Six Reasons to Normalize Customer Data

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Now the big question—why? Why should I spend all that time and effort to normalize my data? It may seem unnecessary if you’re not noticing any serious problems from non-standardized fields.

Well, the answer to that is simple. Most of the negative effects of low-quality data fly under the radar. Sure, it might be embarrassing when you see that you referred to a prospect as “JAMES” in an automated marketing email. But it’s not the end of the world.

But you don’t see all the hidden effects of low-quality data usa phone number example that never get talked about or make their way up to management. But these effects can hurt companies relying on big data over time.

When you have low-quality data, your marketing teams are scared to inject more data-based personalization into marketing campaigns. Even small mistakes, such as improper capitalization of names, can have an impact on your brand reputation in the long term.

Your sales teams are affected too. Low-quality and missing data means that they lack the critical context they need to speak directly to the biggest concerns of prospects and customers. This directly leads to lower sales and poor quality analysis.

Further, low-quality data negatively impacts lead scoring, which hinders sales reps’ ability to effectively segment and categorize prospects so that they can engage with them effectively.

Here are five of the top reasons all companies should normalize their customer data in some form.



1. Identify Duplicate Data
With normalized data, it is a whole lot easier to find and merge duplicate customer records. Duplicate customer records hinder your customers’ experiences at every point in their journey—including all engagements with marketing, sales, and support, both pre- and post-sale. With duplicate records, companies can never be certain that they are working with complete information when referencing a single record.

When it comes to marketing, duplicate records may result in your prospects receiving the same marketing materials more than once. In sales, splitting a single customer's data between two records means that your sales reps may engage with prospects while lacking the appropriate insights.