It’s a direct mailer’s worst nightmare: when data is not only outdated, it’s mismatched, and no one figures it out until it’s too late.
A nonprofit was sending out their annual end-of-year solicitation letter, trying to reach 20,000 people around the Midwest. All the letters went out on time without a hitch. Then the calls started coming a few days later.
“I got your letter, but my name is wrong. You put someone else’s name on my letter.”
“I got a letter at my house, but it was for someone who has never lived here.”
“I received your solicitation in the mail, same as every year, but it wasn’t addressed to me.”
It was a disaster. At least two-thirds of the letters had been sent with the wrong name on the address.
Apparently, the nonprofit had used data from three different sources, and when they tried to merge them into one Excel file, something got bumped, and the address line bumped down a single row, so everyone’s name was now lined up with the previous mailing address. At least 12,000 letters were sent to the wrong address.
Don’t worry, the problem was quickly fixed, and the letters were re-sent to the right people, and the nonprofit still met their year-end goals.
But this could have been an ugly situation just because of dirty data that wasn’t properly cleaned, purged, or matched.
The Cost of a Bad Data Strategy
While this wasn’t one of our clients, this was a perfect example of what can happen when data is badly matched and managed. Companies will often rely on old data that hasn’t been updated in a few years, or use data that wasn’t properly matched between sources, and it can have some embarrassing results. It can erode trust within your company and with your potential customers who aren’t quite who you thought they were.
Eventually, you question the data itself and are frozen with indecision or waste a lot of time cleaning up the data to make it usable. What does that company do? Who is the primary contact? Is the technographic data accurate? Is that person still the department head?
Even if the overall data is filled with good leads of companies that could make good customers, just a small percentage of bad data can make you question the validity of the rest of it. Then it doesn’t matter how good the data is, you don’t know which records are worth following up and which ones need to be cleaned or trashed.
With bad data, you’ll end up chasing people who won’t ever become customers — they aren’t a good fit and have no intention of ever buying. (It’s especially embarrassing to discover that after you got them on the phone!)
The sales department will learn not to trust the marketing department after providing terrible leads that end up crashing their closing rates and reducing their revenue. And all of that will reduce your ROI and your company’s total profits.
Bottom line: Bad data hurts your bottom line.
Two Horns, One Bull: Coverage versus Accuracy
So now you’ve agreed that you need to improve your data strategy, but now the choice comes – coverage of data, or accuracy of data?
Vendor one may provide you with many records that have low accuracy, and vendor two provides you with high accuracy but tremendously lower volume of records. Which one should you choose?
Well, if you had to choose, you should go with the second vendor. In this scenario, at least you’re getting good data and you aren’t wasting a lot of time on ineffective or nonproductive leads. But you may not have enough to keep your sales department busy.
Good news! You don’t have to choose between accuracy and coverage. It’s possible to have many leads that are accurate, well-researched, and provide deeper (and wider) coverage for each contact.
Thanks to artificial intelligence and machine learning, combined with human-powered research, you can find a wide array of records that will keep your sales department busy and give you the accuracy you need to keep them happy.
Ready to learn more about how to combine wide coverage with accurate data? Check out our Enterprise Guide to the B2B Data Revolution and learn how to solve for an ineffective data strategy.