CRM data quality is a big problem for most B2B companies. While customer data quality in general is difficult, many organizations rely on their CRM to be a single source of truth. This means that CRM data quality becomes vital to success for sales, marketing, customer service, and even finance.
What is CRM data quality?
CRM data quality refers to the accuracy, freshness, completeness and usability of data in your CRM system. Data quality inside of CRM systems is difficult because each CRM includes many different “objects” (types of data) that have different attributes and relationships to other objects.
Common CRM systems include Salesforce, HubSpot, Microsoft Dynamics CRM, SAP, Oracle CX Sales, and Zoho. A little bit of research into CRM data models, and B2B customer data models in general, reveals how complex the underlying data structures are inside of these systems. In fact, it could be argued that these complex data relationships are the main reason CRMs are such a vital piece of the B2B tech stack.
Naturally, CRMs do not exist in a vacuum, so it’s important to remember that data quality is a consideration for every important SaaS application across marketing, sales, customer success, finance and product usage analytics. Whether you’re in the healthcare industry, managing a medical courier service, or any other sector, maintaining data quality is essential for effective CRM utilization.
How do you ensure data quality in CRM?
Ensuring data quality in CRM is crucial for maintaining accurate and reliable customer information. Here are some practices that ensure your data quality in CRM:
- Data validation: Implement validation rules and constraints to ensure that data entered into the CRM meets predefined criteria.
- Data cleansing: Regularly cleanse and remove duplicate, outdated, or irrelevant data from the CRM system.
- Data standardization: Establish standardized data formats, naming conventions, and data entry guidelines to maintain consistency.
- Data enrichment: Enhance CRM data by enriching it with additional information from reliable external sources.
- Data governance: Establish clear data governance policies and procedures to ensure data quality.
- User training and awareness: Provide training to CRM users on data quality best practices, emphasizing the importance of accurate and complete data entry.
- Regular data audits: Regularly audit CRM data to identify and rectify data quality issues.
- System Integration: Ensure seamless integration between the CRM system and other data sources or applications within the organization.
- Monitoring and reporting: Implement monitoring mechanisms and generate data quality reports to identify anomalies, trends, and areas requiring improvement.
- Continuous improvement: Continuously evaluate and refine data quality processes and practices (and incorporate feedback from customers).
By implementing these practices, organizations in the field of pediatrics EHR can establish a solid foundation for maintaining high data quality in their CRM systems. And as a result, they’re better able to make informed decisions, provide better customer experiences, and derive valuable insights from their data.
What’s more, we asked several marketing, sales and revenue operations leaders how they work toward high quality data inside of CRM environments.
Here’s what they said:
Igor Krasnykh, CEO and Founder of PowerSync
“In this age, CRMs have become a central hub for all customer data collected across many channels. The biggest problem we see is that there needs to be a common standard data structure. Every channel often captures different customer data; the same data might be stored in different formats; each channel dictates what data they require and what data is optional.
The first thing you should be doing is standardizing data entry by establishing consistent naming conventions, formatting, and validation rules to ensure that all data is entered in a consistent format.
If you have no control over how the data is captured, use integrations and custom logic to transform the raw data you get from the 3rd party to comply with the data standard at your company before storing it in a CRM. Using AI capabilities might be beneficial to help you transform the data. An alternate solution is leveraging AI to offer recommendations your employee can manually review.”
[Related: What is data unification?]
Matt Freestone, Founder and Marketing Director of Unmatched
“Ops teams need to work with the wider organisation to define the goals of the data within their CRM, if you’re collecting data for the sake of it, you’re going to end up in a mess. Ops teams should use their expertise to advise sales and marketing teams on how each data point can be used effectively across campaigns. If you start with an objective-based data approach, the rest will fall into place more easily.”
Joe Kevens, Director of Demand Generation at PartnerStack and the Founder of B2B SaaS Reviews
“Increase The Quality Of The Data You Feed It.
There’s a saying, “garbage in, garbage out,” that’s worth bearing in mind to improve CRM data quality. In B2B, there are loads of data that you could input in a CRM system. To build a high-quality prospect record, you should cover demographics, firmographics, and technographics. It’s important to feed your CRM this data from accurate quality data sources.
For example, at PartnerStack, we use a variety of sources: LinkedIn, Crunchbase, Zoominfo, and most recently, Keyplay, which enables us to use custom data signals that are attributes of our best-fit accounts. By combing these data sources, we feed our CRM high-quality data that helps power our go-to-market activities.”
Lucas Munisteri, CEO, Theia Strategies
“With so many places to store customer data it is difficult to have a unified picture of your customers. Syncari enables organizations to build golden records; you can format the data, enrich its content with other systems and then send that unified golden record out to your end points so that the data looks the same in every system. With Syncari you will no longer be YELLING at your customers in your marketing emails, and your sales team will know about all past due invoices. Data quality is a critical component of every business!”
[ Related: Top CRM Data Quality Tips from Operations Experts ]
What is the importance of data quality to CRM performance?
CRM performance is more than the speed of your CRM: it’s about the speed at which your sales team and other customer-facing teams are able to operate regularly. Data quality issues prevent your teams from working effectively, by making them spend more time manually curating, fixing and updating the records they work with.
Easily the most painful aspect of this is manual data quality work:
Why is manual data quality work so common?
Manual data work is ubiquitous inside sales, marketing and CS teams. Some of it is reasonable. But it’s out of control:
- 41% of b2b marketers cite “manual data wrangling” as their top challenge.
- For the last two years straight (2021 and 2022), over 50% of customer success teams are struggling to either collect data manually or turn it into usable insights.
- 57% of sales teams say their tech stack is harmful to their productivity. Why? Because of a lack of useful integrations.
Go-To-Market work in any of these teams feels so brittle and frustrating when even basic things like contacts and accounts don’t match up across systems.
But here’s why this matters:
- Manual data work causes GTM pros to leave jobs.
- Manual data work is also responsible for causing GTM pros to lose jobs.
1. People leave jobs because of pointless manual data quality work.
Export. Clean. Import. Fail. Clean again. Import. Worked. Validate success?
Login. Lookup. Fail to find. Login elsewhere. Lookup. Fail to find. SQL query. Fail to find. Slack the data team. Wait.
So much time gets lost to manual micro-workflows that should not exist.
Then, as someone in sales, marketing or CS who is most certainly not measured by their ability to find data, you look bad, feel silly, and still have to carve out time for your real job. Many turn to greener pastures in new roles. But that only helps sometimes:
Manual data work is the bane of my existence, but I’m afraid to leave and find the same thing somewhere else.
– The Unicorns are Dying from a Treatable Disease, RevGenius Magazine
2. People lose jobs because of pointless manual data work.
Who wants to pay for a headcount dedicated to CRM data cleansing?
Data wrangling jobs, like that “sales ops” analyst that is just a euphemism for manual CRM data entry work, are not fun. But worse, they’re easily expendable, at least when viewed from the top of the org.
This disconnect is a big problem, with half of executives are confident their data matches across the organization, while only 27% of ops pros agree. (Source: RevOps 2023 Survey)
If manual CRM data quality work sounds like your org, what can be done about it?
- Recognize it is primarily a systems issue. Your stack isn’t unified – it’s cobbled together. All systems suffer when the CRM hosts inaccurate, stale, or incomplete information.
- Study how much time is wasted to manual CRM data cleanups and lookups. Estimate how many leads and opportunities and critical context clues are lost to a lack of integrated systems.
- Pick one or two handoff points between systems and teams to automate (here’s a list of data automation tools to help). But keep the big picture in mind. No sense in picking up Zapier just because it’s cheap and quick, when it’ll lead to a mess down the road.
And remember, there’s not much point in having 8+ tools in your GTM stack if they don’t work well together.
So let’s move together towards the end of pointless manual data quality work, in CRM or elsewhere. With that, we’ll leave you with the definitive guide to data automation.
More CRM Data Quality Recommendations
Here are 16 answers to the question, “What is one tip you have for improving CRM data quality in B2B sectors?”
- Aim for Accuracy to Maintain the Quality
- Use Data Cleansing Tools
- Automate Mundane Administrative Tasks Like Data Entry
- Share Data With Different Departments
- Implement a Data Governance Strategy
- Implement a System of Data Validation and Verification
- Make Work Culture Itself More Data-friendly
- Combine Automated and Manual Data Entry Techniques
- Implement Data Enrichment Techniques
- Provide Regular Training and Support to Your Staff
- Provide Team Incentives
- Duplicate Contacts Must Be Tracked and Eliminated
- Train Users on Data Input Procedures
- Use Third-party Data Sources
- Improve CRM With Business Intelligence
Aim for Accuracy to Maintain the Quality
As an experienced business-to-business leader, I have seen firsthand the importance of having quality CRM data in order to maximize sales and customer loyalty. My tip for improving CRM data quality in B2B sectors is to ensure that your data is accurate and up to date.
Take the time to audit your data regularly and make sure that any new or changed contact information is updated. Make sure that you have complete, accurate data for all of your customers, prospects, and leads. Additionally, take the time to review and validate any new contacts that enter your CRM system. This will help ensure that your CRM data is up-to-date and accurate.
If in doubt with data, leave that out for current business outreach, until it is verified to be 100% accurate.
Use Data Cleansing Tools
I believe the best way to enhance data quality is by employing data cleansing technologies. Data quality concerns, including duplication or improper formatting, can be fixed automatically with the help of the right software solutions. While data purification solutions can save time and ensure correctness, manual reviews, and updates are still required to ensure that data remains current and relevant.
Automate Mundane Administrative Tasks Like Data Entry
Automating data entry saves your sales reps time and reduces the risk of human error to ensure your CRM records are accurate and complete.
Improper CRM data management can lead to bad data quality, which can clog your sales pipeline and lead to high bounce rates. But CRM data automation helps reduce the possibility of duplicate, incorrect, or missing data. And just as important, it ensures your data is always up-to-date.
Automating manual administrative tasks like CRM data entry frees up valuable time for your sales team to reach out to high-value leads and strengthen relationships with long-term customers.
Share Data With Different Departments
By sharing CRM data across different departments, you’ll be able to gain not only more data, but also get different perspectives that maybe you weren’t considering. Instead of keeping all the data to yourself and collecting it from one aspect, other departments could add factors that could possibly affect the quality of your CRM data quality and allow you to make sure it is of better quality.
Implement a Data Governance Strategy
One essential tip for improving CRM data quality is to implement a data governance strategy. By setting clear guidelines and processes for data entry, management, and analysis, you can ensure that all data is accurate, up-to-date, and relevant to your business objectives. Additionally, regular data audits and ongoing employee training can help to maintain high standards and drive continuous improvement.
– James Scott, Founder, Embassy Row Project
A Free Tool to Evaluate the Health of Your CRM Data
The data in your CRM might be high-quality, but it’s hard to know if you have missing data as well. One of the biggest challenges for modern revenue teams is a siloed tech stack that leads to blind spots. We built the CRM Health Grader to help shine a light on those blind spots. It’s a free, browser-based tool that gives revenue leaders a deep understanding of their CRM health based on 12 critical indicators. Once you know what sales data you’re missing, you can take steps to automate the flow of information to where you need it, such as your CRM.
Implement a System of Data Validation and Verification
One tip for improving CRM data quality in B2B sectors is to implement a system of data validation and verification. This means that every time a new contact is added to the CRM, the data entered is automatically checked for accuracy and completeness. This can include verifying the email address and phone number format, checking for duplicates, and validating the company name and industry.
Additionally, regular data cleansing and updating should be performed to ensure that the data in the CRM is accurate and up-to-date. This can involve running regular data hygiene processes such as removing inactive or outdated contacts, updating contact information, and verifying email addresses and phone numbers.
By implementing a system of data validation and verification, along with regular data cleansing and updating, B2B companies can ensure that their CRM data is of the highest quality, which can improve sales and marketing efforts and help drive business growth.
Make Work Culture Itself More Data-friendly
Establishing one’s very own work culture as more data-friendly is one of the best practices for improving CRM data quality. This means encouraging employees to adopt more data-driven strategies and setting up policies to regularly evaluate the quality of collected data. When everyone gets on the same page regarding data management and the corresponding mindset, quality is sure to improve as a result.
Combine Automated and Manual Data Entry Techniques
One of the most effective tips for improving CRM data quality in B2B sectors is to use a combination of automated and manual data entry techniques.
Automated techniques, such as using APIs or web scraping for data collection, can help to ensure the accuracy and completeness of the data, while manual techniques, such as double-checking the data and performing regular audits, can help to identify any discrepancies or issues. Taking things further by ensuring the data is stored in a centralized repository and implementing automated processes for updating the data can help to ensure that the data stays up-to-date and accurate.
Last but not least, creating a set of metrics to measure the quality of the CRM data can help to ensure that any issues are identified and addressed quickly. By using a combination of automated and manual techniques, and by implementing processes to measure and maintain the data quality, businesses can ensure their CRM data is accurate, complete, and up-to-date.
Implement Data Enrichment Techniques
Data enrichment involves enhancing existing data with additional, relevant information from various sources such as third-party data providers or public records. When you enrich your data, you can fill in missing gaps, correct inaccurate information, and add more details that help to create a more complete and accurate profile of your B2B customers. As a result, you get better insights into customer behavior, more effective targeting, and ultimately better customer relationships.
Automate data through software solutions that integrate with your CRM system to ensure adequate data enrichment. It is important to make sure that any data enrichment methods you use comply with relevant data privacy laws and regulations.
Provide Regular Training and Support to Your Staff
One tip for improving CRM data quality in B2B sectors is to provide regular training and support to employees on how to properly enter and manage data in the CRM system. This can help to ensure that all employees understand the importance of data quality and are equipped with the skills and knowledge necessary to maintain it.
Training sessions can cover topics such as data entry best practices, how to use the CRM system effectively, and how to identify and correct errors in the data. It’s also important to provide ongoing support and resources to employees, such as user guides or cheat sheets, to help them navigate the system and troubleshoot issues as they arise.
Provide Team Incentives
One tip for improving CRM data quality in B2B sectors is to encourage and incentivize employees to consistently input accurate and complete data into the system. While this may seem like a must in certain positions, it’s important to remind your employees that you appreciate their hard and accurate work.
This can involve providing training and support to employees on the importance of data accuracy, as well as creating a culture of accountability and responsibility for data management. Businesses can consider implementing gamification elements or other incentives, such as recognition or bonuses, for employees who consistently maintain high-quality data in the CRM system.
By promoting a data-driven culture and incentivizing employees to prioritize data quality, businesses can improve their overall data accuracy and make better-informed decisions to drive growth and success.
Duplicate Contacts Must Be Tracked and Eliminated
For improving CRM data quality in B2B sectors, tracking and eliminating duplicate contacts always helps. In general, CRM data needs to be regularly monitored and updated to avoid situations like duplicate contacts arising.
If multiple people from a single lead company reach out, your CRM will likely mark it as a duplicate contact unless you have specific rules to process and eliminate these simple CRM data quality errors.
Train Users on Data Input Procedures
If we want to keep our data reliable, I think it’s crucial to provide users with training on how to enter it. This includes teaching people how to enter data properly, checking to see that everything is being recorded correctly, and fixing any mistakes that are found.
In my opinion, errors and other problems with data quality can be minimized by instructing users on proper data entry processes.
Use Third-party Data Sources
I believe the best way to improve data quality is to include third-party data sources. Buying data from third-party sources and incorporating it into your CRM system is a necessary step. You can gain a deeper understanding of your clients and enhance data accuracy with the use of third-party data sources, which can supply information like firmographic data, financial data, and social media data.
– Gerrid Smith, Communications Manager, Property Tax Loan Pros
Improve CRM With Business Intelligence
One tip to improve CRM data quality in B2B sectors is to incorporate business intelligence into existing systems. Business intelligence allows companies to develop an understanding of their customers, leveraging demographic and behavioral information that can be used to inform sales and marketing efforts.
For example, incorporating a tool that tracks customer life-cycle stages—such as trial usage, purchase/subscription decisions, or renewal/churn rates—gives businesses the ability to strategically tailor sales and marketing campaigns according to customer behaviors. This strategy helps ensure targeted and personalized messaging throughout the customer life cycle, increasing the likelihood of successful customer engagement.