Merge CRM records without losing data

In Dedupely, you can control how duplicate records are merged by defining field-level merge rules. Instead of picking one record to overwrite another, field-level merge rules allow you to decide how each field behaves during a merge. This gives CRM admins the ability to merge at scale without overwriting useful data or relying on manual review.

Merge failures usually start at the field level

Many CRM tools merge based on record priority, but in practice, different fields require different logic: One record might have a fresher email, another a more complete job title. If the system picks one record to win overall, useful values are lost.

This leads to unpredictable merge results, errors in automation, and added cleanup. The problem isn't just detection, it's resolution logic that treats all fields the same.

Let fields drive merge decisions

Each field should be evaluated based on what makes a value trustworthy, not just which record it came from. In Dedupely, field-level merge rules let you set logic like recency, presence, or preferred source.

Here’s how to approach it:

• Use most recent for fields that change often, like email or phone number
• Use not blank when any value is better than none. For example, secondary contact fields
• Use source when you trust one integration or system more than others

This lets you apply logic that fits how your data flows, rather than relying on record priority or field order.

Build your merge rule strategy from three questions

Instead of applying the same rule to every field, use these questions to set logic that fits how each one functions in your CRM.

1. Does this field reflect identity, stage, or system input?

• Identity fields like email change often — use recency.
• Stage fields like lead status or lifecycle stage should follow a defined value order.
• Fields sourced from integrations may benefit from source priority.

1. What downstream processes rely on this field?

• If the value affects routing, scoring, or automation, use the most reliable source or latest update.
• If it’s used for personalization or reporting, presence may matter more than origin.

This gives you a clear decision path for each field, and a rule structure that scales across imports, deduplication jobs, and syncs.

Review fewer merges by setting the right rules

When each field follows clear logic, you don’t need to review every duplicate. You only check the records that don’t follow your merge rules, not every pair.

Here’s how the process changes:

• Before: Find duplicates → Compare records manually → Decide which values to keep
• After: Set merge rules → Merge → Review only flagged cases

Instead of reviewing everything by default, you're only reviewing what your rules can’t resolve.

Preview merges before they touch your CRM

Dedupely lets you test merge rules using CSV syncs or test segments from your CRM live data. You’ll see:

• What value is kept per field
• What rule made the decision
• What the final merged record would look like

This gives you a full view of how rules behave without modifying live data. You can adjust any logic before applying it to real records.

Consistency makes bulk merging practical

When every field follows a defined merge rule, bulk merging becomes predictable. You know how each value will be evaluated, and only records that don’t meet those rules require attention.

For example:

• A more recent email from a form submission replaces an older CRM value
• A preferred source value (such as the CRM or a trusted integration) is kept over a CSV import
• A not blank phone number fills a blank field on another record

Each merge follows the same logic, regardless of record order or volume. The result is consolidation that improves data usability, not just record count.

Use logs to refine merge logic over time

After each merge, Dedupely logs which values were kept. These logs show exactly how your merge rules perform across real records.

You might notice:

• Lifecycle stages aren’t progressing as expected — update the value priority list
• Certain imports consistently override CRM values — adjust source priority
• Some fields are still missing values after merges — use a not blank rule instead

Each log helps you improve the next round of merges. You don’t need to guess why a value was kept, you can see it, and adjust as needed.

FAQ

How do field-level rules work differently than record-based merges?

Field-level rules apply logic to each field individually. Record-based merges pick one record to keep as-is.

Can I apply different rules for different object types?

Yes. Dedupely supports separate rules for contacts, companies, leads, and other objects.

What happens when both field values are blank or the same?

Dedupely uses fallback logic based on your CRM or your configured record priority.

Is there a way to audit or adjust merges after they run?

Yes. Merge logs show what changed and why, giving you context for future rule adjustments.

Merge outcomes improve when rules define the logic

Deduplication is only effective when the right data survives. Field-level rules make that possible by giving you control over how each value is evaluated. Once this logic is set, merges become consistent, fast, and fully aligned with how your teams rely on CRM data, from routing and segmentation to reporting and sync.

If your merge results still depend on record order or guesswork, you should start here.