How bad data affects customer experience (+ 4 ways to ensure data quality)

A recent study by Accenture found that less than 3% of clients feel that banks, brands, and other institutions understand them, their lives, or their living situation.

This is due to bad data, which harms customer experiences and overall business success. We’ve all been there: Maybe your bank sends you a first-time homeowner program when you’ve owned a home for several years. Or an online store promotes products that you’ve already bought. In this mini guide, we’ll walk you through how bad customer data affects the customer experience and how you can ensure data quality.

How does bad data affect the customer experience?

Here are three ways bad data can hamper the customer experience:

• It reduces consumer confidence
• Your leads don’t convert
• Customer lifetime value drops

1. It reduces consumer confidence

The biggest issue with bad data is that it lowers the consumer’s opinion of your brand since it gives off the impression that you don’t understand them very well. And research shows that customers gravitate towards brands that “get them,” even if the price is higher.

Sending irrelevant emails and text messages can also be annoying, causing customers to leave bad reviews.

So before you run any form of marketing campaign, gather good data—not expired or incorrect information.

2. Your leads don’t convert

You won’t achieve your marketing goals if you’re using bad data to write ad copy. You’ll base ads around false presumptions, resulting in low conversion rates and more money spent on marketing.

But if you’re collecting updated customer data, you can target core problems that customers are facing. This personalizes the ad experience and boosts conversions.

3. Customer lifetime value drops

If a brand doesn’t understand your needs, even after working together for several months or years, it’s safe to say that you’ll spend your money elsewhere.

This is why customer lifetime value typically plummets when using bad data. You might be able to convert the odd prospect, but it’s difficult to keep them when you don’t understand them.

How to get rid of bad customer data

Getting rid of bad customer data isn’t difficult. With some best practices, you’re well on your way to improving the customer experience. These four tips can help. 

1. Identify the bad data

Before removing, cleaning, and structuring data, you must first identify the data creating these negative customer experiences.

Some bad data examples to look out for include:

• Inaccurate information
• Duplicate records
• Data lacking validity

The problem that most companies face is that they make judgments using this data; when they realize it’s incorrect, it’s too late. They’ve already spent thousands on unsuccessful campaigns and customer retention methods.

An easy fix is to run data through a customer data platform (CDP) like Lytics. It identifies inaccurate data before you use it to make business decisions.

2. Revise and update bad data

After identifying bad data, it’s time to convert it into accurate data that you can use to improve customer experiences. The Lytics CDP can help update outdated data. However, sometimes this isn’t enough.

Research shows that 31% of people change their email addresses at least once a year. Most businesses don’t update their list nearly as often and get discouraged with their open rates plummet.

This is where regular revisions can help. By revising data sets like email addresses and audience segments, you keep your data up to date. For example, you could use email software that automatically deletes an email address from your list after sending five emails without an open.

3. Implement a data quality program

Although the best way to remove bad data is to prevent mistakes upon collection, you may already have inaccurate data. So consider CDPs like Lytics since it can streamline the data cleaning process. 

Lytics lowers the amount of bad data collected and establishes precise and reliable processes that your team can follow, preventing the collection of bad data in the future.

4. Optimize data collection

If you currently have terabytes of bad transactional data, there’s a high likelihood that the habits and techniques your team uses to collect data are the root of the problem. Even though you’re using an application like Lytics, it’s necessary to analyze team processes to find where all this incorrect data comes from.

Customers often provide wrong information because they’re not comfortable sharing personal details. In this case, you can:

• Avoid lengthy data collection forms
• Explain why you need certain information
• Offer something in return like a discount or free product

This solves your bad data problem at the core, so you won’t have to clean and organize much data in your CDP.

Collecting high-quality data is simple with Lytics

Collecting correct customer data is vital for business success since it optimizes the customer experience, thus boosting leads, consumer confidence, and customer lifetime value. With Lytics,  that process is easy, painless,  and foolproof.