Data is one of the primary drivers of business strategy and projection, however, many decision makers don't fully understand how inaccurate data collection and poor data maintenance can negatively impact their marketing, sales and bottom line. According to a recent report, bad data accounts for trillions of dollars spent annually by U.S. businesses, and unfortunately, bad data's impact doesn't end there:
According to the 1-10-100 data quality principle, the relative cost of fixing a data error increases exponentially over time, which is likely how it's reached the $1 trillion mark by now.
If we apply that principle to customer relationship management (CRM) systems, the cost of preventing bad data from entering a CRM system is $1, the cost of correcting existing problems is $10, and the cost of fixing the problem after it causes a failure, either internally or with a customer, is in the neighborhood of $100.
As of 2018, email lists are decaying at an average rate of 22 percent each year.
According to Sales & Marketing Institute and Dun & Bradstreet, every 30 minutes, 120 business addresses and 75 phone numbers change, 20 CEOs leave their jobs, and 30 new businesses are formed.
Bad data sneaks into the CRM -- whether it's data that is missing, inaccurate, entered into a wrong field, non-conforming, or duplicated -- and slowly deteriorates the system's value while increasing the cost of remedying a culmination of small data errors. For example, poor data quality dramatically impacts marketing efforts, with stale lists decreasing deliverability rates and increasing spam labeling.
These simple missteps can mean the difference between a successful campaign generating hundreds of thousands of dollars, and one barely reaching your target audience and creating engagement. Having high-quality data in your CRM is key to delivering positive experiences and increasing revenue. With accurate contact information, such as validated email addresses through a third-party email verification tool, and data that is regularly cleansed and standardized, you stand a better chance of reaching your customers in a timely fashion with news, product promotions, and upsell opportunities.
It's not only important to look at data quality's impact on the results of various business sectors though, but its impact on the adoption of the system overall and what that costs a business.
Bad data can impact user adoption in the following costly ways:
The Impact of Bad CRM Data
1. It limits the ROI.
This easy CRM calculator determines that for 50 users over a three-year period and factoring in administrators and add-ons, a company's spend for the CRM is close to $400,000. That's completely wasted if no one trusts the output.
As a sales rep wastes time searching through duplicates looking for the right account, they are less likely to adopt the CRM. Or if data is missing or inaccurate, making it harder to reach contacts, they may opt for other methods to access information.
2. It can fluctuate too much.
The key is to aim for consistency. If the data quality is consistently robust, it builds trust in the data and the teams that need to leverage that data will start to see it as trustworthy. In turn, they're more inclined to use the data to make business decisions and forecasts, making their jobs easier and their teams more effective.
A marketing rep experiencing inaccurate segmentation due to missing or inaccurate data or email addresses will experience a massive bounce rate and have limited trust for the system.
3. It fosters bad habits.
This is across all roles. When a user doesn't trust the data based on experience, he or she will make little effort to ensure the data entered qualifies as "good." But, when the data is good and trusted, users won't be as likely to input bad data. They won't want to "muddy the water."
A manager who depends on CRM data for accurate forecasting/pipeline data is getting inaccurate reports due to duplicates, missing, and inaccurate data. The manager may turn to external resources for data, sending a message from the top down that the CRM isn't useful.
Luckily, there are ways to minimize bad data by instituting a clear process for achieving and maintaining high-quality data. The result is a well-oiled CRM machine, filled with reliable data that fosters respect for the system and the data quality processes the administration put in place, and high user adoption across sales, marketing and other business units.