Identify duplicate data

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With normalized data, it’s much easier to find and merge duplicate customer records. Duplicate customer records hinder your customers’ experience at every point in their journey, including all interactions with marketing, sales, and support, both pre- and post-sale. With duplicate records, businesses can never be sure they’re working with complete information when referencing a single record.

When it comes to marketing, duplicate records can result in potential customers receiving the same marketing material more than once. In sales, splitting a single customer's data between two records means salespeople can engage with potential customers without having the right information.

2. Improve marketing segmentation
How can you effectively segment and categorize customers and prospects so you can send them personalized messages if you have no confidence in the data you use to do so?

Imagine you’re a B2B company. You want to segment your potential customers based on their job title. Makes sense, right? You wouldn’t want to pitch your solution to a CEO the same way you would a CFO. They have different needs and concerns, and your messaging needs to reflect that.

Without a normalized database, you may find that many leads who thailand country code should be segmented into the same group aren't, because their job titles are too spread out. In the case of the CEO segment, you may see different segments based on the non-normalized data, such as:

CEO
Managing director
Owner
Founder and CEO
Co-Founder/CEO
Different titles can be used to describe the same segment. Without normalization and standardization, these leads can end up in different groups, making them difficult to analyze.

3. Improve lead scoring and routing
Lead scoring is the process of assigning a value to certain leads or accounts in the CRM so that you can effectively prioritize the best opportunities. Effective lead scoring relies on high-quality data to actively segment those leads. Using our example above, a B2B could assign scores to leads using their job title as one of the variables. A CEO may be more valuable than a CMO as a lead, and the higher score allows sales teams to prioritize those leads.

Well, without normalized data for the job title, many of your leads will receive inaccurate scores. This extends to all fields used in the lead scoring process. Lack of normalization across the entire database can mean that every lead receives an inaccurate score.