RENXT JustGiving Integration Tips, Tricks And Secrets Blog Series: Part 2 - Constituent Matching Logic – Deep(er) Dive 7467

RENXT JustGiving Integration Tips, Tricks And Secrets Blog Series: Part 2 - Constituent Matching Logic – Deep(er) Dive

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After a donor makes a gift to your organization via the donation form on Blackbaud Peer to Peer Fundraising, powered by JustGiving, if you are integrated with RENXT, the constituent data then flows to RENXT. The database uses matching logic to establish rather this donor is an existing constituent already in your database or a new constituent. Lets talk more about this!

After a donor makes a gift to your organization via the donation form on Blackbaud Peer to Peer Fundraising, powered by JustGiving, if you are integrated with RENXT, the constituent data then flows to RENXT. The database uses matching logic to establish rather this donor is an existing constituent already in your database or a new constituent. This process is called the “RENXT Automatic Constituent Matching” process. The purpose of this post is to:
  1. Help you understand the matching logic by providing examples with real data.
  2. Suggest potential work processes to manage duplicates.
  3. Give you tips and tricks to reduce duplicates that can be created during the integration process.
How Does the RENXT Automatic Constituent Match Work?

At the most basic level, RENXT uses the donor’s First Name, Last Name and Email Address to identify potential matches to the existing constituents in RENXT. Raisers Edge NXT updates an existing record if it finds an exact match for all three fields (last name, first name, email address.) It creates a new constituent record when it finds multiple matches or potential matches. At the most basic level, this makes sense. However, people are complicated and so is their data. (Says the girl with two versions of her first name and two, non-hyphenated last names.) To make more sense of this, let’s break it down.

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*All active and inactive emails and phones are checked
*If email address is an exact match and last name is not an exact match, additional logic is introduced to account for typos and hyphenated last names.
*JustGiving donation form does not ask for the donor’s phone number
*If multiple matches are found, a second layer of matching analysis occurs to determine which is the more likely match using exact matches for phone and certain parts of addresses.

Lets take it one step further and look at a few scenarios. (The gmail is not my actual email address.)
Last Name First Name Email Match Logic
Existing RE Record Zimmerman Jacqueline jzimmerman@gmail.com N/A
Ideal Scenario: Donor supplied data is exact match to RE record Zimmerman
ü
Jacqueline
ü
jzimmerman@gmail.com
ü
YES All matching fields are the same
Scenario 1 Zimmerman
ü
Jackie
~
jzimmerman@gmail.com
ü
YES Last name and email match, fuzzy match on first name
Scenario 2 Armstrong Zimmerman
X
Jackie
~
jzimmerman@gmail.com
ü
NO Last name doesn’t match, fuzzy match on first name, email matches
Scenario 3 Zimmerman
ü
Jacqueline
ü
Jackie.zimmerman@blackbaud.com
X
NO Last name matches, first name matches, email does not match
Scenario 4 Zimmerman
ü
Jackie
~
Jackie.zimmerman@blackbaud.com
X
NO Last name matches, fuzzy match on first name, email does not match











































In the scenarios in the chart, based on donor supplied data, it is likely duplicates will be created. Keep in mind the integration is going to err on the side of capturing correct data in as opposed to overwriting your existing data. This is a good thing! Consider: spouses that share email addresses, legacy names where parent/child share the same name but different emails, donors whose legal name and social names are different (Jacqueline Armstrong Zimmerman vs. Jackie Zimmerman.) At the end of the day, it is human intervention and inference that can determine which records are truly duplicates and which are not.

In sum there are three scenarios for constituent processing while using the JustGiving Integration:
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Work Processes to Manage Duplicates

As always consult your policies and procedures when managing duplicates. What matters for one organization might not be important to the next when deciding which data to keep and which to deleted/merge. Here are two suggested work processes to manage duplicates.

Typical Work Process
  1. Approve gifts, commit the batch.
  2. Use the web view tool “possible duplicates” to identify the existing duplicates.
  3. Use the database view tool Duplicate Constituent Management and Merge Constituents tool. Follow your internal policies and procedures for the merge practices.
This work process is fairly standard and works within Blackbaud’s previously established best practices. These work steps are very linear and thorough. This work process, in my opinion, doesnt account for the fact that donors often supply inconsistent or incompleted data. To be clear, this work process will achieve the goal of removing duplicates.

A New Work Process
  1. Use the web view tool Possible Duplicates. Compare the possible duplicate records side by side and ensure all gifts, including the pending gift from the JustGiving batch, move to the Target record.
  2. Approve gifts via the web view tool, commit the batch in gift management.
  3. Use the database view tool Duplicate Constituent Management and Merge Constituent. Follow internal policies and procedures for the merge practices.
This work process ensures that once you approve the batch in webview, you are assigning the JustGiving gift to the intended constituent.

Please note: I suggest using the webview and database tools in both work flows because each of them contain slightly different criteria for identifying a match. By running both in tandem, starting in webview, you can identify more duplicate records.

A Smattering of Other Tips and Tricks to Regarding Duplicates
  1. As often as possible, try to create the most robust constituent record possible. In the first name field aim to put the full first name, not the nickname. (Jacqueline vs. Jackie) Make use of the nickname field, addressees/salutations to track the other ways constituents prefer to be addressed. More data will only improve match results when using the Duplicate Management tools.
  2. Where possible, try to have an email address associated with each constituent. In database view create a query to help you identify the records with no email associated. (Constituent Query, Criteria: Email Address is Blank)
  3. Each constituent record that contains an email address should have the primary checkbox associated with the primary email address. A quick query in database view can help you identify those without the primary checkbox associated.
  4. In database view, create a constituent query with this criteria: “Added By: NXT System User (Constituent Data Manager)” to spot check records created by the integration. Hint: Set up your output to view the required fields for your database. (Shout out to Luke at the Chicago Public Library for this one.)
  5. Ensure you have set the duplicate business rules in Configuration. This will ensure consistent use of the Duplicate Constituent Management Tool.
  6. Before launching any JustGiving campaign (or any mass marketing effort), complete your duplicate review process. This way when data begins to flow into your database from Blackbaud Peer to Peer Fundraising, powered by JustGiving you are starting with a clean slate.
  7. Set all Form Settings in Tools>Form settings. The email mapping and address mapping will impact your ability to manage address type, email type and primary checkboxes moving forward.
A Final Thought

If you will, allow me to wax and wane for a moment. When I first started in fundraising (when donating online was still new), people’s data was more “solid.” Physical addresses changed, phone numbers changed, but there were/are tools to help track and verify those changes. As the world becomes more and more digital, data is becoming more and more fluid. Most of your donors not only change physical addresses several times in their lives but have at least 3 email addresses and only a mobile phone. And even the owners of this data have a hard time tracking and managing their own information.

All this to say, I believe we are moving into a world where frequent duplicate management is essential to quality data management. If it is not already, duplicate management will become something that is completed multiple times a year, if not monthly or weekly. As donors are given more and more opportunities to interact with organizations online, there will be more and more opportunity to supply inconsistent data.

When I think about the world when I first started fundraising and digital was new, all we wanted was more donor data. Now that we have it, we must decide what to do with it and how to manage it.

As always, I want to hear from you. What work processes have you discovered, tips, tricks do you have up your sleeve. And, most importantly, tell me where you disagree and how you might change things around.


Resources:
Automatic Constituent Matching Process Documentation
Possible Duplicates Documentation

A large part of this blog post was informed by my colleagues working Raiser’s Edge daily. Special thanks to, Samantha McGuin, Principal Product Manager who helped craft many of these ideas, the images were created by Samantha and the chart of constituent matching examples first came from Samantha. If you’d like to watch these concepts in an on demand webinar, especially for the rules related to Constituent Matching, checkout: My Favorite Feature: Event Management in Blackbaud Raiser’s Edge NXT.
News Tips and Tricks 02/23/2021 7:00pm EST

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1 Comments
Great comment about the fluidity of data now - de-duping is ever more important, and ever more tricky!
 
Jonathan Angell‍ , thank you! I have a deep love of data-or rather the choices data can help us make. 

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