July 29th, 2019 | by

Dirty data is cheap – but good data is cheaper.

All that glitters is not gold, but yet few of us can resist the occasional temptation of things that are too good to be true.

The problem is, sometimes things ARE too good to be true. Lucky breaks happen rarely, but just often enough that we think our turn is next, our number is sure to be next.

The quick buck. A free lunch.

Easy money.

Indeed, a $700 billion gambling industry is structured around the eternal human hope of beating the odds. (If you bet $100 every hour playing roulette, where the odds are more favorable than most casino games, you’ll lose an average of $5.26 an hour.)

And yet, businesses make risky investments every day in hopes of saving a few bucks.

And when the cheap, risky investment fails to pay off, the time and cost to repair or make up for the bad investment, usually far outweigh the more expensive option. That’s true of any investment, from cheap stocks, to cheap shoes, to cheap data. The truth is, cheap data is expensive.

Luck ain’t a business strategy

Yes, people get lucky sometimes. No, that’s not actually a strategy.


In the world of “too good to be true,” penny stocks are a good example. If you’re investment strategy in penny stocks is: “You heard from a friend, who heard from a friend, that someone they knew made a huge return on playing the penny stocks, and this one (insert penny stock here) is going to the moon!”

… Except that’s not usually how it works out.

It’s the same with data. Look at user stories from tried and true providers. When you pump good data into your systems, it works. You don’t have to question every phone number, or cross your fingers when you hit “Send” on an email campaign going to thousands of people. Good data is a solid investment. The risks are minimal.

What are the cost and risks of bad data?

Glad you asked.

Cheap lead lists ain’t so cheap

It’s expensive to be cheap. Cheaper products are usually lower in quality – whether that’s buying boots from a discount chain, dollar-store dish soap, or dirty data. While it’s not always true that higher price correlates to higher quality … the reverse usually is.

bad-data-bad-strategy-low-qualitySuppose a discount shoe store is having a sale, and you buy cheap hiking boots. Within a few months or a few hikes (depending on your level of enthusiasm for the outdoors), the soles peel off, the pleather cracks, and your new boots begin to leak.

To keep hiking, you’ll have to buy new boots. This means spending more money, in addition to the money you’ve already spent on boots. 1.) You can either keep re-purchasing cheap, low-quality boots every few months, or 2.) buy higher quality boots. Yes, they’ll cost more initially. But they’ll last years, they’re definitely more comfortable, and since they’re real leather, you can resole them. In fact, they probably came with a warranty.

Either way, the cheap route is a sunk cost that adds no value and can’t be recouped. The higher quality choice often ends up costing less in the long run.

Take data: There are a lot of options for third-party lead data and sales intelligence. And there are a lot of CHEAP options.

Digging out of the dirty data hole

Emails are critical for outbound marketing


It might seem better to buy cheap data and just deal with the occasional errors, especially if you don’t have much budget or are spinning up a new sales team; after all, something is better than nothing. So you spend the money and by a lead list with people that fit a particular target accounts.

Great. You got it. Now you pump that information into your CRM. But once it’s actually in your system, you experience:

  • Email deliverability problems
  • Poor connection rates
  • Outdated titles and phone numbers
  • Incorrect email addresses
  • Blacklisting
  • Dead-ends and gatekeepers

Um, about that shiny new lead list…

Email deliverability

Don't get caught in a spam trapYou’re trying to ramp up, fast, and nothing brings in leads quicker than an outbound email campaign – so you drop those contacts into your CRM, and maybe a marketing automation tool (MAT) like Marketo, Outreach, or Constant Contact. …and the bounces start to roll in.

  • When the bounce rate go above about 5%, some email sending platforms lock you out of your email for 24 hours.
  • When the bounce rate gets higher than that, you’ll have problems with email deliverability even for legitimate addresses. (We’ve heard of bounce rates as high as 60%!)
  • When the bounce rate gets to 10%, your domain is blacklisted.

…Oh, you were sending emails from your company domain? Now everyone in the company with an email address from that domain – from the CEO to the admin – is prevented from sending even 1:1 emails, from any account tied to your domain. That sucks.

Even if the bounce rate from cheap data doesn’t quite get that high, most lead lists contain inevitable spam traps. Derek Smith, our Senior VP of Data & Research, has a great discussion of spam traps.

The purpose of a spam trap is to identify spammers and senders with poor emailing practices.

But sometimes spam traps find their way onto the email list of a legitimate sender by way of bad data hygiene, and the consequences can be severe: Senders may find a significant drop in email deliverability, and entire domains may be blacklisted … preventing even operational or one-on-one correspondence with customers and prospects.


Once the bad data is removed from the system and replaced with something more accurate, it can take two years or more to recover from bad email deliverability and a damaged domain.

The effect of bad data on cold calling

Beyond email, your sales team also suffers from bad data.

Cold-calling is hard during the best circumstances. But it becomes especially hard to maintain enthusiasm when you cold-call all day hearing things like:

  • “I’m sorry, she left the company back in September.”
  • “That person hasn’t been in that role since last year.”
  • “Where did you get that information? Do your research before you call me!”
  • “I don’t know anyone by that name … and no, I’m not interested.”

sales researchFor sales development reps with quotas tied to appointment-setting, a low rate of connection means time wasted: double-checking CRM data; navigating around the phone tree of a main line, trying to to find the right decision-maker; and, of course, getting wrong numbers.

Bad data can become an organizational cancer.

When reps don’t trust their contact data, they’ll spend time double-checking it – or disregarding it all together in favor of their own research. Besides being disheartening and frustrating, bad data causes ineffective sales workflows, and makes smooth, timely hand-off of leads it nearly impossible.

When you’re paying a seasoned sales professional $45,000 per year to do prospect research, you’re doing it wrong.

After organizations have had to deal with the sunk cost of email deliverability and stymied sales operations and decided to switch to better data, they still have to make an investment in data – whether that’s purchasing something more accurate, or hiring researchers.

An experiment by Brian Carroll, CEO of Markempa, revealed that, in a month of calling, the cost per lead with the most expensive list was $373; the cost per lead with the cheapest list was $954. The cheaper list was actually 3x more expensive – just in the first month! (And this study doesn’t consider the costs associated with email deliverability issues.)

It’s expensive to go cheap.

Bad data is cheap, but good data is cheaper

invest in good dataThere’s a big difference between a cost and an investment. An investment is a future-based decision, spending resources based on anticipated long term results. An expense is spending resources for a present-based situation.

Sometimes it’s possible to turn an expense into an investment – that is, leverage the money you’re already spending on a current problem to return a future benefit.

The COIT Group learned this as they transitioned from inbound-only to a mixture of inbound and outbound tactics. (Our own extensive research clearly shows that high-growth companies rely on an blend of inbound-outbound techniques.)

Coit Group CEO Joe Belluomini oversaw the onboarding of good data into COIT’s sales and marketing systems. Using a “single source of truth” – good data – the company developed and executed an ABM strategy on 8,000 contacts at 1,500 target accounts. It included a single Survey-Based Lead Generation play that generated $2.5 million in pipeline in less than three weeks, plus new response data from 75 target accounts that are now building pipeline in a nurture program.

In the case of the COIT group, and so many others, an initial investment in great quality data is an investment in serious ROI.

The Iron Triangle, or “triple constraint” of any endeavor, states: A project has the qualities of low cost, speed, and high quality. Pick two.

Or to put it another way: There’s no such thing as a free lunch, buddy.

But you have to eat, so spend your lunch money wisely.

See how great data creates long-term gains – get a free sample today


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Franklin Bear
About the author

Franklin Bear

Franklin Bear is the Manager of Marketing Operations at DiscoverOrg. From building nurture programs to monitoring deliverability to doing analytics, he eats, sleeps, and breathes Marketo. Franklin earned his BA in History from Washington State University.