As Marketers plan for 2022 and beyond, organizations will continue to increase their investment in data, providing opportunities for businesses to prioritize personalized market initiatives. Marketing departments have gone digital, investing heavily in customer data, lead lists, CRM, and other related tools. All these efforts, however, might go in vain if your organization gathers inaccurate or irrelevant data.
For your organization’s data to be useful, it must be precise, consistent, and recent. You should be able to read, search, and use each segment the same way across all the records in your customer database. Data Normalization can help by standardizing the formats of fields and records within a database. It minimizes the cost and time associated with managing a database, locating missing information, and analyzing it for decision-making.
Essentials of B2B Data Normalization
Data normalization structures relational databases based on a series of rules, making it easier to navigate. If you look at a prospect’s name, for example, data normalization ensures it’s properly capitalized and includes both the first and last name. If there’s a strong data normalization process in place, the prospect’s name should read John Mathew, not John M or JOHN. This is a standard example of normalizing the fields. Different companies adhere to different criteria, so data normalization can look different for each database.
We all know that data can’t always be perfect. Here are common means of data collection that can lead to issues before normalization:
- Third-Party Data Purchase
Increasingly, marketers acquire data from third-party vendors. This data might not be uniform, both in format and in content. Differences are the most common challenge your data might face before normalization.
- Site Landing Pages
Prospect and customer data are often captured through web forms on a product or service landing page. As these forms have open text fields, each prospect can fill them out differently (and some with fake information!). A marketing manager, for example, could fill out the form as a “Manager” or simply a “Marketer.” If you’re collecting data from a landing page, you probably should invest in data normalization.
- Tools Manual Data Uploads
To connect with a qualified lead, sometimes information is captured from prospects manually and then uploaded. Think, a phone call or email, where information is exchanged. As the manual upload is cumbersome, it leaves plenty of room for error, especially in areas that need to be precise.
- Data Extraction Tools
Besides forms, marketers might collect business cards at trade shows and events. They use data extraction tools to pull and convert prospect information from these hard copies into digital form. Normalization issues arise since data fields from these cards can follow different standards. Though this is not a primary form of data collection, especially because of the pandemic, it is one that has affected your older databases in particular.
How Does Data Normalization Help B2B Marketers?
Data normalization is critical for businesses that use marketing automation systems, sales platforms, and reporting tools to engage buyers with highly customized content. Does data normalization only serve this purpose? The answer is ‘No.’ Other reasons exist for B2B companies to normalize their customer data.
Reason #1 – Locate Duplicate Records
Duplicate records hinder your sales and marketing team at almost every step. With duplicate records, you’ll never know what your total addressable audience actually is, or how many leads you have actually brought in from your marketing activities. It’s possible to merge or remove the same data through normalization. Normal forms, such as 1NF or 2NF (first and second normal forms) help you eliminate repeating values by separating them into a set of related attributes with a primary key.
Reason #2 – Enhance Customer Segmentation
Data segmentation is crucial for delivering personalized messaging to buyers. However, a database without normalization could segment the same prospects into multiple buckets. For example, a non-normalized database could sort a CEO into multiple segments based on non-standardized values, such as Chief Executive Officer, CEO, Founder, or Owner. Data normalization might help combine these into 1 segment, helping to properly assign contacts to the right segments for targeting.
Reason #3 – Better Lead Scoring
Lead scoring relies on highly organized, quality data. Suppose your organization assigned a value based on job title, or one of any other fields associated with a lead. If, in our example above, job titles are improperly sorted into the wrong segments, you might have leads scored incorrectly. Unless you normalize the values in the “job title” field, or any other field for that matter, you risk scoring your prospects incorrectly.
Reason #4 – Seamless Data Integration
Over 60 percent of companies today collect customer information from three or more sources, which may have different format requirements. Normalization allows for seamless use of data from third-party sources by rendering it into a single, standardized form. It ensures proximity between dependent values in a dataset.
Normalize your Unstructured Data Now
Data normalization should be the ultimate goal of businesses seeking to improve lead quality. With True Influence Marketing CloudTM, you can input and normalize data for any of your sales and marketing objectives. It lets you leverage cleansed data in support of remarketing or retargeting, ABM strategy, paid display ads, and more. Learn more about data management and the integrated marketing cloud on our other blog posts.