UncategorizedData Integrity: How to Achieve It and Why Your CRM Is Worthless Without It Many businesses adopt a CRM in hopes that it will allow for smarter decision-making, but relatively few of them realize that the return on this investment is entirely dependent on data integrity. Your CRM is only as good as the data you put in it. Nov 15, 2016 If you make it a priority to collect detailed and accurate data, and do it consistently over time, you will continue to reap the benefits of CRM for as long as your business is running. You will be able to rely on your CRM’s database for any and all business decisions, because it will provide you with actionable insight derived from accurate data for every account, lead, and contact, and for every customer engagement that has taken place. If you don’t ensure this level of data integrity, you’ll end up with paying for it—literally. Bad data—whether it be missing data, incorrect data, or duplicate data—incurs real costs, and these costs continue to increase over time. The 1-10-100 Rule To actually measure the relative cost of bad data over time, look at the 1-10-100 rule. Developed by George Labovitz and Yu Sang Chang in 1992, the 1-10-100 rule is widely used to evaluate efficiency. In relation to data quality, the rule operates as follows: The cost of preventing the entry of bad data into a CRM is $1. This is known as the prevention cost. The cost of correcting bad data in a CRM is $10. This is the correction cost. If bad data resides in a CRM over time and is never cleansed or deduplicated, it inevitably results in failure of some kind—either with a customer or with internal business operations—and costs a business $100. This is known as the failure cost. To put things in perspective, consider the fact that on average, 32% of CRM data is inaccurate. So, if your CRM has 50,000 prospect accounts, it will cost your business… $160,000 to have this data cleaned and/or deduplicated. $1.6 million if the data goes untouched. Yep…pretty scary numbers given how easily this can occur. The key takeaway from this is that when it comes to ensuring data integrity, the sooner you take action, the better—and if you can prevent bad data, it’s critical to do so. Automation: The Key to Data Integrity Take a second, if you will, to think about how bad data ends up in a CRM in the first place. Sales reps are given the responsibility of entering pertinent data into their CRM after every conversation they have with a customer. They log calls, update fields, take notes, send emails, leave voicemails, etc. The problem is: reps are also given the responsibility of meeting their quota. When you consider the fact that only 32% of a typical sales rep’s time is spent on actually selling to customers, making sure that all data is accurately reported after each and every call becomes a real hassle. From the rep’s perspective, it actually becomes counterintuitive. So it’s no surprise that according to Bluewolf’s The State of Salesforce 2017 report, 27% of reps admit to entering opportunity data into their CRM only to meet their reporting requirements. Add this to the fact that 79% of sales reps have to input their data into multiple systems, and you’re left with a high risk for human error—or just simple disregard—and poor data integrity that can cost your company hundreds of thousands of dollars (or worse). The absolute best way of avoiding this significant waste is through automation. With DialSource’s Post-Call Automation, reps save about 5 minutes for every call, have more time to talk to customers, and avoid insanity to boot. This is because with a click of a button, reps can log a call, update custom fields, auto-convert leads, send personalized voicemails and emails, and essentially have an unlimited number of customizable workflows triggered as they immediately transition to the next call. The Moral of the Story With all things considered, there is a direct relationship between building your CRM and building your business. Ensuring data integrity should be of the upmost importance. It’s what separates the smart, data-driven companies that are primed for growth from the companies that fail to grow—simply because their decaying database wasted a significant amount of revenue.