Most businesses are at least somewhat aware of the importance of customer data cleanliness. Having accurate data on your customers and prospects is critical for segmenting customers, injecting data into marketing automation systems, and generally providing a better experience to those that engage with your brand.
When you think of data cleanliness, you probably think about missing data, data with typos and errors, or duplicate records that can clog up the gears of your marketing and sales operations.
But one key aspect of clean data that so many companies overlook is data normalization, which oftentimes is even more critical for keeping a customer database clean and organized. In fact, data normalization drives the entire data cleaning process. Without normalized data, it makes it very difficult to even gain a full understanding of how many data errors are within your customer database.
What is Data Normalization?
Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate.
Put simply, data normalization ensures that your data looks, reads, and can be utilized the same way across all of the records in your customer database. This is done by standardizing the formats of specific fields and records within your customer database.
Normalization includes the process of standardizing specific fields. In a customer database, these might include fields like first names, company names, addresses, phone numbers, and job titles. There are many ways that each of these records could potentially be expressed in a dataset.
Here are some examples:
- Names: James vs. james, James A. vs. James, JAMES vs. james. Ensure that all names are properly capitalized and standardize whether initials will be included in a particular field.
- Company Names: Acme inc. vs. Acme. Determine whether company registration terms like “inc,” “ltd,” or “LLC” will be included in the field name as you may want to get rid of these appendages for marketing automation reasons.
- Phone Numbers: 1234567890 vs 123-456-7890. Make sure that your phone numbers are easy to read and are compatible with systems that use them, such as a sales auto-dialing system.
- Job Titles: CEO vs. Chief Executive Officer.
- Addresses: 123 Mulberry St. vs. 123 Mulberry Street New York, New York, 10013
These are all pretty standard examples of the type of fields and customer data that needs to be normalized to make the most of it.
Every company has different criteria when it comes to normalizing their data. Normalized data is critical for the systems that use that data, including marketing automation systems, sales systems, and reporting systems.
5 Reasons to Normalize Customer Data
Now the big question — why? Why should I spend all of the time and effort to normalize my data? Especially if I’m 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 what you don’t see if all of the hidden effects of low-quality data that never get talked about or reach their way up to management.
When you have low-quality data, your marketing teams are scared to inject more data-based personalization into your marketing campaigns. They don’t want to damage the company’s reputation.
Your sales teams are affected too. Low-quality and missing data means that they lack the critical context that they need to speak directly to the biggest concerns of your prospects and customers. This directly leads to lower sales.
Further, low-quality data negatively impacts lead scoring, which hinders their ability to effectively segment and categorize prospects so that they can engage with them in a way that will resonate with them.
Here are 5 of the top reasons why all companies should normalize their customer data.
1. Identify Duplicate Data
With normalized data, it makes it a whole lot easier to find and merge (or delete) duplicate customer records. Duplicate customer records end up hindering the experience of your customers’ experience at every point in your sales funnel due to a lack of a single customer view. With duplicate records, companies can never be sure that they are working with full and complete information when referencing a single record.
When it comes to marketing, duplicate Salesforce 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 data and insights. Duplicate HubSpot data also increases your storage costs.
2. Improve Marketing Segmentation
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 Marketing Director. They have different needs and concerns and your messaging should reflect that.
Well, without a normalized database, you might find that many 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:
- Chief Executive Officer
Different titles can be used to describe the same segment. Without normalization and standardization, these prospects may end up in different buckets.
3. Improve Lead Scoring & Routing
Lead scoring is the process of assigning a value to specific leads or accounts in your CRM (like HubSpot or Salesforce) 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.
Well, without standardized and 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.
Without normalized data, the scores that your prospects receive may be wildly different than what they should be.
4. Inject More Data into Marketing Automation
In order for your marketing team to advance personalization within your marketing campaigns, they need to have faith in the data they are using. If they can’t realistically inject first names into a marketing campaign without a bunch of “JAMES,” “JAmes,” or other common errors in the data being delivered to the customers, they simply aren’t going to use it.
It does more harm than good to send personalized messages using low-quality, non-normalized data.
5. Identify, Aggregate, or Remove Redundant Data
With normalized data, you’ll be able to identify redundant data in your customer datasets, even when that redundant data is housed in multiple different fields. In order to normalize data, you have to ask yourself what the field is trying to say. What is it that the data is tracking? In figuring that you, you can identify when fields are trying to say the same thing and merge them to avoid confusion and limit costs.
A Tool to Help Normalize Data
Insycle is the perfect tool for normalizing customer data. Using pre-built templates or building custom templates that are specific to your organization’s data processes, Insycle makes it easy to identify standardization issues and normalize your data.
Additionally — when you first sign up for Insycle, your customer data will be analyzed and a Health Assessment will be generated. Included in this Health Assessment are multiple predefined categories that help companies with normalization and standardization. The Health Assessment provides links to fixing these issues directly.
The Health Assessment allows you to add custom templates, so standardization issues that are specific to your business can be tracked and fixed as they arise.
With Insycle, you can limit the number of normalization errors in your customer data sets and clean your critical customer data.
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