Install the solution, find duplicate sets, validate them, and merge them safely.
Connect your CRM, define the matching, ranking, and precision rules, and submit the requests that drive everything.
Using AutoMerge
How to find, validate, and merge duplicate data inside your Microsoft Dynamics 365 CRM.
This guide covers the AutoMerge experience inside your CRM: the duplicate views, the ribbon buttons, and what happens to your data when a set is merged. To configure the matching, ranking, and precision rules that drive the analysis, see Administering AutoMerge.
AutoMerge works on Leads, Accounts, and Contacts. Examples throughout use Contacts, but the views, buttons, and behavior are the same for all three.
Where to start
| You are | Start here |
|---|---|
| Setting up AutoMerge for the first time | Install the Solution |
| An end user cleaning up your own records | Potential Duplicates subgrid |
| An analyst merging duplicates in bulk | Dupes list views |
| Troubleshooting a failed merge | Common errors |
| Just evaluating AutoMerge | FAQs or a free Data Quality Report |
How AutoMerge works, in short
- The service analyzes your CRM using your matching rules and tags records that belong to a duplicate set.
- Each set's members are sorted by your ranking rules. The record ranked #1 is the winner-designate.
- Each set is scored 0–100 by your precision rules, from loosely matched to closely matched.
- You review the sets and AutoMerge them. Winners stay active; losers are deactivated, and their field values are preserved onto the winner.
Nothing is merged until you say so.
Three ways a merge gets triggered
| Who | Where | How |
|---|---|---|
| Power user | Your CRM, dupes list views | Select records from one or more validated sets, click AutoMerge |
| End user | Your CRM, Potential Duplicates on the form | Compare fields side by side, then merge that one set |
| Administrator | Management App | Submit an AutoMerge request across all tagged sets above a precision threshold |
All three behave identically underneath: records ranked 2 and higher merge into the Rank 1 record, in ascending rank order.
Precision scores let you merge confidently. Start with the high-precision sets, which need the least validation, working downward to the lower-precision sets need more human validation. Over time you will naturally zero in on a precision-threshold for your particular dataset that separates the high from the low.