You can follow all the sales tricks and best practices in the book, but if you’re working with dirty data, progress will always be one step forward, two steps back.
“Dirty data,” in this case, refers to a CRM (or other database) full of inaccuracies, omissions and duplicate entries; customer and lead profiles are marred by invalid email addresses, misspellings, empty form fields, and expired information.
And don’t kid yourself: dirty data is a reality for almost every business. In 2013, U.S. companies told Experian that about 32 percent of their data is inaccurate. More recently, Dun & Bradstreet’s analysis of 223 million B2B records revealed some startling insights:
- 41 percent of B2B contacts lack a working phone number
- 66 percent of records are missing revenue and industry data (which are critical components of lead scoring)
- 62 percent of companies rank their email deliverability as “questionable”
- The average self-reported health rating for B2B data is a mere 3.2 out of 5
How it Happens
Even if you have airtight data capture practices, data doesn’t just come in clean and stay that way. It decays. According to HubSpot, marketing databases decay by almost 23 percent every year. But how does this happen?
First, it happens in ways you can’t control. People change: they move to new towns, change jobs, change titles, update their email addresses — and it isn’t their first instinct to notify salespeople. Even in a short span of time, entire businesses can shut down, relocate, or rebrand. If you don’t have a system in place for regularly updating contact information, it will inevitably become outdated.
Second, data gets dirty in ways you can control. For sales teams, one of the biggest issues here is CRM adoption. If your reps don’t understand the importance of working in the system at all times and using best practices for data entry, they’ll cut corners or create separate information silos (information stored in an email account or private spreadsheet). Improper CRM configuration can also be a problem — for example, if you haven’t set up your custom fields correctly.
How Dirty Data Damages Productivity
Dirty data isn’t just a clerical issue; it directly impacts your ability to sell and manage customers. Here are some of the most common side effects:
Bad Segmentation: Most of the time, businesses segment their leads by job title, industry, or buying stage. But if the data in your lead profiles is missing or inaccurate, you’ll end up with the wrong leads on the wrong lists, or the same lead on a list twice. When a sales rep follows up, they’ll be going into the conversation with the wrong understanding of the buyer’s needs and intentions . . . which can be a huge turn-off.
Faulty Lead Scoring: The same logic applies to lead scoring. If your lead data is dirty, good opportunities might fall through the cracks and bad ones might falsely trigger the qualification threshold. For example, a lead gets extra points for revenue and a “CMO” job title. But their actual job title is “content manager” and they work for a company with $100K in revenue, not $1M. Now your sales development reps have to waste time calling a lead that may not be a good fit.
Failed Personalization: Salespeople use what they know about a person to build rapport. But if your records don’t line up with the truth then your attempts at personalization could end up being pretty embarrassing, whether they happen through email or on the phone. “Hi, I’m calling for Adele Dazeem? . . . Oh, sorry. Idina Menzel.”
Duplicate Efforts: This one is pretty self-explanatory. If the same contacts are listed more than once in your database, you’re going to repeat yourself. If a prospect gets the cold call from multiple outbound reps, or the same email three times, they’re a lot more likely to shut you out. Not to mention, a database full of duplicate entries could send you into a higher pricing tier for your CRM or marketing automation platform (MAP).
Skewed Reporting: Depending on how extensive your data problem is, it may become difficult to run accurate CRM reports. Executives will lose confidence in sales forecasts, and you’ll have a hard time keeping track of your pipeline. The absence of these and other insights impairs your ability to make data-driven decisions and build effective sales strategies.
What to Do About It
Here’s the good news: there are some clear steps you can take to improve the quality of your sales data. For starters, try to minimize human error by training and coaching employees who handle data entry. You should also make sure your CRM (and MAP, if applicable) is configured to minimize errors — for example, set up required information fields to avoid accidental or intentional omission.
Next, set up some kind of quality assurance or “data verification” process. Data verification is essentially an audit designed to keep your database current and accurate. It happens through data governance, standardization, and by confirming key data values (phone numbers, email addresses, etc.). If you have the resources to do this internally, great. If not, consider partnering with a data verification service.
If you have trouble capturing enough data in the first place, you might consider data enrichment — a system or process used to augment contact profiles. This can be done through progressive profiling (asking contacts for smaller pieces of information over time), or by consulting a data enrichment vendor. Again, your approach will depend on the resources at your disposal.
No business is immune to dirty data. Even if you don’t suffer from a bloated or skewed database, the risk of contamination is real, and the best time to act is before things get out of hand. With the right best practices in place, and maybe a third-party resource to help with execution, you can keep your data squared away and focus on what really matters: selling.
