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Data quality

What’s up Wednesday: Phone validation

Stephanie Zatyko Data quality

In our new video series “What’s up Wednesday,” we’ll give you a quick video overview of a concept that we often get questions about at Experian Data Quality.

What’s up with… phone validation? Verifying phone numbers is a critical aspect of your overall data quality strategy. The phone can be an excellent way to reach out to your customers, but if your organization isn’t handling them properly you could be putting yourselves at risk.

When it comes to consumer privacy and phone numbers, the Federal Communications Commission (FCC) has put a number of regulations in place to protect consumers. Anyone collecting this sensitive information must be aware of the Telephone Consumer Protection Act (TCPA) – otherwise your organization could face poor customer satisfaction, fines or lawsuits.

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6 tools for building a data quality strategy

Stephanie Zatyko Data quality

Determining what type of data quality tools your organization needs depends upon how sophisticated your data quality strategy is. For some companies, a simple data quality approach may be fine, but other organizations may need a very advanced level of data accuracy and data management.

Regardless of your organization’s specific needs, there are a few key universal data quality considerations that are important for any effective approach. Here are six tools that can help with your data quality strategy.

1. Data cleansing: Data cleansing tools are needed when an organization’s data must meet specific domain restrictions, integrity constraints or other business rules. These types of tools provide accurate information for business use. Examples include address verification, email verification, phone validation, etc.

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Are you getting value from your data quality tools?

Erin Haselkorn Data quality

Data is an increasingly important part of organizations today. It can be leveraged for traditional operations and efficiency, but now is also being used to gain a better understanding of the consumer, prompt personalized marketing messages and determine a host of new product innovations.

The increasing use of data is putting a greater spotlight on information and how it is used. But many companies have issues with their data. In fact, a recent Experian Data Quality study found that U.S. companies believe on average a quarter of the information in their databases is inaccurate.

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Data quality boot camp: Getting it right in person

Melissa Nazar Data quality

The first two stages of data quality boot camp helped teach you how to collect better contact data on your website and in call centers, but your work isn't over yet! There are many more places where you can collect customer information, including the good old fashioned in-person experience.

Brick-and-mortar woes

Thirty-nine percent of organizations collect customer contact data at physical locations. Data is typically collected by an employee at the point of sale, but can also be entered into a kiosk by customers. In either case, human error is the number one culprit of bad contact data being collected.

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Data quality boot camp: Managing call center issues

Melissa Nazar Data quality

In our first blog post in this three-part series, we discussed how you can take the first steps in your data quality boot camp program: tackling bad data collected through your website. But how do you manage issues within other channels? Here we’ll focus on managing data quality in your call centers.

Calling out call centers

No matter what type of organization or industry you’re in, at some point, a customer may need to reach out to a call center. But because of disparate systems, a customer can have an entirely different experience with your company on a website vs. when they call you.

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Data quality boot camp: Collecting clean data from your website

Melissa Nazar Data quality

Did you know that on average, U.S. companies are collecting data from customers in more than three different channels? With all that data coming in from different places in different formats, it’s no surprise that 85 percent of U.S. companies believe their bottom line is affected by data issues.

Kicking your data into shape

Our databases are hiding a dirty secret—bad data. In this three-part blog series, we will explore three channels where bad data is collected and share how you can kick your data quality strategy into shape.

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3 reasons your customer on-boarding isn’t working

Joseph Lang Data quality

We live in a world in which people want and expect information at their fingertips at all times through all channels. In the utility space, it’s no different, and especially important during on-boarding, when you first start a customer relationship. Not having a quick and efficient on-boarding process can mean a poor customer experience and significant increases in operational inefficiencies.

A customer’s first impression of your company happens when they start services. That’s why it’s so important to make the on-boarding process as quick and efficient as possible.

Here are three primary reasons that your customer on-boarding could be problematic (and some simple ways to avoid issues):

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Gain buy in for contact data validation

Erin Callahan Data quality

In today’s digital age, data is king. Companies are now warehousing higher volumes of consumer data through more channels than ever before. This data is helping to improve the user experience and customer satisfaction, facilitating more intelligent business decisions and generating overall gains in revenue.

With such high volumes of data being collected, businesses are also faced with the reality of a lack of data integrity and data accuracy. A high level of inaccurate data puts limits on how data can be used, which creates lost time, money and opportunities.

One way organizations can improve accuracy is by utilizing contact data validation. Software tools can be implemented at any point of capture to ensure the accuracy of valuable customer information, such as email address, mailing address and telephone number. This translates to decreased costs and resources associated with incorrect contact information.

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