Salesforce has a built-in deduplication tool for removing duplicate records from leads, contacts, accounts, and opportunities databases. This tool is the primary method Salesforce offers for cleaning data. However, many customers find this built-in option lacking, especially considering the high-quality data that sales and marketing teams rely on.
For example, imagine a newly hired salesperson pulls up a list of contacts for a specific account in Salesforce. In the potential client’s contact record, a person named ‘Bob Smith’ is listed as the purchasing agent, so the salesperson adds them to the recipients list for an upcoming new product release announcement, assuming this would be the best point of contact for that potential client. Let’s say that, on a follow-up call, the salesperson discovers that Mr. Smith no longer works for that company. Bob Smith changed jobs, and somehow, a duplicate record was created for him under the new employer.
This may not sound like a catastrophic mistake, but this error created additional work, and now, that new salesperson has reason to doubt the reliability of the data in Salesforce. That remaining duplicate record could be the only one in the database, but this seed of doubt can translate into extra hours spent double-checking and verifying information. And suppose it’s not the only duplicate out there the salesperson encounters. In that case, they may even start maintaining a separate shadow database, relying on their notes rather than the database in Salesforce. It’s easy to see how a few stray duplicates can snowball into a bigger problem.
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Salesforce Duplicate Management Tools
Duplication identification tools rely on field comparisons. By comparing the contents of a selected number of fields within each record and across the entire database, these tools can determine whether records are unique or duplicative. The record is classified as a duplicate if the content within the chosen fields matches. The system notifies relevant users of the potential duplicate so they can determine which records should be deleted or maintained. Restrictions can also be put in place to prevent users from creating duplicate records in the first place.
Each organization creates its deduplication logic. Data sources, workflows, and business processes determine how programming logic is applied to determine duplicates and decide which records are retained and which are removed. However, there are basic data-cleaning steps that can help optimize built-in tools.
Step 1. Normalize Data
Data normalization sets a standardized data format for all data sources. For example, if a field contains a date, users can set a consistent date format, such as MM/DD/YYYY or DD/MM/YYYY. If the field includes phone numbers, they can be standardized with either periods or dashes between the digits. Once data normalization standards are set, the data must be converted to the desired format either manually or programmatically.
Without clean data, fields can get skipped, calculations can be faulty, or records can be counted multiple times. Ensuring that your Salesforce data is error-free is essential for effective decision-making.
Step 2. Identify Duplicates
Salesforce has multiple fields that can be used to identify duplicate records. These include:
- email addresses
- telephone numbers
- website domains
- company names
While deduplication can work using just one field, matching on multiple fields generally produces better results.
If the data is being imported from multiple systems, removing duplicate records before importing reduces the time needed to remove them once inside Salesforce. Consider using an external platform if cleaned data needs to be pushed out to various systems.
Step 3. Develop Deduping Logic
Once duplicate records are identified, decisions have to be made about what to do with the duplicates. This step includes answering the following:
- Which record should be retained?
- Which records will be removed?
- Will the records be merged? And if so, which record retains what data??
Deduping can be a process of trial and error. That’s why Step 4 is so crucial.
Step 4. Test, Test, and Retest
Do not test deduping logic on the master database. Instead, create a test environment where logic can be tested at a smaller scale. Tests will run faster, and results can be analyzed to refine deduping logic continuously until 100% of accurate duplicates are removed. Once the logic has been thoroughly tested, apply it to the entire Salesforce CRM and validate the output.Â
Why Do You Need Data Automation?
Although Salesforce duplicate management tools can help identify duplicates, the deduplication process is manual, making it time-consuming and labor-intensive. With limited matching rules and simple duplication algorithms, some duplicate records can be missed. Data automation tools often include comprehensive matching rules and duplication algorithms to ensure all duplicate records are identified.
Salesforce deduplication tools do not provide automated data normalization or check for cross-object duplications. This means inaccurate and inconsistent data can exist across leads, contacts, or accounts. Also, Salesforce management tools do not enforce data governance policies. For companies with regulatory or compliance requirements, separate processes must be put in place to ensure adherence to standards. With more advanced, intelligent data automation tools, data governance policies can be established to ensure all requirements are met.
Salesforce’s built-in duplication management tools are a starting point for most organizations. However, as businesses grow, they often need to improve their functionality. Most companies eventually need data automation to realize the full benefit of Salesforce CRM. If your business is facing any of the following scenarios, consider exploring a trusted AI-powered deduplication tool suite for Salesforce data import and cleaning,
Increased Data Volume
Growing companies generate more leads, acquire customers, and receive more opportunities. Humans are prone to making errors, and if data volumes increase, manual data entry errors will grow exponentially. In these scenarios, we recommend a leading data quality solution such as DataGroomr as they can process large datasets and prevent duplicate records from being created when importing CSV files. Their tools can review, update, and enrich existing records using new information in imported files, and they automate recurring duplicate analyses and reduce manual processes. Mass merges and conversions can be set to occur automatically, freeing up more resources. AI technology definitively delivers a more robust duplicate management solution, while allowing organizations to reallocate resources to more critical tasks while maintaining a duplicate-free Salesforce CRM.
