It’s common for a CRM to have multiple entries for the same company, each with slightly different ways of conveying the name — ZoomInfo, ZoomInfo Technologies Inc., ZoomInfo LLC, and so on. Each record might also contain unique contact and sales data.
These records need to be combined, cleansed, and merged into one. By applying Duplicate Survivorship Rules, which are defined in the first phase of a CRM cleanup, data teams can easily decide which records remain after the merge is completed.
It’s why Analyze is the second step in a comprehensive five-step CRM hygiene process. At this stage, it’s time to apply those rules, determining which data to purge and which data to keep and expand upon.
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.
A Key Challenge: Maintain Accuracy and Consistency
It’s all too common for data teams to lack an automated way of analyzing the vast amounts of data on hand, making consistent accuracy at scale that much harder.
This becomes a major challenge in the Analyze phase because it requires that teams meticulously verify the quality and uniformity of vast amounts of data — which is nearly impossible if you’re doing it manually.
Taking the time to properly analyze your data ensures you don’t end up with bad data in your systems after you’ve applied the hygiene process — bad data can quickly compound and severely affect a company’s strategy and direction.
To complete the Analyze phase, consider applying the following:
1. Rate of Duplication
What it is: This analysis uses the duplicate definitions outlined during the Define stage to run an algorithm against the current CRM data.
Why it matters: High-quality data is crucial for making informed business decisions and maintaining effective customer relationships. Alternatively, low-quality data with a high rate of duplication leads to misinformed strategies and lower customer satisfaction.
How to analyze: Use your CRM’s deduplication tools or third-party data cleansing software to identify and quantify duplicate records. Analyzing duplication rates involves comparing the number of duplicate entries against the total number of records, giving insight into the efficiency of your data management processes and how often you need to clean your records.
2. Completeness Ratio Against TAM
What it is: This is the analysis of first-party data against the total addressable market data as determined in the Define phase.
Why it matters: Assessing the completeness of your first-party data against the total addressable market (TAM) is essential for enabling data-driven business decisions. Understanding the current state of your data directly informs how you should strategically purge irrelevant records and enhance existing ones.
How to analyze: Compare the number of complete, actionable records in your CRM with the estimated TAM for your products or services. This analysis helps you see how much of your potential market is currently represented within your CRM.
3. Completeness Level of Existing Records
What it is: Completeness level of existing records measures how fully populated your CRM records are, accounting for missing information in critical fields such as contact details, company information, and interaction history.
Why it matters: Complete records are vital for effective customer relationship management. Missing or incomplete data can lead to missed opportunities, inefficiency in sales and marketing efforts, and a lack of personalized customer interaction.
How to analyze: Assess the completeness of records by identifying the number of key fields with missing data. This analysis helps prioritize and outline data enrichment efforts and lets teams know which data they want to focus on collecting.
4. Validity Rate of Existing Records
What it is: The validity rate measures the accuracy and correctness of the data in your CRM, ensuring the information conforms to predefined formats and is logically correct. For example, it determines which email addresses are in a valid format.
Why it matters: Valid data is crucial for the reliability of your CRM system. Incorrect or invalid data leads to costly communication failures, poor customer experience, and skewed analytics.
How to analyze: Apply data validation rules within your CRM to automatically flag records that do not meet specific criteria. Regularly review these flags to correct invalid data and maintain the integrity of your database.
Next Step in the CRM Hygiene Process: Purge Redundant, Outdated Data
The Analyze phase is pivotal in the CRM data hygiene process because it uses clear rules to sift through existing data, pointing out which data needs purging and which data needs to be expanded.
Analyzing your current data makes sure you’re only working with the most accurate, relevant, and valuable data — raising the quality and return on investment of your sales and marketing strategies.
By the end of this step, the foundation for the Purge phase has been set. Now, it’s time to mass-delete records that don’t serve your broader business goals.