{
"version": 1,
"slug": "bulk-merge-pipedrive-duplicates",
"title": "How to Bulk Merge Duplicates in Pipedrive (Step-by-Step Guide)",
"excerpt": "Tired of duplicate contacts in Pipedrive? Learn how to find and merge duplicates (People & Organizations) using Datablist. Fix your CRM, clean your data, and keep Pipedrive organized—fast and easy!",
"cover": {
"src": "/howto_images/pipedrive-dedupe/merge-duplicates-pipedrive-cover.png",
"optimized": "https://www.datablist.com/_next/image?url=%2Fhowto_images%2Fpipedrive-dedupe%2Fmerge-duplicates-pipedrive-cover.png&w=1200&q=75"
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"url": "https://www.datablist.com/how-to/bulk-merge-pipedrive-duplicates",
"contentMarkdown": "\n**Pipedrive** is great for managing leads and sales. However, like any CRM, duplicate contacts and organizations pile up over time.\n\nThe built-in Pipedrive deduplication tool has major **limitations** ([see all Pipedrive Merge Duplicate Limitations](#pipedrive-built-in-merge-duplicates-limitations)):\n\n- It only detects **exact matches** using the persons/organization names - No matching of company names with legal suffixes (Google LLC == Google), no fuzzy matching, or deduplication using the website, email address, etc.\n- It **doesn’t let you bulk merge** duplicates.\n\nIf you’re dealing with duplicate People or Organizations in Pipedrive, you need a **better solution**. \n\nThat’s where Datablist [Duplicates Finder](/features/duplicates-remover) comes in to dedupe your Pipedrive data. In this guide, I'll show you how:\n\n1. [Import your Pipedrive People and/or Organizations in Datablist](#step-1-import-your-pipedrive-contacts)\n2. [Find and merge duplicates using advanced algorithms](#step-2-find-duplicates-in-pipedrive-people-and-organizations)\n3. [Sync Merges in bulk back to Pipedrive](#step-3-sync-merges-in-bulk-back-to-pipedrive)\n4. [Pipedrive Built-in Merge Duplicates Limitations](#pipedrive-built-in-merge-duplicates-limitations)\n\n## Step 1: Import Your Pipedrive Contacts\n\nThe first step to cleaning up your Pipedrive duplicates is to **import your data into Datablist**. This allows you to use advanced deduplication tools that Pipedrive doesn’t offer. Follow these steps to import your contacts and organizations:\n\n**1. Create a New Datablist Collection**\n\nA collection in Datablist is like a spreadsheet where you store and clean your data. Each collection will hold your Pipedrive contacts (People) or companies (Organizations).\n\n- If you want to deduplicate People, create a collection for People.\n- If you want to deduplicate Organizations, create a collection for Organizations.\n- If you need to clean **both**, create **two separate collections: one for People and one for Organizations**.\n\n\n\n\n**2. Select “Source → Pipedrive”**\n\nDatablist connects directly to your Pipedrive account through the API.\n\nIn your Datablist collection, go to `Import` → Select `Source` → Choose `Pipedrive`. Or directly from the \"See all sources\" link in the collection start screen.\n\n\n\n**3. Find Your Pipedrive API Key**\n\nYou’ll be prompted to enter your Pipedrive API key.\n\n\n\nYour **Pipedrive API key is required** to link Datablist with your Pipedrive account. Go to [Pipedrive API Settings](https://app.pipedrive.com/settings/api) and copy your personal API key. Paste it into Datablist.\n\n**4. Choose People or Organizations**\n\nOnce connected, decide what you want to import:\n- If you’re cleaning duplicate contacts, select People.\n- If you’re cleaning duplicate companies, select Organizations.\n\n\n\n> Note: If you need both, **import them separately** into two different collections.
\n> **Why separate collections?** - Pipedrive stores People and Organizations differently. You can’t deduplicate a mix of both at the same time. Keeping them separate ensures accurate results. \n\n**5. Datablist Fetches All Pipedrive Fields**\n\nOnce you select People or Organizations, Datablist will pull in:\n- Standard Pipedrive fields (Name, Email, Phone, Website, etc.).\n- Custom fields you’ve created in Pipedrive.\n\n\n\n\n\nThis ensures that all relevant data is available for deduplication.\n\n**6. Run the Import**\n\nClick Import to start fetching your data. The time it takes depends on the number of records.\n\nDatablist processes your contacts and structures them for deduplication.\n\n\n\n**Change data types for URLs** \n\nDatablist [Duplicates Finder](/features/duplicates-remover) has special processor to deal with `Emails`, `URLs`, etc. If your data has websites, change their property type to `URL`.\n\n\n\n\n\n\n**Multiple Email Addresses & Phone Numbers**\n\nIf a contact has multiple emails or phone numbers in Pipedrive, they are concatenated into a single field, separated by a semicolon `;`.\n\nExample:\n- John Doe has two emails in Pipedrive: `john@company.com` and `j.doe@gmail.com`.\n- In Datablist, he appears as: Email: `john@company.com;j.doe@gmail.com`\n\nThis allows Datablist to handle multiple values correctly during deduplication.\n\nOnce the import is complete, your data is ready for deduplication. Next, we’ll find duplicates using advanced matching techniques.\n\n## Step 2: Find Duplicates in Pipedrive People and Organizations \n\nNow that your Pipedrive contacts or organizations are in Datablist, it’s time to **find duplicates**. Unlike Pipedrive’s built-in tool, Datablist uses **advanced matching algorithms** to detect similar records—even when names are slightly different or contact details are incomplete. \n\nHere’s how to do it: \n\n### 1. Open the Duplicates Finder and choose a Deduplication Property \n\nIn your Datablist collection, go to **Clean → Duplicates Finder**. \n\n\n\nA **deduplication property** is the field Datablist will use to find duplicates. The best one depends on the type of data: \n\n- **For People:** Use **Email** (most reliable) or **Name** (if emails are missing or inconsistent). \n- **For Organizations:** Use **Website** (best) or **Name** (if no website is available). \n\n💡 **Example:** \n- If \"Google\" has two records: one with `Google LLC` and another with `Google`, Datablist will detect them as duplicates because their names are similar. \n\n\n\n\n### 2. Run Deduplication\n\nFor **best results**, don’t merge everything at once. Run deduplication in **iterations**: \n\n1️⃣ **First pass:** Match on **Email** (for People) or **Website** (for Organizations). This finds **clear** duplicates. \n2️⃣ **Second pass:** Match on **Name** (for People or Organizations) to catch duplicates that may have different emails but refer to the same entity. \n\n🔹 **Why two steps?** \n- If you merge on **Email + Name together**, minor name variations (like \"John Doe\" vs. \"Johnathan Doe\") might prevent matches. \n- Doing an **email-first** pass ensures exact email matches are handled first, avoiding errors. \n\n**Special care for multiple Emails and Company Names**\n\nSome Pipedrive fields contain **multiple values**, like Emails and Phones. \n\nIf you are deduping on fields such as `Emails` or `Phones` that have multiple values (separated with `;`), enable **\"Multiple Values\" mode** in Datablist. This ensures that if a contact has `john@company.com; j.doe@gmail.com` on a record, and just `john@company.com` on another, it still gets correctly matched.\n\n\n\nIf you are deduping on **Company Names**, Datablist Duplicates Finder has a dedicated processor to deal with **legal suffixes** and clutter words.\n\n\n\n> If your are deduping on websites, use the `URL` processor.\n\n### 3. Review the Detected Duplicates and Conflicting values\n\nOnce the scan is complete, Datablist groups duplicates together. \n\n- You’ll see **clusters** of similar People or Organizations. We call them \"**Duplicate Groups**\".\n- Each group will show **how they match** (by email, name, or website) and if they have conflicting data.\n\n \n\n\n#### 3.a. Understand Duplicate Groups \n\nEach duplicate group contains multiple records that are considered the **same entity**. \n\n- Some groups can be **merged automatically** because they have identical or complementary data. \n- Others require **manual review** because they have **conflicting information**. \n\n💡 **Example:** \n- If two records for “John Doe” have the same email but different phone numbers, Datablist will detect them as duplicates but flag the phone number as a **conflicting field**. \n\n#### 3.b. Set Up Merging Rules for conflicting values\n\n**Understanding Conflicts**\n\nConflicts happen when duplicate contacts have different values for the same field. For example, two records might have different job titles or phone numbers. Datablist highlights these conflicts so you can decide which value to keep.\n\n**How to Deal with Conflicts**\n\nDatablist provides an intuitive interface to resolve conflicts efficiently, ensuring you maintain clean and accurate data.\n\n- **Combine Values**: If the values are complementary (e.g., multiple phone numbers, notes), combine them.\n- **Keep One Value and delete the others**: If one contact is clearly more complete, and you want to discard the other, select \"Drop conflicting values...\".\n\n> For the **Combine conflicting values** and **Drop conflicting values** settings, you have a shortcut link to automatically select all conflicting properties.\n\n\n\n**Select a master record**\n\nYou can also configure how Datablist selects the master record. When merging duplicate records, Datablist keeps one record, updates its fields, and deletes the other records to end up with only one record.\n\nYou can control how Datablist selects this Master Record by choosing from several rules:\n\n- **Most Complete**: Picks the record with the most populated fields.\n- **Last Updated**: Picks the most recently modified record.\n- **First Created**: Picks the oldest record based on the creation date.\n- **Highest Value**: Picks the record with the highest value for a selected property. If multiple records have the same value, it selects the most recent one.\n- **Lowest Value**: Picks the record with the lowest value for a selected property. If multiple records have the same value, it selects the most recent one.\n- **Matching Value**: Picks the record that contains a specific value in a selected property. If no record matches, they won’t be merged.\n\n\nDatablist shows a **preview listing** of the changes to be made. You see which records will be deleted, which values will be combined, etc.\n\nClick on the \"**Refresh**\" preview once you have finished defining the merge settings.\n\n \n\n \n\n\n🔹 **Example:** Merging Two Contacts\n\nThe following example shows the result of the merging rules:\n- Drop Conflicting values on the `Name` field.\n- Combine Conflicting values on the `Phone` field.\n\n\n
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\n \n \n | Field | \n Contact 1 | \n Contact 2 | \n Merged Result | \n
\n \n \n \n | Name | \n John Doe | \n Johnathan Doe | \n John Doe | \n
\n \n | Email | \n john@company.com | \n john@company.com | \n john@company.com | \n
\n \n | Phone | \n 555-1234 | \n 555-5678 | \n 555-1234; 555-5678 | \n
\n \n
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