Duplicate Matching Rules & Conditions in CRM
A Complete Guide to Identify Duplicate Records Quickly using Duplicate Matching Rules and Conditions in Dynamics 365
Table of Contents
Duplicate identification in Dynamics 365 CRM does not work by chance; it works with well-configured duplicate matching rules. These rules define what should be compared, where duplicates should be searched, and how similar records must be before they are flagged.
This guide explains what duplicate matching rules are, why they are critical for clean CRM data, and how to configure them end-to-end using DeDupeD, so users can quickly and easily identify duplicate records, even in complex CRM environments.
What Are Duplicate Matching Rules?
Duplicate matching rules act as the foundation of duplicate identification in Dynamics 365 CRM.
In simple terms, a duplicate matching rule answers three key questions:
- Which type of records should be checked?
- Where should possible duplicates be searched?
- Under what conditions should two records be considered duplicates?
Without properly configured rules, even the most advanced duplicate detection techniques cannot deliver accurate results.
Why Duplicate Matching Rules Are Critical
Many CRM users assume duplicates are missed because of bad data.
In reality, duplicates are missed because rules are either too strict or too loose.
Poorly configured rules lead to:
- Missed duplicates
- Too many false alerts
- User frustration
- Inaccurate CRM reports
Well-designed rules, on the other hand, allow organizations in the US, UK, Australia, and global markets to maintain clean, trusted CRM data at scale.
How DeDupeD Uses Duplicate Matching Rules
DeDupeD enhances Dynamics 365 CRM by allowing multiple, flexible duplicate matching rules per entity.
This means:
- One rule can focus on email-based duplicates
- Another rule can handle name-based similarities
- Another can focus on phone-based detection
This layered approach ensures maximum accuracy without complexity.
Types of Duplicate Matching Rules You Can Create
Duplicate matching rules can be configured to compare:
1. Same Record Type (Within an Entity)
Example:
- Contact → Contact
- Lead → Lead
Best for identifying duplicates created within the same process or team.
2. Different Record Types (Across Entities)
Example:
- Lead → Contact
- Account → Contact
Ideal for organizations where customer data flows across multiple CRM entities.
Step-by-Step: How to Configure Duplicate Matching Rules
Below is a complete, easy-to-follow walkthrough designed for clarity rather than technical jargon.
Step 1: Open Duplicate Matching Rules
- Open the DeDupeD App
- Navigate to Duplicate Matching Rules
- Click New Duplicate Matching Rule
This creates the framework for defining how duplicates should be identified.
Step 2: Name the Duplicate Matching Rule Clearly
Choose a name that describes:
- The record types being compared
- The purpose of the rule
Examples:
- Contact to Contact – Email Check
- Lead to Contact – Name & Phone
Clear naming helps CRM admins manage rules easily as systems grow.
Step 3: Select the Primary Record Type
The primary record type defines:
- Which record is the user currently working on
- Where duplicate checks will originate
Examples:
- Contact
- Lead
- Account
This selection tells DeDupeD which records to evaluate for duplicates.
Step 4: Select the Matching Record Type
The matching record type defines:
- Where DeDupeD should search for duplicates
This can be:
- The same record type (similar entity duplicate identification)
- A different record type (cross entity duplicate identification)
Example:
- Primary: Lead
- Matching: Contact
This setup helps prevent duplicate customers across entities.
Step 5: Choose Which Data to Display for Review
Admins can select which fields should be shown when duplicates are found.
This helps users:
- Quickly understand why a duplicate was flagged
- Make confident decisions
- Avoid accidental merges
Showing meaningful fields improves user trust and adoption.
Step 6: Apply Advanced Filters (Optional but Powerful)
Advanced filters allow rules to focus on specific subsets of CRM data.
Examples:
- Only recently created records
- Only active records
- Only records from a specific source
This makes duplicate identification:
- Faster
- More relevant
- Less noisy
Especially useful in large CRM databases.
Step 7: Decide Whether to Exclude Inactive Records
Inactive records often represent:
- Old customers
- Archived leads
- Closed accounts
Admins can choose to:
- Include them for full cleanup
- Exclude them to reduce noise
This flexibility helps align duplicate detection with business goals.
Step 8: Save the Duplicate Matching Rule
Once saved:
- The rule framework is created
- Matching conditions can now be added
⚠️ Important:
A matching rule alone does nothing until at least one matching condition is added and published.
Adding Duplicate Matching Conditions (Where the Logic Lives)
Matching conditions define how records are compared within a rule.
Each condition specifies:
- Which field to check
- Which field to compare against
- How similar values must be
This is where advanced duplicate identification truly happens.
Step-by-Step: Configure a Matching Condition
Step 1: Open the Matching Rule
- Open the rule you created
- Click New Duplicate Matching Condition
Step 2: Select the Primary Field
Choose the field from the current record:
- First Name
- Phone Number
This is the value users typically enter.
Step 3: Select the Matching Field(s)
Choose one or more fields from existing records:
- Multiple fields increase accuracy
- Broader coverage reduces missed duplicates
Step 4: Choose the Matching Technique
Admins can choose different matching approaches depending on the data:
- Exact
- Partial
- Contains-based
- Sound-alike
This ensures flexible and intelligent duplicate identification.
Step 5: Adjust Match Sensitivity
Admins can fine-tune:
- How strict comparisons should be
- How much variation is allowed
This balance prevents both false positives and missed duplicates.
Step 6: Decide How to Handle Blank Values
Choose whether blank values should:
- Be ignored
- Or treated as potential duplicates
Best practice is to ignore blank values.
Step 7: Save and Close the Condition
Repeat this process to add additional conditions if needed.
Step 8: Publish the Duplicate Matching Rule
Once all conditions are configured:
- Publish the rule
- Duplicate identification becomes active immediately
From this point forward, DeDupeD uses the rule to identify duplicates in real time and in the background.
What Are Advanced Duplicate Matching Techniques?
Advanced duplicate matching techniques define how Dynamics 365 CRM records should be compared when identifying duplicates.
Instead of asking:
“Are these two values exactly the same?”
Advanced matching asks:
“Do these two records likely represent the same real-world person or organization?”
DeDupeD enables this through multiple flexible matching approaches, each designed for specific data scenarios.
How DeDupeD Uses Matching Conditions
In DeDupeD, matching techniques are applied through matching conditions.
Each condition defines:
- Which field should be checked
- How it should be compared
- How strict or flexible the comparison should be
This gives CRM administrators fine-grained control over duplicate identification without technical complexity.
Technique 1: Exact Value Matching
Best for Clean, Structured Data
Exact matching identifies duplicates when values are identical character by character.
When to Use Exact Matching
- Email addresses
- IDs or reference numbers
- Standardized fields
Example
- smith@company.com
- smith@company.com
These records clearly represent the same contact.
Why It’s Still Important
Exact matching is fast, precise, and produces almost zero false positives, making it ideal for high-confidence fields.
Technique 2: Partial (Start-Based) Matching
Best for Shortened or Abbreviated Data
Partial matching checks whether the beginning of values is the same, even if the full value differs.
When to Use It
- First names
- Company names
- Abbreviated entries
Example
- Alexandra
- Alex
Although not identical, these values often represent the same person.
Business Benefit
This technique helps catch duplicates caused by shortened data entry, especially in fast-paced sales environments.
Technique 3: Partial (End-Based) Matching
Best for Consistent End Patterns
This technique compares the ending portion of values, which is useful when prefixes vary.
When to Use It
- Codes or identifiers
- Names with prefixes or titles
Example
- Mr. Smith
- Smith
By focusing on the ending portion, duplicates are identified more accurately.
Technique 4: Contains-Based Matching
Best for Incomplete or Embedded Values
Contains-based matching checks whether one value exists within another value.
When to Use It
- Truncated names
- Partial imports
- CRM data copied from external systems
Example
- Christopher Johnson
- Chris
Even though the second value is incomplete, it still points to the same record.
Why It Matters
This technique catches duplicates that are otherwise invisible in traditional CRM checks.
Technique 5: Sound-Alike (Phonetic Fuzzy) Matching
Best for Spelling Variations and Typos
Sound-alike matching identifies duplicates based on how values sound, not how they are spelled.
When to Use It
- Names with spelling variations
- International data entry
- Manual CRM inputs
Example
- John Smith
- Jon Smyth
To a human, these are obviously the same person.
To basic CRM logic, they are completely different.
Why This Technique Is Powerful
Sound-alike matching dramatically improves duplicate detection accuracy in:
- Global CRM environments
- Sales-driven data entry
- Marketing imports
Using Multiple Fields for Smarter Duplicate Identification
Real duplicates rarely exist based on a single field.
DeDupeD allows multiple fields to be compared together, improving accuracy and reducing false positives.
Example
A contact may be identified as a duplicate based on:
- Name similarity
- Phone number similarity
- Email pattern
This layered approach mirrors real-world identity recognition.
How to Configure Advanced Matching Conditions (Step-by-Step)
Below is a simplified, end-to-end walkthrough designed for clarity.
Step 1: Open Duplicate Matching Rules
- Open the DeDupeD App
- Navigate to Duplicate Matching Rules
- Open an existing rule or create a new one
This defines which records will be compared.
Step 2: Create a New Matching Condition
- Click New Duplicate Matching Condition
- Select the primary field you want to compare
Step 3: Select One or More Matching Fields
- Choose one or multiple fields from existing records
- Multiple fields improve accuracy
Step 4: Choose the Matching Technique
Select the matching approach that best fits your data:
- Exact
- Partial (start or end)
- Contains-based
- Sound-alike
Step 5: Adjust Match Sensitivity
Admins can fine-tune:
- How strict comparisons should be
- How many characters should be evaluated
- How flexible matching should be
This balances accuracy vs coverage.
Step 6: Decide How to Handle Blank Values
Choose whether:
- Blank fields should be ignored
- Or treated as potential matches
Best practice is to ignore blank values.
Step 7: Save and Publish the Rule
Once saved and published:
- Advanced duplicate matching becomes active
- Results are visible immediately
Real-World Example: Multi-Region CRM Data
A company operates in:
- The US
- The UK
- Australia
Each region enters customer names slightly differently.
Without advanced matching:
- Same customer appears multiple times
- Reports are inflated
- Customer history is fragmented
With advanced matching:
- Variations are detected
- Duplicates are flagged early
CRM data remains reliable
Real-World Example: Lead to Contact Duplicate Prevention
A marketing team imports new leads weekly.
Without matching rules:
- Existing customers re-enter CRM as new leads
- Sales teams waste time
With properly configured matching rules:
- Each new lead is checked against contacts
- Existing customers are identified instantly
- Sales teams focus on real opportunities
Common Mistakes to Avoid
❌ Creating too many rules without clear purpose
❌ Using only one matching condition
❌ Publishing rules without testing
✔ Best Practice:
Start simple, test results, then expand coverage.
Who Should Configure Duplicate Matching Rules?
Duplicate matching rules are essential for:
- CRM administrators
- Data governance teams
- Organizations with high data volume
- Businesses using imports and integrations
If duplicate identification matters, matching rules must be configured carefully.
FAQs
What is advanced duplicate matching in CRM?
Advanced duplicate matching uses flexible comparison techniques to identify duplicate records even when data is incomplete, formatted differently, or spelled differently.
How does CRM identify duplicates without exact matches?
By using partial, sound-alike, and multi-field matching techniques.
Is advanced matching suitable for large CRM databases?
Yes. Advanced matching is designed to scale and improve accuracy as data volume grows.
Can multiple matching techniques be used together?
Yes. Combining techniques provides the highest accuracy.
Why Advanced Duplicate Matching Is the Core of Duplicate Identification
Advanced duplicate matching techniques transform duplicate identification from a basic check into a smart, scalable data quality strategy.
With DeDupeD, organizations can quickly and easily identify duplicate records in Dynamics 365 CRM, even in complex, real-world scenarios—ensuring clean data, confident users, and better business outcomes.
What are duplicate matching rules in Dynamics 365 CRM?
Duplicate matching rules define how CRM records are compared to identify duplicates based on selected fields and matching techniques.
Do matching rules work in real time?
Yes. When combined with client-side detection, they work instantly as users enter data.
Are matching rules required for duplicate detection?
Yes. Without matching rules and conditions, duplicate identification cannot function.
Why Duplicate Matching Rules Are the Backbone of Duplicate Identification
Duplicate matching rules turn duplicate detection from a guessing game into a structured, reliable, and scalable process.
With DeDupeD, organizations can quickly and easily identify duplicate records in Dynamics 365 CRM, protect data quality, and maintain confidence in every CRM-driven decision.
Reach out to us today to know more!
