CRM Hygiene: The 5-Step Data Cleansing Process for Modern Business
The most important growth drivers for any business simply can’t succeed without a core infrastructure of high-quality, trustworthy data.
For most companies, the first step in building that advanced data foundation is to cleanse the trove of business data already on hand in their customer relationship management (CRM) system.
And while it’s easy to assume large enterprises have data cleansing down to a science, one major global survey found that CRM data decays by about 34% annually, with nearly half of users estimating their companies lose more than 10% in annual revenue because of poor data quality.
While the benefits are clear, the work of cleaning and updating a typical CRM can seem so overwhelming that many go-to-market leaders are unclear where to start and unsure how to get buy-in from executive leadership.
This CRM hygiene checklist, developed by the data experts at ZoomInfo, offers a step-by-step guide to creating a scalable, clean CRM that’s primed for the next phase of AI-enabled growth.
The CRM Hygiene Series
This blog is part of a comprehensive series of guides that dive deeper into each of the five steps in the CRM data hygiene process. Navigate to each step to learn more about each step, including how to apply them, why they’re necessary, and the technical aspects of it all below.
What is CRM Hygiene?
CRM hygiene is the practice of regularly cleaning and updating your CRM system to ensure data accuracy and relevancy. The process typically includes removing outdated contacts, correcting errors, and updating information to keep the database current.
Proper CRM hygiene helps improve customer relationships and supports effective sales and marketing strategies.
How to Cleanse Your CRM in 5 Steps
The best data cleansing solutions provide robust features that deduplicate, enrich and standardize your data from multiple sources with the help of automation. The first phase requires defining your business data needs.
1. Define Data Governance Rules
The first step in any CRM cleanup is defining the rules for an overarching data governance strategy, and tailoring company standards to fit the specific nuances of its business.
Properly defining your data governance system will have massive downstream effects, so take care to do it thoroughly. The definition stage requires answering a few key questions. Once those rules are defined, software with rule-based workflows can apply them at scale.
Here are some key steps in the initial Define stage:
- Total Addressable Market (TAM) of businesses: The largest possible group of businesses could potentially buy your product or service. Often expressed in terms of total revenue or number of customers.
- Ideal Customer Profile within your TAM: Of that larger TAM, the characteristics of typical business that is the best positioned to become a customer.
- TAM of business professionals: Similar to TAM of businesses, but focusing on the number of potential end users or individual buyers within companies.
- Ideal Persona Profile within your TAM: The specific traits of individual professionals you’re targeting in these businesses.
- Characteristics of duplicate data: Signs of repeated information in your system, such as shared corporate HQ addresses.
- Duplicate survivorship rules: Deciding which record “survives” in the event of a duplicate or multiple records that need to be merged into one.
- Matching rules: Specify how your system will know if it’s found a match — if two records share the same HQ address but have slightly different names, is it considered a match?
- Enrichment survivorship rules: Similar to duplicate survivorship — when records are enriched with new data, these decide which data points remain in place and which get updated.
- Naming conventions: Standard ways to render identifying data for companies, titles, departments, and other key data points. Record assignment rules: How your system will distribute data to members of the team, such as in a lead-routing system.
Define Stage Key Challenge:
Creating enforceable rules that sync with company needs and remain flexible for future change.
Navigating the balance between flexible and firm data governance rules is crucial to maintaining your CRM’s data quality. It’s a balance between where rules can adapt for expansion while still being robust enough to be respected across the company.
In the Define stage, overcoming this common challenge requires a detailed grasp of your processes and goals, getting everyone on board, and creating governance rules that are in line with the specifics of your company.
2. Analyze Existing Data
Now you can take those rules and apply them to your current data — determining which data to purge and which data to expand upon. Robust data management helps GTM teams do this at scale, relying on sophisticated algorithms that can evaluate a record’s validity, rate of duplication, and completeness to deliver a more comprehensive picture of your data’s current state.
For example, imagine your CRM has three entries for “Tech Innovations Inc.” under slightly different names, with each record containing unique contact and sales details. By applying Duplicate Survivorship Rules, you decide the record with the latest “Last Modified Date” will dictate which details persist post-merge.
Keep in mind that details like the “Sales Representative” from the entry with the highest sales will be retained. Next, you merge these entries into a single, updated record for “Tech Innovations,” ensuring the most accurate and relevant information is preserved, cleaning your CRM data.
Consider the following steps:
- Rate of duplication: Check how often the same information shows up more than once.
- Completeness ratio against TAM: Measure how much of your total addressable market is covered by your current data.
- Completeness level of existing records: Evaluate how full and detailed each piece of information in your system is — are there crucial missing fields?
- Validity rate of existing records: Overall, how much of your data is accurate and up-to-date?
A Key Challenge:
Keeping data accurate and consistent as it grows.
Maintaining precision and uniformity in large data sets is crucial during the Analyze phase. This step demands thorough checks of data quality and adherence to established standards.
Failing to ensure accuracy and consistency can weaken decision-making and affect overall performance.
3. Purge Bad Data
Removing bad data from your CRM sharpens accuracy, boosts smart decision-making, and increases productivity. It also does a lot to improve customer relationships, targets marketing more effectively, reduces waste, and helps ensure regulatory compliance.
Follow this three-step process with the help of a robust data management solution:
- Remove duplicate records: The simplest step, this can dramatically clean up a database and reduce myriad errors.
- Mass-delete businesses and professionals outside TAM: Companies and individuals who aren’t part of your target market can quickly clutter databases and cause wasted spend and effort.
- Mass-delete outdated records: Old or irrelevant information that’s no longer useful prevents targeting of accounts or individuals that no longer fit or have changed roles.
Purge Stage Key Challenge:
Keeping data clean without losing important account information.
Carefully removing incorrect or unnecessary data, while preserving essential customer information, becomes a major challenge during the Purge phase.
The result? Errors that lead to lost accuracy and revenue. Ensure any software or data services on your vendor list can demonstrate their ability to eliminate bad data while preserving your valuable market-ready data.
4. Enhance Your CRM Data
Enhancing your CRM fills the gaps in first-party data that can easily go stale — and once a data team has cleaned its CRM, enhancing with customer and prospect data points that you don’t already have provides an immediate lift to your data’s tactical value.
Say your CRM has records for “Acme Corp.” with incomplete contact information and outdated details on decision-makers. Enhancing and enriching the data allows the information gathered from all your crucial data points — including web forms, sales, and list uploads — to be appended onto their original records, including custom fields to collect supplementary information.
By integrating data from all your data points, you automatically update “Acme’s” record with the latest email addresses, phone numbers, and job titles for key contacts. You might also include new information, such as company revenue and industry trends that were previously missing. At this point, your CRM now contains a richer, more complete profile of “Acme Corp.” for hyper-targeted marketing campaigns and truly personalized customer engagement.
Key steps for enhancing CRM data:
- Fill gaps of existing records in the TAM: Update incomplete information for businesses and professionals you’re targeting, such as additional contact information or details on subsidiaries and territories.
- Add businesses and professionals not currently in the database: Enriching your data with new contacts and prospect accounts drives immediate value for go-to-market teams.
- Standardize field values: Making sure all information follows the same formatting rules creates uniform, scalable data that drives more consistent, predictable results.
- Segment data populations: Grouping similar types of customers or data points together makes management, modeling and forecasting simpler and more powerful.
- Score records: Not all prospects are created equal — scoring is a key step that turns a massive data set into a more actionable, prioritized asset for GTM teams.
- Assign records: Allocate customer information to the appropriate team members for follow-up or action, based on the routing system that fits your sales team’s needs and priorities.
Enhance Stage Key Challenge:
Ensuring proper data ingestion and survivorship to prevent data loss.
Enriching your data means you’ll run into some challenges. Namely, data survivorship and making sure you’re keeping, not losing, the right data. When blending first- and third-party data to enrich your datasets, you may have existing data in several fields, which raises the risk of data loss.
To prevent losing data, apply the survivorship rules you established in the Define phase. These rules work as the backbone that keeps the accuracy of vital data. Survivorship rules help you maintain data integrity long-term.
5. Maintain Your CRM
Don’t let all your hard work go to waste. The final and critical step in the CRM data hygiene process is careful maintenance, to assure that your CRM doesn’t backslide into a messier version that needs another wholesale hygiene update. This includes ongoing updates that reflect the changing nature of your TAM, such as adjusting parent/child linkages to reflect mergers and acquisitions as they happen.
Many businesses use advanced signals data, such as ZoomInfo’s Scoops, to stay informed about such changes in real-time. Automation is key: by subscribing to a service that supports data hygiene maintenance, you make sure teams work with data that’s accurate and useful, well beyond your hygiene program’s initial completion.
To maintain optimal CRM hygiene:
- Apply steps 2, 3, and 4 on a scheduled cadence: Regularly analyze, clean, and enhance your data to keep it up to date and infused with the most accurate information.
- Set up and maintain automated rule-based triggers: Leveraging automation to schedule key CRM maintenance activities keeps this important work moving on a predetermined timeline, reducing the chances you build up a backlog of critical cleanup work.
- Review and adjust your strategies based on performance insights: Regularly assess how well your CRM maintenance strategies are working and make adjustments accordingly, so your CRM isn’t locked into an outdated strategic framework.
Maintain Stage Key Challenge:
Scalable flexibility.
Maintenance requires continuously adapting and updating processes to keep data accurate in the face of frequent market changes, such as mergers and acquisitions.
Scaleable maintenance ensures that teams can continue to make informed decisions, build out their operations efficiently, and maintain a competitive edge.
Data Infrastructure Built for Growth
Keeping your CRM data clean enhances decision-making, strengthens customer relationships, streamlines marketing and sales, and boosts customer service.
But beyond the day-to-day improvements, a clean, up-to-date CRM sets your company up to take advantage of the next wave of disruptive, transformative technologies — generative AI tools, for example, can’t deliver their growth potential when they’re fed inaccurate, incomplete data.
Regularly updating and maintaining your CRM transforms it into a dynamic, living data asset that powers your entire GTM strategy.
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This article was written by Zoominfo and originally published here.
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