5 Common Customer Data Problems & How To Fix Them

5 Customer Data Problems & How to Fix Them

5 Customer Data Problems & How to Fix Them

High-quality data is critical to ensuring organizations can deliver great customer experiences and achieve expected business outcomes. However, many companies encounter common customer data problems that significantly affect data quality. 

Data quality focuses on creating data that is complete, consistent, timely, and accurate. It also needs to meet specific requirements for how it will be used based on your organization’s needs. 

Good data quality means organizations can provide relevant, personalized experiences that build customer loyalty and retention. It also gives business leaders access to data that helps them derive insights, improve operations, and make decisions that drive successful outcomes. 

Poor data quality can cause problems that lead to wasted time, lost opportunities, and incorrect decision-making. According to Gartner, $12.9 million is lost yearly due to poor data quality. In addition, having access to reliable customer records is important for data privacy measures which aim to protect personal data from malicious use.

Businesses can reap the rewards of accurate customer data by understanding the importance of data quality and implementing effective measures to improve it. Let’s look at five common customer data problems, and how to develop a strategy that maximizes business performance.

  1. Incomplete Data

A complete data set means you have all the required data for a specific task or purpose. Incomplete data means that critical information is missing from a set of data. 

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Incomplete data affects insights and analysis activity. For example, if an organization is trying to understand a given market’s size, and the data is incomplete, it could make the wrong assumptions about that market.

Say you want to send a special offer to customers in a specific state. If the state field is not completed in every contact record, some customers will not get the special offer, which could leave you missing out on revenue opportunities.

How to Fix It  

So, how can you ensure your data is complete? The first step is to know what “complete” means for your organization. What information do you plan to use in your decision-making processes? What data is essential for activating the right campaigns? Once you know this, you must assess your customer data profiles for missing values and establish processes to find that data.

Data enrichment tools help fill in the blanks using third-party data. Treasure Data provides a series of ready-to-use Treasure Boxes, including pre-built code, components, and applications, that can help you automatically connect with other data sources to complete customer profiles. 

Remember that you continually collect data from customers as they engage with you. The additional data you get through ongoing loyalty will help you improve their experiences. 

  1. Inaccurate Data

Inaccurate data happens when information is recorded incorrectly, often through manual data entry errors, incorrect automated processing errors, or a lack of data validation when the information is recorded.

Poor data accuracy is difficult to detect, and can even go unnoticed for extended periods. Businesses could misidentify customers, delivering experiences that are completely irrelevant or wrong, leading to poor customer experiences. Incorrect data can also reduce the effectiveness of customer insights and result in decisions that don’t support business objectives.

How to Fix It

Fixing inaccurate or incomplete data requires time and resources that could be better used elsewhere. The key is to put processes in place that check the accuracy of data as it’s ingested and stored to reduce these costs.

One way to do this is by using automated validation processes that check incoming data for accuracy and completeness before storing it in a database. This ensures that any incorrect information is flagged immediately before it becomes part of the company’s system. 

  1. Inconsistent Data

Inconsistent data occurs when the same record is recorded differently across data sets. For example, a contact record in a CRM database may have a different structure for a contact record than a customer support system, or a marketing automation system. 

Improper customer data management can lead to inaccurate reporting, duplicate records, and difficulty accessing vital information. Duplicate records can also lead to inconsistent or unfavorable customer experiences, if different parts of the organization contact the same customer in different ways. 

How to Fix It 

The best way to ensure inconsistent data doesn’t occur is to implement unified data collection and storage standards. Automated validation processes can also quickly detect any inconsistencies.

In addition, periodic testing allows you to identify discrepancies between different versions of customer records, and ensures the most up-to-date version is used consistently across business operations.

  1. Outdated Data

Customer data constantly changes, so it wouldn’t be surprising to find outdated or irrelevant data in your systems. Customers change jobs and addresses; get married or divorced; stop using products or services; and so on. If these changes are not caught, you may make decisions using the wrong data. 

Imagine sending an offer to a customer for an expanded service contract for a product they no longer use. Your customer data must be representative of the current situation for each customer in order for it to be relevant.  

How to Fix It

Trying to manually keep track of customers in real-time is impossible. An automated process that regularly validates and updates existing customer information will help reduce errors and outdated records. 

Treasure Data includes audit features that track when data is added or updated, giving you an understanding of how old your data is, where the data came from, and who made changes to it. You can also use Treasure Boxes that integrate with third-party data to enrich and update your customer data with  information.

If you manage zero-party data through a membership program, you can also encourage customers to help keep their information updated. Tracking changes in customer preferences over time helps anticipate demand for certain products or services while avoiding costly mistakes. 

  1. Non-Standardized Data

Decision-making rarely happens on a single data set. Instead, multiple data sets are brought together to give organizations a single customer view, allowing them to make better, more accurate decisions. 

When data is not uniform or standardized across data sets, it makes it challenging to unify data for analysis and decision-making. Data standardization is essential for ensuring data accuracy and quality. Without it, customer records can become inconsistent, leading to confusion and inefficiency. 

How to Fix It

Automated validation checks and business intelligence (BI) tools can help maintain accurate records by identifying discrepancies between data sets. Additionally, clear standards should be set for how data is collected and entered into the system. These standards can be applied through workflows that validate data and apply standards. Finally, regular reviews of the customer database are necessary to identify any inaccuracies or non-standardized entries before they become a problem. 

Fix Your Customer Data Problems

Data quality is essential for providing great customer experiences. Organizations can ensure that their data is consistent, accurate, and up-to-date by taking proactive steps to validate and automate customer data. 

With Treasure Data, you can ensure that your customer data is accurate, complete, and ready for activation, with a range of capabilities and integrations to help automate data hygiene. Focusing on high-quality customer data means you are well-equipped to succeed in an ever-changing marketplace. 

You can learn more about Treasure Data’s data governance capabilities here.

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Jim Skeffington
Jim Skeffington
Jim Skeffington is a Technical Product Marketing Manager at Treasure Data. He has years of experience working with data, including as a financial analyst, data architect, and statistician. Recently, he was recognized by the Royal Statistical Society for his thought leadership in the fields of statistics, data science, and data research. He is also proud to serve as a Captain in the United States Marine Corps.
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