Prospect and Owner Matching Logic

As different types of data flow through Aimbase, the system always attempts to consolidate data pertaining to the same person. There are a couple of different examples of this. See examples below: 

1. Prospect to prospect – Aimbase looks at data gathered from new prospects and/or leads and tries to determine if the person the information is about already exists in the platform. If specific matching criteria are met, then Aimbase rolls the newly collected data into the existing prospect. The result of this data can be seen on the Prospect Timeline. 

2. Owner to owner – Aimbase looks at data gathered from new owners and/or registrations and tries to determine if the person the information is about already exists in the platform. If specific matching criteria are met, then Aimbase rolls the newly collected data into the existing owner. The result of this data can be seen on the Owner History page within the Owner Details. 

3. Owner to prospect – Aimbase looks at data gathered from new registrations and tries to determine of the person the registration is for already exists as a prospect. If specific matching criteria are met, then Aimbase notes that the registration matched to a prospect. When this happens, the record is counted as a “closed sale” which appears on the prospect’s timeline, closed sales reports, and the Closed Sales grid. 

About the Matching Logic

As Aimbase attempts to match incoming data to existing owners and prospects within the system it looks at several key fields that are typically collected regardless of the type of data being looked at.  

Those fields are: 

As each of the fields are processed, Aimbase looks to see if any of the values on the fields match exactly to values on existing owners and prospects. When an exact match is found, then the new record receives points for each field that qualifies. If a certain point threshold is met, then the new record is considered to be a match to whichever existing record it met the point threshold for. If a new record matches to more than one existing record, Aimbase will choose whichever existing record had the highest number of matching points to roll the new record into. When a match takes place, key fields on the existing record are updated with data collected on the newest record.  

Note, if a value is passed in the ProspectExternalId and the ProspectExternalId matching setting is enabled in Aimbase, the prospect external Id value is used to match to an existing prospect and the prospect details will be updated vs creating a new prospect record.

Example Field Matching Weights 

Typically, a score of 100 is required for two records to be considered ‘matched’; however, this could vary from Aimbase to Aimbase. The weights (which dictate how many points each field receives when a value matches) associated to each field can vary. Below is an example of a standard point configuration. 

Importance of ‘Clean’ Data

When any type of matching logic is utilized, it becomes increasingly important for the data sent to Aimbase to be as ‘clean’ as possible. What is ‘clean’ data? Clean data means the values you’re sending to Aimbase are free from dummy or placeholder information. Two of the most common occurrences of bad data coming into Aimbase on records are the phone number and email address for a registration.  

Often, the owner or a dealer (on behalf of the own) will provide 555-555-5555 for a phone number or noemail@email.com to avoid providing real personal data. Allowing data similar to this causes issues for matching logic. Aimbase has other functions built in that allow it suppress some of these bad values, but it is always a best practice to keep data as accurate and ‘clean’ as possible to avoid causing false positives.