Reports have shown 94 percent of businesses suspect that their customer data is inaccurate. Thus, B2B companies need to take proactive measures to ensure data is clean for customers to have better experiences with their company.

B2B marketers are driven crazy by dirty data. At the same time, most marketers would prefer not to be involved with managing the data they use. Keeping marketing data clean is mostly a ‘not in my back yard’ issue. Marketers will talk about how important it is and how it’s a must, but they don’t want to, or don’t have the expertise to roll up their sleeves and clean their customer databases.

Bad data sucks

Yet, it’s noteworthy because companies having inaccurate data leads to less effective and enjoyable experiences for customers. We all want our Uber driver showing up at the correct address and our vendors knowing what products and services we use.

Maintaining accurate data is a small part of any marketing budget, but there are plenty of reasons not to gloss over the dirty data problem. IBM estimates losses in the U.S. economy are over $3 trillion per year from poor data quality and costs businesses 15 to 25 percent of revenue. In fact, 62 percent of organizations rely on marketing and prospect data and marketers can generate 209 percent more revenue when they are well-aligned with their sales department.

In B2B marketing dirty data consists of both records for contacts that are no longer with the company, companies out of business and inaccurate information in critical targeting or contact fields. An erroneous email is bad but having the wrong industry or company size assigned to a company can have the same impact. All it takes is one bad email to damage a relationship between your company and the prospect being sold.

Also, there are additional costs for dirty data when you look at the customer relationship management, enterprise resource planning, and marketing automation systems used today.  Most of these systems have a cost for the volume of data you keep in the system.

Companies that use multiple systems are paying multiple times to house dirty data. If you look at the cost of systems like Salesforce, Marketo, and Pardot — compared to what the data costs that you put into these systems, it’s easy to see how the cost to house bad data can quickly become a significant portion of your data budget.

The “dark side” of dirty

Dirty data in emails can get marketers kicked off email delivery platforms and can lead them to getting blacklisted.  A lesser known but just as problematic of an issue is inaccurate targeting of email campaigns due to information like industry and geography being wrong.  Complaints from recipients can also have marketers booted from their internet service provider or email delivery service resulting from spam complaints.

Improper targeting in email campaigns can also hurt your SEO. By targeting the correct audience, with accurate data, you increase the number of purposeful searches for your site and users will tend to spend more time on your site, decreasing the likelihood for them to go back to Google and search for the same keywords.

When you target the wrong contacts with your emails, it has the opposite effect.  Google SEO thinks your site doesn’t fit the keywords searched if visitors quickly leave and run the same or a similar search again.  This causes them to lower your search ranking for those keywords.

The answer is obvious but not so easy to implement

The easiest way to keep your data clean is to start with clean data. The two main areas to cover here are thoroughly vetting new data providers and having all contact and account data automatically fed into your systems screened.  The type of screening will depend on the volume of data you get this way. If it’s manageable, someone can manually review it. Otherwise, filters can be set for what is manually reviewed or it can be outsourced to a third party.

Reviewing data providers is easy, it’s recommended you request and review a contact data sample of a reasonable size before making any purchase.  The number of records in a sample is crucial. For example, if they only give you three records they most likely hand-picked them and only when a list is purchased will you see what they’re really providing. Reviewing the sample will show you the accuracy of both the individual contact fields as well as the overall targeting.  If a list company is unresponsive when you ask for a sample or their sample looks like garbage, you know to stay away from them.

Contact data goes bad quickly, even if you start with accurate data, you will still need to regularly clean your contacts. Flagging and removing email bounces is easy enough to do, but many emails will never bounce even when the contact is long gone. The best solution for a thorough cleaning is always a third party tool like EmailChecker that can leverage a large internal database and go deeper than basic email verification.