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CRM Data Hygiene: Best Practices to Improve Data Quality & Coverage
I challenge you to find a company where nobody complains about the CRM - ever. It’s like the junk drawer everybody has at home. You know it’s there, and you keep adding stuff to it but how often do you export everything, sort through it, and make changes to stop it from happening again? You might deal with it all right when it’s your own drawer, but now imagine sharing that drawer with everybody in the company, people adding and retrieving… ouch.
Poor data can lead to missing opportunities and loss of trust in the CRM by the team, meaning that they will be less likely to update it with the information they have. The CRM will thus become less and less reliable, and in addition to poor data quality and coverage, its integrity will be compromised.
In this article, we will dive into the concepts of data quality, hygiene, and coverage and see how we can use workflows to implement best practices.
Understanding Data Hygiene
What is CRM Data Hygiene and Why It Matters
CRM data hygiene represents the processes implemented to keep the data clean. All CRMs start from a clean base, whether you’re starting from 0 or doing a transition from one to another one. However, as time flies by and more data is accumulated in the CRM, it’s essential to have processes implemented ahead of time to ensure that the data is still qualitative.
The impact of poor CRM hygiene can be dreadful. A 2022 study by Validity estimated the revenue loss due to poor CRM hygiene at 10%. Outdated data prevents you from seeing some key opportunities, putting your team in a reactive position instead of a proactive one. It also restricts the possibility of an effective collaboration between sales and marketing.
Ideally, these processes prevent issues from happening. However, we often implement new processes based on current issues. Processes to ensure good CRM data hygiene include data reviews, automated enrichment workflows, dashboards to monitor key data points… and many more that we will see in a second.
Key Practices for Data Hygiene
There are many things you can do to maximize your data hygiene:
- Regular Data Audits
No matter how many processes you implement, it’s essential to double-check your data and adjust your processes to catch any gaps.
A good practice is to have a quick check at the end of each month and a thorough one at the end of each quarter.
Below are the main topics and questions you should ask during a data audit.
- Properties for each objects
- Is there any redundancy?
- What is the average fill rate for the properties?
- What are the less-filled properties? Why?
- Are we missing anything that would help us perform better?
- Do we have different views on the properties based on the team we’re in? For example, a BDR and an ops person will not necessarily look at the same thing.
- Lists
- How many lists do we have?
- What is the percentage of lists based on filters?
- Are they all used? If not could we archive them?
- How are they named?
- Workflows
- How many active workflows do we have?
- How are they organized?
- Are we clear on what each one does?
- When was the last time they were updated?
- How could some of them be improved?
- Users
- Are all users on the workspace active?
- Do they belong to specific groups/teams?
- Do they have the right permission sets?
- Tasks
- How many overdue tasks do we have in total?
- How many were due before 1, 3, or 6 months ago?
- What is the average number of tasks per person per team?
- Do you see any repetitive tasks that could be automated?
- Forms
- Are all the forms still active?
- Are they relevant?
- Are we asking for key properties?
- What could be removed?
- Reports
- How many of these dashboards are used on a daily, weekly, monthly, and quarterly basis?
- Any outdated dashboard?
- Are there any inaccurate reports?
- How often do you check the data on these reports?
- Integrations
- What flows in and out?
- Are there any integrations that you don’t use anymore?
- What could we use to bridge potential data issues? (👋)
- Defining and Maintaining Properties
Properties are at the center of your data quality and hygiene. So much depends on whether the important properties are being filled or if they’re being left out.
As we just saw with the list of questions for the data audit, properties must be relevant. Processes change and associated properties can become outdated, you need to ensure that the properties are still valid, and with that any workflow updating it.
To facilitate updating your properties, it’s important to have them documented, whether it’s a description directly on the property or by adding which properties are used in your different processes.
If it’s not something you’ve done before, CRMs like Hubspot enable you to export all of your properties.
- Setting Rules and Naming Conventions
Another key practice for data hygiene is setting rules within your CRM.
To ensure you have accurate, up-to-date, and consistent data, rules can include:
- Authorization sets: who has access to what, who can edit what, who can add new users etc. These are usually in your CRM settings.
- Object creation: how are the objects created? What are the rules to follow when creating a contact/organization/deal manually?
- Assignment: Who is the default owner at each stage?
- Qualification criteria: is there a lead, company and/or deal scoring system implemented? What are the criteria?
- Pipeline management: How are deals created? How do they move further in the funnel? What does the sales cycle look like? What are the different playbooks associated?
- Tracking and analytics: What are the key metrics tracked in the CRM? How often is the reporting shared with the team?
- Automated workflows: What is done automatically inside the CRM? Which properties are filled automatically? Who is the decision-maker on this? How often are they reviewed?
To make this process easier, remember to use naming conventions. There’s no right or wrong naming convention as long as it’s relevant to the object in question and most importantly the business. Including the date (when), owner (who), and subject (what) is a good rule of thumb. Feel free to use brackets, parentheses, slashes, and dashes to make it more readable.
For example, if we’re building outbound lists, we could have something like
Q4/24 [G] New position - CMO
- Data Cleaning Techniques
Some data cleaning techniques are already integrated as features within your CRM; such as options to easily manage duplicates, unengaged contacts or validate contact information.
However, when it comes to checking or filling in incomplete or invalid records, filtered views come in handy. Whether it’s part of your data audit or just your monthly data check, sometimes it just helps identify what is lacking in terms of data hygiene processes.
There might come a time when a CSV export to Google Sheets is required to enrich these properties with external sources and import them back to your CRM. After you’ve identified what needs to be done and done it once, think about how you can automate it!
- Ensuring Data Integrity
Ensuring data integrity requires you to have all these rules and processes documented somewhere, ideally in the company knowledge base. Break them down by order of importance and see how you choose to train your team on it. It can be a reading session followed by a Q&A during their onboarding, monthly refresher sessions on specific processes, updates via messaging tools…
Now we all know the pace at which things change and you need to have someone internally who is going to own that subject. Whether it’s to document the processes, train the team, run weekly, monthly, and quarterly checks, and mostly identify the gaps.
The key to ensuring data integrity is to automate your processes as much as possible so you leave just a tiny room for mistakes.
Automating Data Hygiene
To ensure the highest level of data quality, you can automate your data hygiene processes.
First, you need to perform an audit on your CRM based on the indications we listed previously. When that’s done, you can look at what can be improved and how to automate most of it.
Most CRMs are based on a contact and company logic, and most CRMs become outdated because these contacts and companies are no longer up to date.
Below are a list of Captain Data workflows you can use to automate contact and company enrichment based on the different data points you have:
Company
If you have their LinkedIn information > Extract LinkedIn Company Profile
You don’t have any LinkedIn information but you have the company’s domain > Enrich a list of domains with LinkedIn
You’re starting from 0 and only have the company names > Enrich a list of company names with LinkedIn
Contact
If you have their LinkedIn information > Extract LinkedIn People Profile
You don’t have any LinkedIn information but you have their emails > Enrich Email with Outlook & LinkedIn (People & Company)
You only have their full name and company name > Enrich leads from full name & company name
All of these workflows seamlessly integrate within your CRM logic, directly from your CRM or by launching workflows and getting results with Make.
If you’re using Hubspot you can check our dedicated playbook!
Improved data quality and coverage checks
Maintaining clean, accurate, and up-to-date CRM data is essential for optimizing your business operations.
By establishing routine checks and maintenance practices, you can ensure continuous data hygiene and address issues before they grow. Regular audits, even in the presence of automation, are critical to keeping the integrity of your CRM intact.
Ultimately, the benefits of improved data quality extend far beyond simple housekeeping. With high-quality CRM data, your team can make more informed decisions, improve customer engagement, and drive better marketing and sales outcomes. Regular data hygiene practices, coupled with automation, not only ensure data accuracy but also enhance overall business performance and collaboration.
