Extract Name From Email Address

[Chieko Boteler]

Iwant to create a formula that will create a name column that returns Nero Naidoo. I've tried some of the split-to-text formulas suggested and seem to only come up with nero.naidoo. I want to capitalize the "N" and replace the "." with space. any ideas?

thank you for your response. I have tried that. however, I'm looking for a formula that will capitalize the first letter of the name and last name and remove the "." So for the second example, it must return Nero Naidoo, not nero.naidoo

I have a workflow that will copy a row to another sheet when the status changes to Re-open. I've done this so that I can count the number of times a ticket gets re-opened after it has been completed (testing quality). This seems to work fine if the ticket has been re-opened once, but when it's re-opened the second time, it seems to create an additional row with the same ticket number instead of updating the status. In this instance, the ticket is re-opened by the requester where the email address is captured through the systems created by the function and does not have shared access to the sheet. they submit a request through a form and re-open through the open update form link.

I have a similar workflow for when the ticket is rejected by a reviewer and it seems to work fine - in this instance the reviewer's email address is entered with a drop-down selection and has shared access to the sheet.

Could you please share me as an admin on a copy of your sheet after removing any sensitive data and I will check it for you.please stay wake up for my conversation on the shared sheets if you share me.

Can you describe your process in more detail and maybe share the sheet(s)/copies of the sheet(s) or some screenshots? (Delete/replace any confidential/sensitive information before sharing) That would make it easier to help. (share too, and...@workbold.com)

Thank you. I have shared. It's all test data. I've shared the workspace so you can see where it's copying. the other thing I've noticed is that, every time the ticket is re-opened for the 2nd time, it disables the copy row workflow. Let me know if it will be better to collaborate over a call.

I'm trying include a date range with counting the number of applicants within various depts, in certain date ranges, but it's saying incorrect argument set. =COUNTIFS(DISTINCT([Name of Requestor]:[Name of Requestor], [Submission Date]:[Submission Date], AND(@cell > DATE (2023, 9, 30), @cell

When prompted to choose delimiters, check the box next to 'Other' and type in the '@' symbol. This action instructs Excel to use the '@' symbol as the point to split the email address into two parts. Click 'Next' to continue.

In the wizard's next step, specify where you want the split data to appear. It's recommended to select a cell in a new column next to your emails. This prevents overwriting the original data. Confirm by clicking 'Finish'.

We hope that you now have a better understanding of how to extract names from email address in Excel using formulas and the text to column feature. If you enjoyed this article, you might also like our article on how to extract domains from email addresses in Excel or our article on how to create a link to an Excel file in an email.

Notes: (1) TEXTBEFORE is a newer function in Excel. In older versions of Excel, you can use a formula based on the LEFT function as explained below. (2) All emails shown in the worksheet are fictional.

In this example, the goal is to extract the name from a list of email addresses. In the current version of Excel, the easiest way to do this is with the TEXTBEFORE function or the TEXTSPLIT function. In older versions of Excel, you can use a formula based on the LEFT and FIND functions. All three options are explained below.

Text is the text string to split, and delimiter is the location at which to split the string. Since all email addresses contain the "@" character separating the name from the domain, we can extract the name with a formula like this:

Another easy way to solve this problem is with the TEXTSPLIT function, which is designed to split a text string at a given delimiter and return all parts of the split string in a single step. To solve this problem with TEXTSPLIT, use a formula like this in cell D5:

At the core, this formula extracts characters from the left with the LEFT function, using FIND to figure out how many characters to extract. C5 contains the email "john....@abc.com", so FIND returns 12, since the "@" occurs as the 12th character. We then subtract 1 to prevent the formula from extracting the "@" along with the name:

The final result returned by LEFT is "john.doe123". As the formula is copied down the column, it performs the same operation on each email address. Although this formula is more complicated than the TEXTBEFORE or TEXTSPLIT options above, it achieves the same result.

The Excel TEXTBEFORE function returns the text that occurs before a given substring or delimiter. In cases where multiple delimiters appear in the text, TEXTBEFORE can return text before the nth occurrence of the delimiter.

Hi - I'm Dave Bruns, and I run Exceljet with my wife, Lisa. Our goal is to help you work faster in Excel. We create short videos, and clear examples of formulas, functions, pivot tables, conditional formatting, and charts.

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This will give you the usernames from the email ids. Note that if you do not specify a destination cell in Step 3, the original data is overwritten with the extracted data (excel does warn you before overwriting).

Say I have two email addresses and I would like to see if it is likely that they belong to the same person. For example, [email protected] and [email protected] is likely to be from the same person (it doesn't have to be certain, providing the likeliness would be sufficient).

I had two directions in mind to achieve this, one is a string comparison between the two email addresses and the other is to first extract the names from the email addresses then compare if they might be the same person. Like in the example above, the names extracted should be Cameron M Thompson and c thompson.

I am also wondering if given that one of the email addresses is guaranteed to contain the full name (usually company email addresses have the full name), would that help the extraction of name in the other email address (personal email addresses might not always contain the full name), or would that help on the comparison of the two email addresses.

I have had a hard time trying to figure out if any of the above two directions would be feasible. Especially when email addresses might not have separators and names can vary a lot that a listing might not be sufficient to find a match.

UPDATE:I have a dataset that has two columns, email address and name, and about 2k rows there. I believe this could be used for the second direction (name extraction). For the first direction (string comparison similarity), I am thinking of modifying the dataset to three columns (email address 1, email address 2, label of whether they are the same person), which should give about 1k rows of data.

Before talking about the solution, why don't you focus on the content instead? I think it would be more helpful to solve your problem, considering that most of the email addresses end with the sender's sign, Name Surname. Also, the probability of failing to obtain this information from an email address is much higher than the probability of failing to get it from the content. Especially, this is the case with company email addresses which might not contain the whole name in the email address (first letter of name and surname e.g. John Travolta - [email protected]), but it must contain the author's full name (at least the name) at the end. Furthermore, consider that plenty of email addresses will contain only name or surname or neither of them, but substitutive words like superboy122133@+++.com :D. But most of the email apps contain a default sign that includes name and surname. In addition, you can combine these two techniques. That is, combine the email address data with email content data so that, if it is infeasible or impracticable to obtain data from one of these, then you can use another one.

However, if let's say you have to do it with nothing but an email address I think using Machine Learning techniques would be overrating or overestimating the problem. Also, using non-machine learning techniques does not mean you are simplifying the solution, all these techniques give the best outcome when they are applied in the correct context. Let's imagine a simple situation: if you know or can easily infer that [tax] = 0.2 * [salary] + 20 $, why would you find (or fit) this equation using Machine Learning?

Unless you have data in the format of email address, fullname, you shouldn't start with using Machine Learning. (If you would have email address, fullname data, as an option, you would train a model to learn the general relationship between the email address and full name, thus you would identify similar email addresses).

One alternative approach would be having a hashed dictionary of all available names and surnames, then you can cut pieces(substrings) from the email address then hash them to find the names and surnames from the address (Of course, vice versa would be highly inefficient). The email addresses that have the most similar, properties would be matched.

Another solution would be, using the above-mentioned patterns, you can generate a bunch of artificial email addresses. Considering that it is highly probable that there is not a dataset that includes the name and surname of people and their one or more email addresses, data augmentation is the first order of business. (I am not sure whether the data augmentation term fits this situation. If it does not then let's say data generation). So your input would be Name Surname (you can include middle name, number, etc.), and output would be randomly generated email addresses based on the pre-defined patterns. The number of emails that are generated for a single input should be randomly selected also, but be careful about the generation of the same email address more than once. E.g. input -> John Travolta -> output -> j_travolta12@+++.com, john.t.99@+++.com, john.travolta@+++.com (Lets suppose for this example we randomly choose 3 emails to be generated).

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