Customer Data Quality Metrics

Regardless of the industry you are in, if you have customers, you are gathering and integrating customer data. From website browsing habits and purchase history to personal details and email addresses, companies work hard to gather and analyze this information.

With personalization taking the center stage in each company’s sales and marketing strategy, good data quality is becoming vital to success. To evaluate the quality of your customer data, it’s vital to set proper metrics.

Let’s take a closer look at the importance of high customer data quality and go deeper into data quality metrics.

The Importance of Customer Data Quality

In the modern data-driven environment, it’s impossible to build a high-quality marketing or sales strategy without high quality data. Companies of all sizes are working hard to gather, analyze, and implement different types of data. One of these highly important pieces of information is customer data.

A common mistake many organizations make is taking customer data for granted and working with it as if it all were the same quality. However, not all customer data is created equal. Some of this information can be flawed, thus hindering your analytics and causing problems with your strategy.

Data requires careful vetting and maintenance. Let’s consider your email list as an example. Your marketing and sales team works hard to collect contact details from potential and existing customers. Once an email address comes in, do you add it to the list and forget about it?

There are several problems with this approach:

The email address may be incorrect – a customer could make a typo when sharing the address, or they could intentionally give an incorrect address in order to avoid marketing emails.
The email address can become invalid – with time, customers lose their passwords, change email addresses, or switch email service providers.

If you don’t do anything about these email addresses, you will eventually have a list of invalid emails. This will hinder your entire email marketing strategy. However, with proper email list cleaning, you can avoid unfortunate mistakes.

This example demonstrates the importance of initially clean data and further data maintenance. Without a proper approach, the ROI (Return on Investment) of your marketing and sales efforts drops significantly.

According to Harvard Business Review, only 3% of companies’ data meets proper quality standards. The obvious downsides of bad data are:

Bad customer insights – bad data quality creates wrong insights. Eventually, you end up basing business decisions on something that isn’t true. For example, if you are sending a marketing email to 100 email addresses but only 60 of them are valid, you get bad information about the effectiveness of your email marketing tactics. This could cause you to adjust the strategy and lose money.
Low Revenue – clean data assets are key to improving your company’s bottom line. If you receive bad data from your marketing teams, you can make a decision about your customers’ needs and offer products that don’t generate the expected revenue.
Unexpected expenses – according to Gartner, poor data quality can cost organizations around $15 million a year. As the volume of data is growing, so are the expenses. Eventually, your company can feel the effect of poor data quality on its bottom line.

The worst part about bad quality data is the company’s inability to see a problem until it’s too late. That’s why customer data quality management is one of the most important parts of a company’s operation.

How to Improve Data Quality

Good data quality depends on many factors. If you aren’t sure that you are getting high quality customer data, you need to start from scratch. Here are a few things to consider:

Data cleansing – once customer data comes in, you can clean it with different automated solutions. For example, an email validator can check for typos and domain name errors. Your CRM system could also check for punctuation mistakes and other details that could prevent you from using accurate data sets.
Duplicate data – duplication is a serious issue that can hinder your marketing campaign effectiveness. For example, if you are using one customer data platform, you keep everything in one place. However, if your team is juggling several systems, they may be entering duplicate data. As a result, you can’t gain accurate insight or ensure data quality. You can remove duplicate data with high-quality data automation tools and switch to a centralized data management solution.
Data management – the key to removing data quality issues is implementing the right approach to data quality management. Once you collect data and start analyzing it, you need to continue maintaining its quality. Since data quality can change over time, continuous maintenance is the key to identifying errors.

A data specialist should have all the available tools to monitor, manage, and maintain customer data.

For effective data governance, they need to have a clear understanding of important customer data metrics to ensure the success of the cleansing, management, and maintenance process.

Ensuring data quality is an ongoing challenge that requires the close attention of your entire team. Many business leaders don’t pay sufficient attention to consistent data quality and feel significant losses.

Customer Data Quality Metrics

To make sure your company has continuous access to high quality customer data, you need to set specific data quality metrics.

1. Customer Data Completeness

This data measures how complete the customer data is. For example, if you are asking for customer details, the complete data would mean that all fields are filled with information. This metric should measure if the data is sufficient for high-quality analytics.

2. Customer Data Accuracy

Accuracy is one of the most important metrics. Inaccurate data sets initiate a series of incorrect activities that eventually lead to wrong decisions. Each piece of data you implement must be accurate.

3. Customer Data Consistency

You need to check if the same types of data collected from different datasets are similar or not. If the information isn’t consistent, there may be a problem at the data collection stage.

For example, customers’ bank details data should be consistent across all databases. Otherwise, you may process the payment but fail to make a refund.

4. Customer Data Integrity

You need to make sure that customer data quality remains the same as it passes through different systems. If you store your data in different programs, this can affect its integrity and lead to data errors. The common way to measure integrity is to check the data transformation error rate.

5. Customer Data Format

You have to check that the data format is the same across your data sources, systems, and databases. For example, the postal address should always start or end with a zip code.

Additionally, you should check which format you store the data in. For example, images of documents or PDF files can’t always be analyzed properly, which hinders your data usability.

The Takeaway

The quality of customer data is highly important to the success of your company. To stay ahead of the competition, gain valuable insights, and increase your revenue, it’s imperative to have a customer data quality management system.

To make sure your data is always in top shape, you can leverage a variety of automation tools and implement data quality maintenance in daily business operations.