employer who will. Sixty-four percent of study participants say they would consider leaving their current role if additional resources were not allocated to a robust CRM data quality plan.
The relationship between sales and marketing can easily grow contentious.
Oftentimes, it’s the same old story: the marketing team says, “We’re delivering all these MQLs, leads, inquiries, etc., and sales never follows up on them.” Then the sales team says, “Yeah marketing, you deliver these leads, but they stink. I couldn’t sell these people a glass of cold water in the desert!”
These problems only get worse when data quality is an issue.
Our study found marketing professionals were 155 percent more likely than their sales counterparts to say that their sales forecasts were ”inaccurate” or “very inaccurate.” Unsurprisingly, we found that one of the main culprits behind inaccurate forecasts is low-quality data.
It’s easy to see why marketers are frustrated. Marketing teams need accurate forecasts to plan campaigns and activities that support these pre-defined targets. But sales forecast accuracy is heavily dependent on the data in the CRM, including the information sales representatives input to predict when/if deals will close and what stages certain deals are in.
Sales and marketing teams struggling with data quality issues have a huge opportunity to put their collective power toward solving their data challenges. By doing so, they’ll see data become a bridge that unites these teams, not a wall that keeps them apart.
When organizations fail to make data quality a priority, employees can develop an undesirable relationship with their data.
A whopping 76 percent of respondents said employees “sometimes” or “often” manipulate data to tell the story they want decision makers to hear, and 75 percent even fabricate data at the same frequency. (Our jaws dropped too.)
How does this happen? It’s safe to assume CRM users don’t make up data on purpose. But we’re only human—when we make a hypothesis, it’s natural for us to be biased toward data that backs up our assumption.
In organizations with data quality issues, these biases become more pronounced. When folks already lack confidence in their data, it becomes all too tempting to hunt through the CRM to find evidence to support their points. Improving your business processes to reinforce the need for high quality data, however, will strongly discourage the manipulation or fabrication of data, and much easier prove when data has been altered.
There’s no point in dwelling in the doom-and-gloom of how poor data quality is hurting your business, and it’s clear that ensuring good quality data (or better) is essential to keep your team happy and effective.
It’s time to create an environment that guarantees you’re working only with the best data possible. Here’s how:
Each of the issues we explored is a strong warning sign that many organizations need better data governance. They need to get real about how they’re using data, where it comes from, and what data points they’re using to create each report.
Develop a data governance program, which will likely include a data governance team, to create standards around how data should be collected, reviewed, and updated. Adding structure to this process and instilling its importance in everyone who uses your CRM will help you avoid the issues of poor quality data.
Every CRM needs a good scrub. Data cleaning is an essential way to reduce or eliminate issues within your datasets and ensure poor data quality is addressed. We explain what data cleaning is, and five steps for cleaning your CRM data, here. Plus, you can find 10 tips to improve Salesforce data quality here.
Using well-built CRM software can help you avoid many data quality management issues and help your reps more easily input customer details and access the information they need. Ask your reps whether your CRM software is empowering them, or if it’s adding hurdles they consistently have to overcome. Seek to understand the obstacles they face, and consider implementing a new CRM that’s better built to improve your business processes.
CRM stakeholders are becoming more aware of the issues of poor quality data. Luckily, there are steps you can take now to start fixing data quality issues and improve your CRM / Salesforce data management.
To learn more about why and how CRM users should raise the bar for data quality, read through our full State of CRM Data Management in 2022 report, which features insights from over 1,200 CRM admins from 606 organizations.