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The Real Cost of Dirty CRM Data
Here’s a riddle. Where can you find $3 trillion hidden behind a few clicks of your mouse? The answer: dirty data.
According to research, dirty data—data that is errored, duplicate, or fraudulent—costs the U.S. economy $3 trillion per year. If your jaw doesn’t hit the floor at that number alone, consider a statistic that may make the cost of dirty data more relatable on the scale of your own business: the average company, according to Experian, loses 12 percent of revenue due to bad data. That’s a hefty chunk of money slipping between your fingers at the expense of a problem faced by nearly every sales team.
It’s also an excellent opportunity to put yourself ahead of the competition if you can solve it.
Where Does Dirty Data Come From?
Dirty data can be attributed to two causes—intentional and accidental error. Intentionally dirty data is fraudulent data, and usually comes from individuals, but more typically bots, entering your funnel with the purpose of misleading you. As bots become increasingly sophisticated in their ability to mimic human behavior, the issue of fraudulent data continues to rise.
Accidentally dirty data comes from errors in entry either on the customer or company side—for example, a person speeding through a form mistyping their email address, or a migration error occurring as data is moved from third-party systems to CRM software platforms. This type of dirty data is also hard to weed out since human error is universal.
How Does Dirty Data Cost A Business Money?
The $3 trillion cost of dirty data is a gigantic number to digest, but it’s made up of many different little costly mistakes. A few common ways that dirty data affects sales operations include:
- The cost of sending direct mail to duplicate or inaccurate addresses
- The cost of housing fraudulent data in your CRM
- The cost of sending inappropriate offers to leads
- The cost of not addressing incorrectly filed customer concerns
- The cost of acting on incorrect priorities
- The cost of poor customer relationship management
- The cost of frustrated team members losing faith in your leadership
- The cost of reduced productivity in weeding through data
The list goes on.
What Can I Do About Dirty Data?
No business will ever be entirely immune from dirty data. As you work to detect fraud, bots will get more intelligent and beat your systems until you outsmart them again, and the cycle continues. The human error that causes accidental dirty data will always be an issue. But there are several ways to drastically reduce the impact of dirty data on your business.
1. Start With The Data You Have
Undergo an extensive scrub of your data. This will require an investment in time, headcount, and software, but getting your existing data as accurate as possible will help establish a quality baseline for the data that continues to stream in.
2. Implement A Data Quality Program
Identify the roles or programs you need to have in place in order to better find and filter fraudulent data; consistently scrub existing data; and house data safely. This part will likely require some help from your IT department, or from an outside consultant.
3. Consider UX Fixes
Run through your customer-facing offers and forms to identify potential problem areas for accidental dirty data. Partner with UX experts to find ways to improve the customer experience and make it more difficult for users to commit errors.
4. Make Data A Priority
Unless our titles have the term in them, most of us don’t think of our roles as data-oriented. But the truth is that anyone who works in sales is in a data-facing role. Get your whole team onboard with the importance of making accurate data a fiscal and scheduling priority.
In sales, data is your most valuable asset. When that data doesn’t get the attention it needs, your customers, your operations, and your bottom line can all suffer. Cleaning up your data hygiene takes a significant commitment, but with $3 trillion at stake, the results are anything but trivial.