How can you effectively segment and categorize customers and prospects so that you can deliver personalized messaging to them, if you don’t have any faith in the data that you are using to do so?
Imagine that you are a B2B company. You want to segment your prospects based on their job titles. It makes sense, right? You don’t want to present your solution to a CEO in the same way that you would a CFO. They have different needs and concerns and your messaging should reflect that.
Without a normalized database, you might find that many usa telephone code prospects that should be segmented into the same bucket are not, because their job titles are so dispersed. For the CEO segment, you might see different segments based on their non-standardized data, like:
CEO
Chief Executive Officer
Owner
Founder & CEO
Co-Founder/CEO
Different titles can be used to describe the same segment. Without normalization and standardization, these prospects may end up in different buckets, making analysis difficult.
3. Improve Lead Scoring & Routing
Lead scoring is the process of assigning a value to specific leads or accounts in your CRM so that you can effectively prioritize the best opportunities. Effective lead scoring relies on high-quality data to actively segment those prospects. Using our previous example—a B2B might assign scores to leads using their job title as one of the variables. A CEO might be more valuable than a Marketing Manager as a lead, and the higher score allows your sales teams to prioritize those leads.
Well, without normalized data for the job title, many of your prospects will receive inaccurate scores. This is extended to all fields that are used in the lead scoring process. A lack of normalization across an entire database potentially means that every lead is inaccurately scored.
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