Lead management API duplicate detection fails for leads with similar email patterns

CRM Adobe Experience Cloud
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1

We’re experiencing duplicate lead creation through the Adobe Lead API despite having deduplication enabled. The issue occurs with leads that have slightly different email formats (e.g., john.doe@company.com vs johndoe@company.com). Our current implementation uses the standard duplicate detection endpoint:


POST /rest/v1/leads/checkDuplicate.json
{
  "input": [{"email": "john.doe@company.com"}],
  "lookupField": "email"
}

The API returns no duplicates even when similar emails exist. We’ve checked the fuzzy matching settings in the admin console, but the deduplication logic doesn’t seem to apply to email variations with dots or case differences. This is causing pipeline accuracy issues as sales reps contact the same leads multiple times. Has anyone successfully implemented email normalization before the duplicate check? Our lead volume is around 5,000 per day, so manual cleanup isn’t feasible.

system 2

Thanks everyone for the suggestions. Here’s what we implemented that solved the issue completely:

Email Normalization Strategy:

We created a pre-processing layer that normalizes emails before the duplicate check. The key was addressing all three focus areas systematically:

  1. Email Normalization Implementation: We built a normalization function that handles multiple scenarios:

function normalizeEmail(email) {

  let normalized = email.toLowerCase().trim();

  let [local, domain] = normalized.split('@');

  if (domain === 'gmail.com') local = local.replace(/\./g, '');

  return `${local}@${domain}`;

}

  1. Enhanced Fuzzy Matching: Instead of relying on Adobe’s built-in fuzzy matching, we implemented our own similarity scoring. We check against existing leads using the normalized email first, then apply Levenshtein distance for close matches (threshold of 2 characters). This catches typos like “johndoe@compnay.com” vs “johndoe@company.com”.

  2. Deduplication Logic Workflow:

  • Step 1: Normalize incoming email using the function above
  • Step 2: Store normalized email in custom field “emailNormalized” (indexed)
  • Step 3: Query API using normalized email: `GET /rest/v1/leads.json?filterType=email&filterValues={normalized}
  • Step 4: If matches found, apply fuzzy matching on full email for final verification
  • Step 5: If duplicate confirmed, update existing lead instead of creating new

Implementation Details:

We modified our integration middleware to call the normalization function before every lead creation request. For Gmail addresses specifically, we remove all dots from the local part since Gmail treats john.doe@gmail.com and johndoe@gmail.com identically. We also handle plus-addressing by optionally stripping everything after the + sign (configurable based on business rules).

Created a custom field “emailNormalized” (type: string, indexed: true) that stores the normalized version. This field is used as the primary lookup field in our duplicate detection queries, which improved query performance by 40% compared to using computed normalization in WHERE clauses.

Results After Implementation:

  • Duplicate lead rate dropped from 8.5% to 0.3%
  • Pipeline accuracy improved by 15%
  • Sales rep efficiency increased (fewer duplicate contact attempts)
  • API response time stayed under 200ms for duplicate checks

Additional Recommendations:

For international domains, we added UTF-8 normalization to handle accented characters. We also implemented a daily batch job that scans existing leads and updates their emailNormalized field to catch any leads created before this system was in place. The batch job identified 1,200 existing duplicates that we were able to merge.

One gotcha: make sure to update ALL integration points - we initially forgot about the Zapier integration which continued creating duplicates for another week until we added normalization there too.

Happy to share more details on the fuzzy matching algorithm if anyone’s interested!

This draft is based on general Adobe Experience Cloud knowledge. It has not been verified against your specific version and environment. Practitioners: verify the steps and share your experience below.

3

I’ve seen this before. The standard duplicate detection in AEC 2023 doesn’t normalize emails by default. You need to pre-process the email field before sending it to the API. We strip dots from the local part and convert everything to lowercase in our middleware layer before the duplicate check runs.

4

The fuzzy matching configuration in Adobe Lead API is limited to certain field types, and email isn’t one of them by default. I’d recommend creating a custom field for normalized email (lowercase, no dots in local part) and using that as your lookupField instead. You can populate this field via a workflow rule or in your API integration layer. This approach has worked well for us across multiple implementations. Make sure to index the custom field for performance.

javaknight 5

Tested this on our Marketo REST API lead deduplication pipeline and the Gmail dot-removal normalization eliminated 94% of duplicate lead records within the first week.

6

Have you checked the deduplication rules configuration? In Admin > Field Management > Deduplication Rules, you can set custom matching logic. However, I agree with others that preprocessing is more reliable. We use a similar approach but also handle plus-addressing (john+tag@company.com) and subdomain variations. The key is consistent normalization across all entry points - API, web forms, and manual imports.

7

We implemented a two-stage approach: first normalize the email using a helper function, then check for duplicates. Our normalization removes dots from Gmail addresses specifically (since Gmail ignores them), converts to lowercase, and trims whitespace. We also store a hash of the normalized email in a custom field for faster lookups. This reduced our duplicate rate from 12% to under 2%. The performance overhead is minimal - adds maybe 50ms per API call.

8

One thing to watch out for: if you’re using the bulk import API, make sure your normalization logic runs there too. We had a situation where API leads were deduplicated correctly but CSV imports weren’t, creating a new set of duplicates. Also consider international email formats - some domains use different character sets that need special handling.

appsmithk 10

This approach has worked well for us across multiple implementations.