There’s no way around it, bad data impacts every aspect of a business—from lead generation, to marketing, to customer relationships, to cold calling, to revenue. After all, if you can’t reach your prospects or customers, your message, offer, and product no longer matter. Consider this (source):
- 94% of businesses suspect that their customer and prospect data is inaccurate.
- 40% of business objectives fail due to inaccurate data.
- Up to 25% of B2B database contacts contain critical errors.
- 62% of organizations rely on marketing data that’s up to 40% inaccurate.
- Bad data costs U.S. businesses more than $611 billion each year.
It’s clear data is a critical business resource. Yet, many companies continue to struggle with database maintenance. If you’re not sure where to start, keep reading. Today we break down some key database terms and concepts.
Data normalization is the process of standardizing values within your contact database. This process creates relativity and context by grouping similar values into one common value. Any data point can be normalized. Examples include job title, job function, company name, country, state, industry, etc.
Although data normalization may seem simple enough, collection processes often complicate things. Consider the following:
- Manual Data Entry: You should expect errors and discrepancies whenever humans are responsible for manual data input. Whether someone fills out the form on your website, a salesperson doing their own prospecting, or an events manager entering data from business cards—each will have their own set of spelling errors, abbreviations, and capitalization methods.
- Multiple Names for the Same Data Point: What we mean is this: there are many different ways to say the same thing. Think states—Connecticut is the same as CT. Or job titles—a Content Marketing Manager is the same as a Marketing Content Manager.
- Data Source Differences: Depending on how you collect your contact data, you might receive the same set of data points in several different formats. For example, contacts that come in through partner programs may follow different naming conventions than contacts that come in through your website.
These discrepancies, though they may seem inconsequential, can have a huge impact on your sales and marketing outreach. A data normalization strategy can fix bad data by grouping values and standardizing inconsistencies.
For an in-depth guide to data normalization, check out The B2B Marketer’s Guide to Data Normalization.
A database append is the process of taking your existing contact database and matching it against a vendor’s database to fill in any gaps or missing information. This is helpful for several reasons—more specifically, appended data can result in:
- Increased sales connect rate
- Improved email deliverability
- Improved client retention
- Better customer service
- Maximized ROI
- Increased revenue
- Wider marketing reach
It’s important to keep in mind—not all appends are created equal. The vendor you choose will determine the quality of your database append. Choose wisely.
For an in-depth guide to selecting a data vendor, check out Try Before You Buy: Data Sampling Strategies.
This final concept is a little more difficult to explain—simply because it can mean so many different things. A database cleanse can involve some normalization and appending but also may cover things like deduplication, data corrections, market intelligence reports, and even net new leads.
In the simplest of terms, a database cleanse is the process of detecting data inaccuracies and fixing them.
We’ve said it before, and we’ll say it again, your sales and marketing outreach will only be as good as your data. If your data is full of inconsistencies, inaccuracies, or missing information, you won’t be able to reach your prospects or communicate with customers.
Make database hygiene a priority and you’ll see instant improvements in your sales and marketing campaigns. Not convinced? Consider these statistics (source):
- A strong organization can generate up to 70% more revenue than an average organization based solely on the quality of its data.
- Companies that employ consistent data hygiene practices create 700% the number of inquiries and 400% the number of leads than those who do not.
For more information about data quality, check out the following articles:
- How to Get More Value From Your B2B Data Purchase
- A Guide to B2B Data Sources
- 36 B2B Data Statistics: The Effect of Dirty Data on ROI
- How to Get More from Your B2B Data
If you’re ready to prioritize your B2B database, contact ZoomInfo today.