The customer data you collect will be used to help you make predictions and decisions to assist you in furthering your business goals. To implement these business decisions and strategies successfully, you need to have systems in place that ensure CRM data quality and accuracy.
Ensuring data quality and accuracy is key to creating and implementing data-driven business decisions.
Unreliable or poor-quality data can lead to impractical, poorly developed sales, marketing, and customer service strategies. These ill-conceived strategies can have detrimental consequences for your business.
While low-quality data can potentially wreak havoc on your business, high-quality customer data can do wonders for multiple areas of your business.
Quality customer data can help your business identify prime sales opportunities through trends in your data reports. You can then focus your sales efforts on the most profitable opportunities and allocate your efforts toward business actions that will bring the most reward to your business.
Additionally, ensuring data quality and accuracy can help you identify customer shopping behaviors and popular products or services. With this information, you can better develop new products and services that meet your customers’ wants and needs.
Impact on marketing
Your marketing efforts are essential to attracting new customers and clients. With the help of quality customer data, you can develop and launch targeted marketing campaigns that effectively reach your ideal customers. Additionally, personalized marketing campaigns can result in better customer conversion rates, improving your campaign’s return on investment (ROI).
Impact on customer service
When you’ve got high-quality customer data on your side, you can consistently deliver excellent service to your customers.
Timely data related to customers’ buying behaviors, interests, and preferred methods of contact allows you to assist them in the best way possible, provide personalized shopping experiences, and ensure every interaction with your business is a positive one.
Characteristics of quality data
There are four key indicators of quality data to look out for when ensuring data quality and accuracy:
Timeliness: Outdated data can lead to inaccurate or false predictions for sales and industry trends. Customer data should always be up-to-date and timely.
Accuracy: Quality customer data should be error-free to ensure accurate forecasting for sales, industry, and customer behavior trends.
Reliability: Quality customer data is reliable data you can trust. Your customer data should always come from sources you know, trust, and can ensure are reliable.
Consistency: The data you collect should be consistent, meaning it shouldn’t contradict other timely data you’ve collected, regardless of source.
How to ensure CRM data quality and accuracy
Ensuring your CRM contains quality data is critical in customer data collection. Below are three data management and organization processes you can employ with your CRM today to ensure your customer data is timely, accurate, reliable, and consistent.
Regular CRM data quality reviews
Data reviews look for outdated, incomplete, duplicate, or inaccurate data that could cause your team to make decisions without all the facts. Conducting regular data reviews is essential to preventing dirty data and ensuring you can trust what you see in your CRM.
The importance of regular data audits
Carrying out thorough data reviews at regular intervals is vital to ensuring your CRM data remains helpful in your organization. Audits help pinpoint and correct inaccurate data, like outdated or missing contact information, ensuring your contact information remains reliable.
With regular data audits, you reduce the risk of incurring unnecessary costs associated with poor decisions or wasted resources due to inaccurate data. Improving your CRM data quality through these reviews also enhances customer experiences and business decision-making, leading to growth.
How to review CRM data
Regular data review is much simpler if you and your team follow a few critical steps:
Create a data review schedule: Going too long between data reviews can lead to inaccuracies, whether from user error or leads and customers changing their information. Set a data review schedule and have your team ensure all your information is current. A good practice is to review CRM data quarterly to identify errors and inconsistencies.
Assign data review tasks to specific people: Divide data review tasks between several people so you can ensure certain tasks are completed. Consider who will be responsible for data input, generating analytics reports, and ensuring data accuracy. Assigning these tasks to specific people will help the process run more smoothly.
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CRM data cleaning, also known as data cleansing, is the process of modifying or removing customer data that is:
Inaccurate
Outdated
Corrupted
Inconsistently formatted
Duplicated
Datasets often include these kinds of errors, whether due to poor data input practices or data becoming outdated. During data cleaning, you examine all the data in your CRM, find any problems, and correct them to ensure your data is usable.
Data cleaning is an invaluable step in customer data collection because it ensures your CRM’s data is accurate, consistent, and trustworthy.
You may also have heard of data scrubbing, a more in-depth version of data cleaning involving an exhaustive search of your data for errors. Think of data cleaning as a casual wash and data scrubbing as an intense cleansing. Data scrubbing could be a part of your yearly data cleaning process.
How to clean CRM data
Understanding how to clean your CRM data before you begin the process is crucial. Follow these best practices for cleaning CRM data:
Update your CRM when you receive new data: Knowing how to edit and bulk edit your CRM data is crucial for properly cleaning your data. As soon as you become aware of a change you need to make, go into your CRM and update the records. This ensures your team continually operates with updated data.
Manually remove disengaged contacts: Keeping contacts in your CRM as long as possible may seem logical, but hanging onto bad contacts could slow your team down. Manually delete no-longer-useful contacts and make sure you unsubscribe contacts from your email campaigns who request to stop receiving them.
Automate data cleaning with a data cleaning tool: While manually cleaning data has its place, you might find automating certain aspects of data cleaning helpful. Plenty of third-party data scrubbing tools exist for you to use with your CRM. Your CRM may also detect and merge duplicate data for you.
Data validation
One way to ensure data quality and accuracy within your CRM is through data validation. Data validation is the process of reviewing customer data to confirm that it meets the standards and rules set forth by your business and industry.
Data validation is an important step in the customer data collection process because it helps ensure data accuracy and clarity. This process allows you to avoid any errors in your data and can help your teams better interpret the information.
This process verifies whether the data cleaning was successful and whether the information meets your company and industry standards. Essentially, data validation is the time to perform final checks before putting your data into use.
How to validate your data
Here are a few best practices for validating your CRM data:
Implement data validation rules: Before performing data validation, you need to set data validation rules. These criteria provide a set of standards by which you can judge your data to ensure its quality. Consider what rules might be appropriate for your company or industry, like a valid email format or specific date range.
Check for inaccuracies and redundancies: With your data validation rules in place, it’s time to perform data validation. Set up scheduled validation checks with specific datasets to keep from overwhelming your team. During this step in the process, you check whether the data is complete, accurate, and updated according to your rules.
Utilize automation: Depending on the tools at your disposal, you may be able to automate some steps in the data validation process. Automation can speed up the validation process and reduce the likelihood of error. Research some data validation tools that integrate with your CRM or automated data collection tools within your CRM to keep all your data organized.
Enforce data entry standards: Ensuring data is updated and accurate before inputting it into your CRM can reduce the time needed to validate it later. During the data validation phase, determine what processes you can enforce to ensure accurate data entry and provide your team with clear guidance on how to put data into the system. Perhaps your team needs to enter data into the CRM as soon as they receive it or create custom data forms to capture required information from leads.
Data governance
Data governance is the process of setting up internal standards and policies related to data collection and storage. Your business’s standards help “govern” how your customer data is collected, stored, analyzed, and deleted.
Essentially, it’s a set of rules you create for how your team manages the data you gather in your CRM.
4 pillars of data governance
There are many different answers when it comes to nailing down the four pillars of data governance—it’s a concept many people share, but everyone has their own ideas about what those four pillars are.
On this page, we’re giving you our take on the four pillars we think are most important:
Data quality: Your data should be high-quality, reliable information.
Data accuracy: Your data shouldn’t have any errors and should always be timely and accurate.
Data management: Your data should always be handled with care, meaning it should be stored appropriately to avoid the risk of data breaches. It should also be organized so it’s easy to navigate.
Data usage: You should always use the customer data you collect ethically and efficiently.
CRM data quality training
Another way to control CRM data quality is to provide training and educational resources for CRM users. Teaching teams how to use the system effectively should improve data input accuracy and increase user adoption rates, resulting in better data consistency across your organization.
Training could take various forms, including workshops, company-wide events, or one-on-one tutorial practice sessions. Any combination of these will help your users understand the best practices for CRM data entry and management.
It’s also vital that your users understand the importance of CRM data quality and how it impacts your business financially. Try to motivate teams to be part of the solution and provide continuous support to reduce the likelihood of costly errors.
With Nutshell’s help, ensuring data quality and accuracy is a breeze. You can easily comb through data and weed out any low-quality information to make room for high-quality data.
Start a free Nutshell trial today to learn more about how our all-in-one CRM can help you collect and store accurate and reliable data.
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