Impacts of Poor Quality Data on Your Business

All companies, no matter what their size, are reliant on data. Data is the lifeblood of any organization and is used to make informed business decisions every day. However, not all data is created equal. In fact, poor quality data can have a significant negative impact on businesses of all sizes. In this blog post, we will explore the impacts of poor quality data and discuss how a CDP can help mitigate these effects.

What is poor quality data?

Poor quality data is any data that is inaccurate, irrelevant, or outdated. This type of data can be caused by a variety of sources including poor data entry practices, human error, poor record keeping processes, or outdated technology and systems. No matter the source, poor quality data can have financial and operational consequences for your business.

Does bad data actually make businesses lose money?

It is estimated that poor quality data costs businesses an average of $12.9 million annually. This figure includes expenses associated with data entry errors, redundancies caused by poor record keeping, and the time wasted trying to make sense of inaccurate or incomplete data sets.

Some of the biggest costs are associated with fixing errors.  When errors are created during data collection, efforts to fix those errors after the fact can be costly, time-consuming, and can disrupt workflow. And not fixing the errors also comes with associated costs as well, often in the form of poor customer service. For example, if poor quality data is used to send out incorrect prices or discounts to customers, it can lead to decreased sales and dissatisfied customers.

Mistakes made from poor quality data

When poor quality data enters your system, it can disrupt operations in a number of ways. Here are just a few of the most common mistakes businesses make when dealing with poor quality data:

1. Inaccurate targeting

Poor quality data can lead to poor segmentation and targeting strategies, which in turn results in poor marketing ROI. For example, poor quality data may lead to sending out emails or ads that are irrelevant to the intended audience, leading to wasted resources and poor customer engagement.

2. Prevents successful automation

The use of poor quality data can also prevent automation from running smoothly. Automated processes rely heavily on accurate and up-to-date data in order to function properly, so poor quality data can lead to incorrect or incomplete output from automated systems.

3. Possibility of getting your emails blacklisted

Bad data can result in poor deliverability rates and possibly getting added to email blacklists. This is especially true when poor quality data leads to sending emails to non-existent or inactive email addresses. Common reasons providers blacklist emails include poor content quality, poor deliverability rates, and poor data hygiene–all of which are contributed to by poor data.

4. Increased email churn

Email churn is the rate at which people unsubscribe from email lists or mark emails as spam. Poor quality data can lead to high levels of email churn due to poor segmentation, poor targeting strategies, outdated contact lists, or non-personalized emails. If a business sends emails to poor-quality contacts, it leads to poor open and click through rates as well as a decrease in engagement.

5. Bad or frustrating customer experience

Poor quality data can also lead to poor user experience. If customers are presented with inaccurate or irrelevant information, it causes frustration and can result in decreased customer satisfaction. Additionally, poor quality data can lead to incorrect or incomplete orders being sent out, leading to unhappy customers and poor customer service.

Poor quality data disrupts workflow in more ways than one

Poor quality data can have a variety of impacts on businesses, ranging from financial losses to poor customer experience. But it also directly impacts workflow as well. It takes time to clean and validate data, and the worse the data quality, the more time it takes. This increased friction slows down processes, takes time and resources from other tasks, and leads to missed opportunities.

1. Uncertainty of reliability for the company name (reputation)

Poor quality data can lead to poor customer experiences, resulting in a damaged reputation. Customers rely on accurate and up-to-date information when making decisions, so poor quality data can lead to customers feeling frustrated or misled and weaken the overall customer engagement. This reduces trust in the company, leading to decreased sales and lower customer loyalty.

2. Lost revenue

If poor quality data is used to send out incorrect prices or discounts, it can lead to decreased sales and dissatisfied customers. Additionally, poor data hygiene processes can result in poor customer segmentation and targeting strategies, leading to wasted resources on campaigns that don’t reach the intended audience.

3. Missed opportunities

Poor quality data can also lead to missed opportunities. If poor data is preventing the proper segmentation and targeting of customers, it means that businesses are missing out on potential leads and sales. Additionally, poor quality data hinders decision-making abilities due to poor insights into customer behavior and preferences.

4. Lack of compliance with industry regulations

When data is low in quality, this can lead to issues related to compliance. Poor data hygiene processes can lead to data being collected and stored in ways that violate privacy regulations, such as GDPR. It can also result in targeting strategies and companies sending out emails or ads that are non-compliant.

5. Disrupts data flow

Poorly curated and managed data leads to incorrect or incomplete output from automated systems, such as customer segments in a marketing automation platform. This poor data flow leads to poor decision-making abilities due to poor insights into customer behavior and preferences. Poor quality data can also contain misspelled brand or customer names resulting in multiple files for one company.

6. Reduced operational efficiency and productivity

Poor quality data results in poor operational efficiency. It takes more time to clean and validate poor-quality data, thus taking away from productive work. Poor quality data can lead to errors that require manual intervention, making processes inefficient and bogging down personnel.

7. Misanalysis

If you’re relying on poor-quality or incomplete data, it’s impossible to make accurate decisions about customer behavior or preferences. Poor data leads to poor insights, thus resulting in poor marketing strategies and less effective campaigns.

How a CDP can help with bad-quality data

Customer Data Platforms (CDPs) are designed to help companies with poor-quality data. CDPs are centralized databases that collect, store and analyze customer data from multiple sources. They help clean up poor-quality data by validating inputs and scrubbing bad entries from the system.

Additionally, CDPs improve segmentation accuracy by providing a single source of truth for customer information, which can be used to ensure accuracy in targeting and personalization. Finally, CDPs help automate data hygiene processes, thus reducing manual labor and increasing efficiency.

Final Thoughts

Poor quality data can have a significant impact on businesses in terms of financial losses, poor customer experience, missed opportunities, lack of compliance with industry regulations, poor data flow and reduced operational efficiency. A CDP can help organizations address poor quality data by aggregating, cleaning and validating.

Zeta Global’s CDP+ is designed to aggregate data from all sources into one unified dataset that is always up-to-date and accurate. But Zeta’s CDP+ goes beyond your standard CDP, providing personalized experiences at scale. You can even integrate third party data into Zeta’s first party data collection, streamlining data cross points from various channels to deliver a holistic user experience.