Part One – Let’s talk about duplicates, The silent infiltrators of your Salesforce CRM.

You’re trying to run a clean donor list for a tax appeal campaign and suddenly “Betty Smith” shows up three times: once with an email, once with a phone number and another record with nothing but a mailing address from 2017.
Sound familiar?

Maybe contacts were manually entered from a spreadsheet, or your website form integrated new contacts without checking if they already existed. Maybe someone imported a third-party donor list and didn’t realise they were adding to the mess.
For NonProfit organisations, duplicates don’t just waste time – they hurt relationships. Suddenly you’re emailing the same donor twice, missing a major gift opportunity, or worst of all, sending a welcome message to a 20-year donor like it’s their first time interacting with you!

We’ve all been there and thankfully, most donors are understanding. They know it’s an honest mistake. But the time spent untangling the mess? Chasing down the right info, merging records, fixing reports? That’s the part you can avoid with a little upfront care.

I will walk you through some reasons why duplicates occur, give you strategies to prevent them from happening in the first place, best practices for identifying and merging them if they do sneak in and leave you with a few tips and tricks you can take away to keep your data dupe free.
 
How Do Duplicates Happen in the First Place?
Before you can solve a problem, you must understand it. Duplicate records are one of the most common and frustrating data issues faced by organisations. They clog up CRM’s, confuse teams and skew reporting. But how do they end up in your database in the first place?

Here are a few real-world reasons duplicates appear and why it’s not always as simple as someone entering the same thing twice.

1. Multiple Entry Points
Whether it’s a bulk data import, an integration with the CRM, or manual entry by different team members, every access point introduces a new opportunity for duplication. Without centralised standards or checks, it’s easy for the same record to sneak in through different channels.
It’s important to document the entry points, field mappings and data import method for each integration and import. If you’re managing multiple integrations, consider setting up a middleware layer to apply a consistent set of deduplication rules before any data enters your CRM.

2. Inconsistent Data Formatting
Inconsistencies like “John Smith” vs “Smith, John” or “(04) 23 456 789” vs “+61423456789” can prevent automated systems from catching duplicates. Even though the data is referring to the same person, the variation in format can mask that fact.
Creating standard formatting rules for all the contact details fields is crucial for identifying duplicates.

3. Incorrect Field Mapping
When data is imported into the wrong fields, say a phone number ends up in an address field, it can confuse your system’s ability to match records correctly. This misalignment makes it difficult to identify and merge duplicates.
Regular data audits and data cleanse will identify the incorrectly imported data and assist in keeping all your data in a better state. This also reinforces the need for field mappings documentation for all data entry points.

4. Disparate Deduplication Methods
Not all systems and integrations treat duplicates the same way. If your CRM uses one set of matchings method and your marketing automation tool another, they won’t catch the same issues. Without consist matching rules across platforms, duplicates are almost guaranteed.
It’s a good idea to create a baseline of duplicate methods that can be applied to all systems. Design your methods independently from your system. A feasible approach here is to design the methods based on the Salesforce fields and then run the deduplication process after the data has been inserted to Salesforce.

5. Restricted Picklists and Mandatory Fields
When users are forced to select from limited options or fill in mandatory fields they don’t have information for, they often make something up just to enter a contact. This workaround behaviour can create misleading or duplicate entries. Biggest advice here keep web forms simple and mandatory fields to a minimum.

6. Time-Poor Employees
Speed is the enemy of accuracy. When staff are under pressure to get data in fast, they often skip validation checks or don’t bother searching for existing records. It’s often not carelessness; it’s the reality of time constraints and NFP staff wearing many hats with limited resources. If you apply an automated matching process to run at regular intervals you can pick up the missed duplicates.

7. Typos and Misspellings
We’re all human and humans make mistakes. A simple misspelling, like “Jon Smith” instead of “John Smith” can be enough to trick your system into treating it as a new entry. Consider utilising matching techniques that use nickname matching all common first name spelling mistakes lists. It is important to always include personal details such as Date of birth, mobile number or email address to validate the duplicate is actually the same person.

8. Fear of Missing Out
This is one of the most common duplicate causes I see! Not-for-profits worry about missing a new contact or donation, fearing that their CRM might reject a record from an integration or web form. To avoid this risk, they often allow every record to be created without checking for duplicates “just in case.”
This well-meaning intent can quickly lead to cluttered, duplicate filled CRMs. With a few simple dedupe rules and configurations, this is one of the easiest quick wins.

Final Thoughts!
Duplicate records aren’t just annoying; they can cost you time, money and trust in your data.
The first step to cleaner data is awareness: knowing where the cracks are that let duplicates slip through.

Every NFP should take the time to review their deduplication management strategy. If you don’t have one, now’s the perfect time to create one! And if you’re not sure where to start, I’d be happy to help – just reach out!

In my next post, we’ll dive into a few common methods for identifying and eliminating those duplicates efficiently.