The Dirty Little Secret About B2B Data

October 10, 2017 6:32:53 AM | By Matt Benati

B2B Data Cleansing Solution

When good B2B data goes bad, it can be downright embarrassing. Using erroneous names or titles in your messages, sending to the wrong contact within an organization, or even missing the memo when an account went through a merger — simply isn’t good for business. Dirty data can soil your brand’s good name, damage your email sender reputation, and be the equivalent to tossing opportunities into the garbage. In fact, ZoomInfo found that bad data costs US businesses more than $611 billion annually.

And here’s a dirty little secret about B2B data… if you aren’t maintaining your database, your data could be decaying at an incredible rate of up to 70% per year. As alarming as that statistic is, what’s more alarming is the lack of action many businesses take to keep their data clean. According to the same research by ZoomInfo, 94% of businesses suspect that their data is inaccurate, but 30% of organizations have no strategy for updating incomplete or inaccurate records, or simply leave those bad records in their database.

Yes, ongoing database maintenance can feeling like an overwhelming task. But it’s a necessary chore for the health of your business. Fortunately, technology is on your side. Let’s take a closer look at dirty data, and then I’ll share the one simple tool you need for keeping your data squeaky clean.

What Is Dirty Data, And Where Does It Come From?

Dirty data is inaccurate, incomplete or just plain wrong. You may have started out with a clean database, but the moment data is created it begins decaying. Why? Because things change.

Here are some of the most common changes that occur within your accounts and leads that result in dirty data. ZoomInfo research puts a number to a few of the natural factors you might suspect, such as job, email and phone number changes.

People experience change:

  • Name change – for example, due to marriage
  • Email change – 37% change annually
  • Job title/role change – 66% change annually
  • Phone number change – 43% change annually
  • Job change – 30% change annually
  • Move offices
  • Resignations and terminations
  • Retirement

Organizations experience change:

  • Merger and acquisitions – 34% of companies change their name annually
  • Email domain changes
  • Phone number change
  • Offices open and close

State and local governments experience change:

  • Zip codes and area codes change
  • Streets renamed

And let’s not forget simple human error. When records are manually entered or changed, one wrong character can render the contact unreachable.

The Ramifications Of Working With Dirty Data

Dirty data is kind of a big deal. ZoomInfo found that as much as 40% of business objectives fail due to bad data. They’re also the mind behind the 1-10-100 rule and the cost of bad data — that it costs $1 to verify a record as it’s entered, $10 to scrub and cleanse it later, and $100 if nothing is done. With as much as 50% of IT budgets being spent on data rehabilitation, it’s easy to see how dirty data costs businesses so much each year.

Let’s take a closer look at some real-life examples for perspective.

Left the Company – When a lead leaves the company (as 3 in 10 people do every year) it is nearly impossible for Sales Intelligence (database) vendors to keep up. They look for press releases announcing the new hire of a VP or CXO level person (which misses movements/changes within the rest of the organization) or scroll through LinkedIn looking for updates (which isn’t an effective or efficient use of time).

Change of Title/Role – Keeping up with internal changes within accounts is critical. If your user gets promoted to your buyer, you want to change gears and begin sending them content relevant to their new position. Again, a database vendor is only going to find information on high-level promotions. What about other influencers within the organization that may play a role in a buying decision?

The One Simple Tool For Keeping Your Data Squeaky Clean

Relying on a database vendor can be inefficient given modern automation technology. The BEST source for clean data is straight from your leads themselves. But let’s get real, no one has time to call every lead in their database.

That’s where we come in. An automated reply email mining service, LeadGnome looks at every reply that comes back from your email marketing campaigns – for example Out-Of-Office (OOO), Left-The-Company (LTC), Unsubscribes – and mines them for data that can be used to enrich and cleanse your database.

In fact, Emily Dick, Director of Marketing for QuickMobile, commented on a recent webinar “We say ‘Power of the Gnome’ when alerts or other actions are triggering.” She said LeadGnome benefits both sales and marketing, saving their teams countless hours manually mining emails while providing valuable insights into lead and account activity.

For example, an OOO reply may contain changes in a lead’s contact information or title. If your lead included someone to contact in their absence, you might also uncover a net new referral contact to add to your database. From a LTC auto-reply, you learn your lead is gone, and also likely to obtain the name of the person who replaced them. Beyond database cleansing and enrichment, learning about a LTC from an auto-reply versus waiting for a hard bounce gives you a significant timing advantage that your sales team can leverage to drive revenue.

If it’s time to take your dirty data to the cleaners, sign up for your free 30-day trial of LeadGnome. Our customers add up to 36% more net new contacts and enhance 72% of existing contacts annually — all from something they’re already doing: sending email marketing campaigns!

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