Deduplication Best Practices

Insycle uses the existing data in your CRM to identify duplicate records. Matching duplicates requires unique identifiers—data that is unlikely to be shared by any other record unless it is a duplicate. If you don't use unique identifiers, you may identify unrelated records as duplicates.

For instance, many CRMs capture first names, last names, and email addresses. If all of those match or are similar, you can confidently determine that the record is a duplicate.

Define what kind of likeness to look for when deciding if field values should be considered a match.

Commonly used unique identifying fields include:

• First and last name
• Company name
• Email address
• Email or website domain
• Phone number
• Website URL
• Various ID numbers
• Mailing address

Most CRMs include very basic duplicate detection systems. These systems often identify duplicates using name, email, or company name, which is an effective way to find surface-level duplicates.

But by broadening how you match duplicates, you'll be able to identify more of them in your database.

First, you can use both exact and similar matching in Insycle. With similar matching, you can match duplicates even when the data has slight differences between the two duplicate records.

Using these options, Insycle's pre-built deduplication templates can help you match duplicates in various ways.

You can require that field values are an Exact Match to be considered a duplicate:

Or you can use the Similar Match Comparison Rule on one or more of the fields to account for close variations on a value:

If you'd like to look at the data in two different fields (that contain similar data) as if it were one, you can set up Related Fields under the Advanced tab. For example, you might want to look at both the Phone Number and Mobile Phone Number fields for duplicate values:

Building multiple templates for deduplication allows you to catch and merge more duplicates that are gumming up your processes.

Learn more about detecting duplicates using advanced methods:
Hidden Duplicates: 11 Advanced Ways to Identify & Deduplicate Customer Data

The Similar Match Comparison Rule found in Step 1 will detect matches that are two keystroke deviations away from each other, like:

• insertion: bar → barn
• deletion: barn → bar
• substitution: barn → bark

It looks for values that may be close but with a one-character difference (maybe a typo) and broadens the search.

This search behaves like when Google shows results for a slightly different term or says, “Did you mean...” For example, if an Email address of “hueyy@coahulldu.co” is found, it could include records with the Email values “hue.y@coahulldu.co, huey@coahulldu.co,” etc., as a match.

Similar matching can be a great way to detect more duplicates, but you have to be thoughtful about how it is used, or you risk matching non-duplicate records and merging them together.

Similar Match uses looser criteria that cast a wider net for what can be considered duplicates. It is best to try Similar Match with very open and generic fields after trying everything else. When you do use it, make sure to carefully review the results to ensure the duplicates being identified are what you're expecting.

For example, if you used Similar Match on a phone number, other contacts who work at the same organization might be mistakenly identified as duplicates because they have similar numbers. 

When you merge duplicates, you should consider how data is merged.

Rules to Automatically Chose the Master Record

Under Step 4 on the Records tab, you define how all of the matching duplicate groups should be merged at scale. To do this, you need to create a series of rules that tell Insycle how to select the record from each group to become the master. The master is the record that will remain after the merge.

For example, if you had four records representing the same company, they would make up one duplicate group with four records, all of which would be merged into one master record. The other three records would no longer exist.

The master record for each duplicate group is determined using rules via an elimination process. Rules are read in order, from top to bottom. As soon as a record meets one of the criteria, this becomes the master and the rest of the rules are skipped. So if a record meets the first criterion (in the example below, the first criterion is “highest number of marketing emails clicked”), it is chosen as the master. 

Configure Rules That Determine Values to Keep

The Merge Duplicates module allows you to control the values saved in the master record after the merge, regardless of the default merge behavior. By adding each field you want to control the data retention for and selecting a Condition, you can tell Insycle where the data for the field should be taken from and how to handle it.

Under Step 4, click the Fields tab. For each field you want to control the data retention for, you need to select a Field and tell it where the data for the field should be taken from. This is merged into the master. Any data that is not in the master or not copied to the master is deleted.

The Criteria dropdown gives you various options for choosing the data to keep, and the Group Fields let you keep values of multiple fields from the same record:

• From record where value – Select data from one of the records in the duplicate group based on the values. These options vary depending on the field type. For example, if you select an Owner field, you can select “From record where value” and the Condition “active user.”
• Most frequent value* – If the same value appears in multiple records, use the one that appears most frequently.
• From record based on other field value – Look at the value in a different field to decide which value from the duplicate group should be kept. The example above highlights how a Last Modified Date value can be used to determine which Lifecycle Stage value to use.
• From master record – Use the value that exists in the master record.
• From master record (even empty) – If the field on the selected master record is blank, keep it that way. Don’t automatically fill it in with a value from the most recently updated record.
• Combine and append all values* – You can merge the values from the selected field for all records in the group. For example, if there is some type of Notes field, you could keep the notes from all of the records in the duplicate group.
• Collect non-master values from other field* – Aggregate the values of all the duplicates that are not the master and not the same as the master, meaning all instances of that value are excluded from collection. This can be especially helpful if you want a record of the object IDs that were removed, so you can also remove them from another system and keep the master. Select a destination field to copy and combine values into, then select what field the data should come from.
• Collect all values from other field* – Select a destination field to copy and combine values into, then select what field the data should come from. For example, this could be used to keep the record Owner values of all duplicates and combine them into a custom field.

* Indicates Criteria options that do not allow you to Group Fields.

  When one or more fields are added to the Fields tab, a special row –Master (After)– will appear in the CSV report. For each duplicate group, the Master (After) row shows the values the final record will contain based on your Field rules and the default behavior. 

When you click the Review button under Step 5, you can choose between two modes: Preview Mode and Update Mode.

Preview mode does not update your live CRM data, but instead produces a CSV showing you the results that the deduplication process would have produced.

To save time and ensure that your duplicates merge consistently on an ongoing basis, you can set up automated deduplication using scheduled templates or Recipes. HubSpot users can also integrate Insycle Recipes with Workflows. 

Automate a Single Template

First, return to the Template menu at the top of the page and click Copy to save your configurations as a new version of whatever template you started with. Then click the pencil to edit your new template name.

Under Step 5, click the Review button, and select Update mode.

On the When tab, select Automate, and configure the frequency you'd like the template to run. When finished, click Schedule.

Each row in your matching fields setup is cumulative, so records must meet all of the criteria. For example, looking for records that have the same First Name and Last Name and Phone Number returns only results where all three values are the same.

  The minimum required length for the matching values is four characters or more. Values such as "Joe" or "Ace" will be disregarded.

Sometimes, you might want to match duplicates using data in two separate fields. For example, you might want to compare your Phone Number field to a Mobile Phone Number field to identify duplicates.

Using the Related Fields feature, you can use two different fields (that contain similar data) as matching fields to catch more duplicates.

You can set up Related Fields in the Advanced tab.

Common Examples of Related Field Matching

When using two or more fields to identify duplicates, records can still be considered matches even if one of the field values is blank. You just need to specify which field(s) allow a blank value.

Under Step 1, configure your matching rules in the Simple tab, then click the Conditions tab.

All the matching fields you included will automatically appear with the Value Required in All Records condition selected. Change the condition to Empty Allowed in Any Record to allow empty values for certain fields. You can also use the At Least One Record with Non-Empty condition to help you determine which is the master record. Make sure at least one field remains required and is a reliable unique identifier to ensure the records are really duplicates.

For example, on the Simple tab, you may have the matching fields: First Name, Last Name, and Phone Number. But on some of your records, the Phone Number field may be empty. Using the conditions "Empty Allowed in Any Record," or "At Least One Record with Non-Empty," all records with the same name, same phone number, and no phone number will be considered duplicates.

Priority Match: Looks through the master selection rules in order, one by one. As soon as a record meets one of the criteria, Insycle makes the master selection and skips the rest of the rules on the list. The vast majority of duplicate templates should use Priority Match.

Absolute Match: The master record must meet all of the listed rules in the Record tab in Step 4. If a record does not match every rule listed, no master record will be identified. Absolute Match is appropriate for less flexible master selection.

For example, if a company wanted to ensure the chosen master record is in their sales pipeline and already has a sales rep working the record, they can choose Absolute Match and set the Record rules:

• Lifecycle Stage is lead
• Contact Owner exists

Choosing Absolute Match can often result in no master record being identified since the record has to match every rule listed, so in most cases, you should select Priority Match.

Let's say we have four records that represent the same person—Marta Vaskovitch. The Merge Duplicates module will identify this as one duplicate group consisting of four records.

Here is the data that we have for this duplicate group:

Here are the master selection rules we have set up:

We haven't sent any emails to Marta yet, so when Insycle processes the first three rules—Marketing emails clicked, emails bounced, and emails opened—Insycle cannot eliminate any record because they all have the same value of zero.

In the next rule about contact owner, records 61301, 61201, and 61251 are eliminated since no contact owner exists for those records. Now, only one record remains, 61351, therefore that's the master record.

For contacts, it's often useful to pick master records based on engagement. For example, the highest number of email clicks or the most recent email opened. You can also use other statuses to pick a master record, such as the furthest along in your sales lifecycle or the most recently updated record.

For companies, it's often useful to use associated records to determine the master record, such as the highest number of associated contacts or deals.

If you have a connected app, like Salesforce or an ERP system, pick the master record that is syncing with the other apps.

Though it's possible that duplicate records may be exactly the same, often there is only partial data overlap between them. When data is split between two different records, both records may contain unique and important information about the customer you'd like to keep.

By default, Insycle will keep the master record values; if the master field is blank, the value from the most recently updated duplicate will be used. The Merge Duplicates module allows you to control the values saved in the master record after the merge, regardless of the default merge behavior. By adding each field you want to control the data retention for in the Fields tab under Step 4 and selecting a Criteria, you can tell Insycle where the data for the field should be taken from and how to handle it.

If you want to preserve data from several fields based on one Criteria, select them under Group Fields. Note that this is not available for all Criteria.

If you want to preserve the record IDs of the duplicates that are merged into the master record, you can add a new custom field, “Insycle Merged Record IDs,” to your CRM.

Then, in the Merge Duplicates module, under the Fields tab of Step 4, add a rule to override the default merge behavior. Select the "Insycle Merged Record IDs" field, the "Collect all values from other field" criteria, and "Record ID" as the other field. 

You can use the Preview to see how this will preserve the Record IDs of all the duplicates in each duplicate group.

You could set multiple criteria for the same field, which can be useful when establishing a hierarchy for selecting the value so "Value A" takes precedence over "Value B."

In this example, two different rules are defined for the 'Lifecycle Stage' field. The first rule states that if a record has the value "Customer," that value will be merged into the master record. However, if no records match the "Customer" criteria, the second rule for "Opportunity" comes into play.

For situations where there are no common rules you can apply for identifying duplicates for all or some of the records, you may need more granular control for picking records to include or exclude from the process.

To do this in bulk, you can create a CSV and use the Magical Import module.

In these cases, you can use CSV files to customize your bulk merging, designate master records, and exclude records from deduplication. Then you can import the CSV from the Magical Import, and use the Merge Duplicates module for complete control over the final merge operation. Learn how to customize merging Duplicates in bulk using a CSV.

To do this one record at a time, you can use Manual mode of the Merge Duplicates module.

In Manual mode, you have complete control over which records are merged together by selecting them from the Record Viewer. Manual mode should be reserved only for cases where you need a careful, controlled process. Learn more about merging duplicates in Manual mode.

Currently, neither HubSpot nor Salesforce provides an automated way to prioritize active owners during the merge process. You'll need to verify owner status manually for each merge operation.

When merging records in Insycle, you can create a Field rule under Step 4 to keep the owner who is an active user.

Add a rule with the following parameters:

• Field: Owner
• Criteria: From record where value
• Condition: active user

When using two or more fields to identify duplicates, records can still be considered matches even if one of the field values is blank. You just need to specify which field(s) allow a blank value.

Under Step 1, configure your matching rules in the Simple tab, then click the Conditions tab.

All the matching fields you included will automatically appear with the Value Required in All Records condition selected. Change the condition to Empty Allowed in Any Record to allow empty values for certain fields. You can also use the At Least One Record with Non-Empty condition to help you determine which is the master record. Make sure at least one field remains required and is a reliable unique identifier to ensure the records are really duplicates.

For example, on the Simple tab, you may have the matching fields: First Name, Last Name, and Phone Number. But on some of your records, the Phone Number field may be empty. Using the Condition "Empty Allowed in Any Record" or "At Least One Record with Non-Empty," all records with the same name, same phone number, and no phone number will be considered duplicates.

Yes. This can be done, for example, if you want to look at both the Phone Number field values and Mobile Phone Number field values as a single pool of values to compare between records and identify duplicates.

Using the Related Fields feature, you can use two different fields (that contain similar data) as matching fields to catch more duplicates. You can set up Related Fields in the Advanced tab.

Currently, there are two ways to make sure that the records that you are merging are indeed duplicate records.

First, always run your deduplication templates in Preview Mode before running them in Update Mode. This produces a CSV that shows you how your records would have been merged. Then you can ensure that your Merge Duplicates template is working as expected and not merging non-duplicate records together.

Additionally, to ensure a smooth merge process, consider narrowing down the matching settings in Step 1. Try the Exact Match Comparison Rule instead of Similar Match. Then make sure that you are using actual uniquely identifying fields—first name, last name, email, and phone number are popular choices. The more tightly defined your filter is, the less likely you are to merge non-duplicate records.

If the Result column of the CSV report displays this error:

Cannot determine master record because multiple records (#) satisfy the master selection rules. In ‘Master Selection’, change/add/reorder the rules such that only one record satisfies them (if cannot determine master based on field values, use ‘ID is lowest’ as the last rule).

This error means that based on the master rules you set, Insycle could not figure out which would be the master.

Check Step 4 to ensure that you have Priority Match selected and not Absolute Match.

With Priority Match, the rules configured in the Records tab of Step 4 are processed in order and your master record only has to match one rule. Using Absolute Match, your master record would have to meet all of the rule criteria. The majority of the time it is best to select Priority Match.

If Priority Match was used, then none of the records meet any of the criteria on the list more than the others. In this case, you'll need to experiment with Step 4, reordering or adding additional rules for fields likely to have unique values.

Yes. You can merge specific records using a CSV file containing the records you want to combine. Here's how:

• Prepare a CSV file with columns for the record IDs and a "Merge Master" column. In the "Merge Master" column, mark which record should be kept after merging.
• Create a custom field called "Merge Master" in your CRM.
• Use the Magical Import module to import your CSV file into the CRM, updating the "Merge Master" field for the relevant records.
• Go to the Merge Duplicates module and set up a filter to select records based on the "Merge Master" field.

Learn more about customizing bulk deduplication from a CSV.

Yes, Insycle allows you to select which field data is retained in the master record using the Fields tab under Step 4. See the Bulk Merge Duplicate People, Companies article for more details.

Yes. You can exclude records from deduplication by creating a CSV with a "Deduplication Exclude" field.

First, you'll export a Preview CSV from the Merge Duplicates module, add an exclude column, and specify which records should be excluded from the merge process. Next, create a custom field in your CRM to facilitate the merging. Use the Magical Import module to import the edited CSV file into the CRM, populating the new custom field. Finally, utilize this custom field to merge the remaining duplicate records in the Merge Duplicates module.

Learn how to customize bulk deduplication using a CSV.

Yes, there are several ways to share details and get approval before merging duplicates.

You can manually approve master records and mark them in a CSV, then use Insycle to bulk deduplicate down to those master records. Consult with this Customize Bulk Deduplication Using Exclusions and Pre-Defined Masters article to learn more.

Or, you can run the Merge Duplicates module in Preview mode and then deliver the preview CSV that Insycle generates. The CSV report that Insycle generates includes your entire merge operation down to individual duplicate groups but does not update your live data. Then your team can approve the merge based on this report, before running Merge Duplicates in Update mode.

Additionally, team members can review duplicates and manually select the master for each record under Step 4. Review the Manually Merge Duplicates article for more detail.

No, the field values do not need to match exactly. The Similar Match Comparison Rule found in Step 1 looks for values that may be close but with a one-character difference (maybe a typo) and broadens the search.

This search behaves like when Google shows results for a slightly different term, or says “Did you mean...” For example, if a Company Name of, “Acme” is found, it could include records with the Company Name values “Akme, acm, Acma,” etc., as a match.

You should be careful when using Similar Match as the looser criteria can incorrectly identify non-duplicates as duplicates. 

Review the Understanding Similar Matching best practices for more detail.

Why can I only process 50 duplicate groups at a time?

Insycle shows 50 records on the module screen as a preview; this isn't the entire list of records. see To everything, include All records when you view the Preview CSV report.

Insycle can process thousands of duplicate groups in one operation. Potentially, you could deduplicate your entire database in one operation. 

You can merge up to 100 duplicate records into a single master record. 

If you have duplicate groups that contain more than five records, you may want to change the value in Skip duplicate groups with more than 5 records per group under Step 3 to make sure you can get them all.

This is a precaution to ensure that if you use a duplicate matching filter that is too broad in Step 1, you do not accidentally merge many non-duplicate records together. If you are going to set this number at a high level, it is a good idea to run Preview Mode first to make sure your deduplication template is operating as you intend.

All plans include unlimited usage, unlimited users, and unlimited operations. During the free trial, there is a cap of 500 records updated, cleansed, or merged. See the pricing page for more details.