There’s no way around it, bad data impacts every aspect of a business — from lead generation to marketing, customer relationships, cold calling, and revenue.
After all, if you can’t reach your prospects or customers, your message, offer, and product no longer matter.
Today we break down some key terms and concepts with our database glossary.
What Is B2B Data?
B2B data is insightful information used to help drive sales and marketing efforts. This includes a database of company details, contact information of sales leads and decision-maker contacts, and more.
It’s clear data is a critical business resource. Yet, many companies continue to struggle with database maintenance. For 54% of B2B businesses, their biggest challenge to achieving success is the absence of data quality.
If you’re not sure where to start, keep reading.
What is Data Maintenance?
Data maintenance in B2B is the process of practicing good data hygiene, including database normalization, appending, and cleansing.
What does data normalization mean? It 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:
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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. In fact, 43% of sales and marketing teams a lack of accurate data negatively affects their alignment, impacting outreach efforts.
A data normalization strategy can fix bad data by grouping values and standardizing inconsistencies.
Database append refers to 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
- Better customer service
- Wider marketing reach
- Improved email deliverability
- Increased revenue
- Maximized ROI
- Improved client retention
It’s important to keep in mind — not all appends are created equal. The vendor you pick will determine the quality of your database append. Choose wisely.
This final concept can mean so many different things. In the simplest of terms, a database cleanse is the process of detecting data inaccuracies and fixing them.
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.
[Related Read] 5 Reasons to Clean Your Marketing Database Today
Maintaining Your B2B Database Streamlines the Customer Journey
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.
For more information about data quality, check out the following articles:
This post was updated January 18th, 2021