While name matching may seem straightforward, it is one of the more complex tasks we do at Trestle. Imagine for a moment the different ways that individuals express their names when filling out a business form compared to a more promotional one, or perhaps the difference between a legal and preferred name.
As an identity data company, name matching is one of our core competencies. We use it not only in Real Contact API but also internally to match names when we receive data from multiple providers and have to merge records.
Name matching plays a direct role in helping determine a lead’s phone contact grade. Grade A contacts, for example, require a high-confidence name match to a valid mobile phone with high activity signals. Grade B may reflect a valid number with a lower-confidence name match. The strength of the name match helps distinguish between grades and ultimately affects contactability and prioritization.
Typically, in name matching, the following steps are involved:
- Name Normalization: This includes capital casing, hyphens, Irish apostrophes (e.g., Michelle O’Flanagan), prefixes, and suffixes. We normalize names to ensure we have the right ones for matching.
- Name Parsing: A single name can be both a first or last name (e.g., Brady), and a single letter can be used for a middle initial or a last name initial. This gets even trickier when the names are longer (e.g., Maria de los Angeles Martinez or Erich van der Stroheim). So, accurately identifying the first, middle, and last names is critical for any further name matching and avoiding false positives.
- Nicknames and Phonetic Names: In many cases, names like Bill and William are the same person; it’s just that Bill is a nickname for their full name, William. Related to that is phonetic name matching, like Emily and Emile. This scenario might be about fat-fingering or name pronunciation not being captured correctly.
- Name Matching: Is John Smith the same as Smith John? How about John S and John Smith? They might or might not be the same person. To provide more context, we have 13 different types of matches to decide whether the two names match.
- Name Match Ranking: We have the best match if multiple names are associated with a phone or address.
Below, you will find different scenarios to provide further color to this concept:
- Jamie K Lannister and Jamie Lannister
- This would return a match because the first and last names match.
- Jon Snow and Jon S
- This would return a match because the first name and last initial match.
- Jamie Lannister and James Lannister
- This would return a match because the first name is a recognized nickname, and the last names match.
- Jon Snow and Jon Stark
- This would return a match because the first names are the same. While we would not necessarily merge these names in our database, Real Contact API would recognize the first name match as valid.
- Jon and Jon Stark
- This does not match. When only a single name is provided as input, Trestle assumes that the single name is the last name.
- Jon Snow and Snow Jon
- This would return a match because we view names that are swapped as matches for the Real Contact API name-matching feature. We would not merge these names in our database because we have a higher degree of confidence in the first name and the last name.
- S. Jon and Jon Snow
- This would return a match. This is because we recognize the names are swapped, and the initial provides enough context to see the initial and fully input name as two separate parts of a name (i.e., first and last).
- Jon Stark and Eddard Stark
- This would return a match. This is because the last names are the same. While we would not merge these names in our database necessarily, the Real Contact API would recognize the first name match as valid.
- John Stark and Jon Stark
- This would return a match. This is because the first names are a phonetic match, and the last names are a match.
The above examples give a sense of all the nuance involved in ensuring we match the right names and avoid false positives.
Because name matching directly influences contact grading, the accuracy of the process has a measurable impact on contact and quote rates. A stronger name match contributes to a higher grade, which helps teams prioritize the leads most likely to result in a successful connection.
Finally, we work to improve this every day. It is at the core of what we do – from data merging and ingestion to name matching in our various products. If you have any questions, feel free to reach out to us at support@trestleiq.com.