Inbox categorization
Benefit from Reply’s AI-backed algorithm that automatically categorizes your replies.
Emails are sorted into six default categories: Interested, Not Interested, Not Now, Do Not Contact, Forwarded
Use the ‘interested’ category to focus on conversations with your hottest leads or build your custom workflow. Our AI-backed algorithm is proven to deliver +90% accuracy for correct categorization.
Example input json
{
"ReplyTextBody": "Hi Kathy\r\nYou can call me tomorrow Friday or over the weekend..\r\n\r\nKind regards,\r\nJulie\r\nJULIE ...",
"ReplySubject": "meeting request: julie + kathy",
"SentEmailBody": "Hi Julie,
Wanted to reach out to you as I know there is a lot of change happening in the market and specifically at Pacific Union.
I\u2019ve been through these transitions before and know they can lead to tremendous opportunities for agents affiliated with a stable, nationally recognized brand who has proven plans in place to grow their business.
We are currently looking to add a few select agents to our Los Angeles office and thought you may be a good fit.
Do you have time for a quick call later this week to discuss?",
"SentEmailsDate": "2018-08-12 11:29:33",
"ReplyDate": "2018-09-27 22:58:49"
}
Result
{
"InboxCategory": "interested"
}
Automatic opt-outs via text
This method allows to track your contact’s intent to stop further communication with you.
It can be used to detect opt-out from your emails or texts more precisely
even if you already have another opt-out mechanism.
Example input json
{
"ReplyTextBody": "Please stop bothering me.\r\n> On 28 Sep 2018, at 2:37 AM, Maria ...",
"ReplySubject": "geri, quick question"
}
Result
{
"IsOptOut": true
}
Call or meeting request
The method detects the intent to meet face to face with your prospects or book a call with them.
If the contact mentions a specific date/time, suggested times will be generated
for your contact. You can use this method to automate meeting bookings in your app.
Example input json
{
"ReplyTextBody": "Hi Kathy\r\nYou can call me tomorrow Friday or over the weekend..\r\n\r\nKind regards,\r\nJulie\r\nJULIE ...",
"ReplySubject": "meeting request: julie + kathy",
"ReplyDate": "2018-09-27 22:58:49"
}
Result
{
"IsCallmeeting": 1,
"CallMeetingDate": "2018-09-28 09:00:00"
}
Out of office detection
Use this endpoint to detect unavailability or out-of-office for your contacts and their date of return.
This can be used to automate communication workflow in order to email contacts once they return.
Example input json
{
"ReplyTextBody": "Dear sender,\r\n\r\nfrom September 28th till October 7th I will be out of office with no or very limited acces to my mail.. I will respond to your mail after my return on Monday October 8th.\r\n\r\nBest Regards\r\n\r\nJan He\u0159m\u00e1nek\r\n",
"ReplySubject": "jan, are you ok?",
"SendedEmailsDate": "2018-09-27 22:42:27",
"ReplyDate": "2018-09-27 22:42:32"
}
Result
{
"IsOutOfOffice": 1,
"OutOfOfficeDate": "2018-10-07 22:42:32"
}
Email text analysis
Maximize reply rates and make sure your email is actionable and perfect it before you send it.
The method checks email text in real time using five key parameters which will affect open and reply rates.
These parameters include: subject length, body word count, number of questions (aka call-to-action),
reading level, and positivity of the text.
Example input json
{
"ReplyTextBody": "Hi Jason, My name is thyagarajan and I am Business Developer Manager at Company Inc. I found your profile on LinkedIn, really liked what your company do and decided to reach out. Quick question: Are you currently looking for the new ways to grow your SaaS product sales? I might have something for you that potentially could become a new user aquisition channel for you, but that conversation can't begin until I listen to you first.",
"ReplySubject": "Intro"
}
Result
{
"Body length": {
"Coef": 0.48,
"EndOptimalCoef": 0.69,
"EndOptimalValue": 111,
"StartOptimalCoef": 0.37,
"StartOptimalValue": 59,
"Value": 76.0
},
"Positivity": {
"Coef": 0.08,
"EndOptimalCoef": 0.74,
"EndOptimalValue": 0.74,
"StartOptimalCoef": 0.46,
"StartOptimalValue": 0.46,
"Value": 0.08
},
"Question count": {
"Coef": 0.17,
"EndOptimalCoef": 0.33,
"EndOptimalValue": 2,
"StartOptimalCoef": 0.17,
"StartOptimalValue": 1,
"Value": 1.0
},
"Reading level": {
"Coef": 0.92,
"EndOptimalCoef": 0.75,
"EndOptimalValue": 0.75,
"StartOptimalCoef": 0.53,
"StartOptimalValue": 0.53,
"Value": 0.92
},
"Subject length": {
"Coef": 0.07,
"EndOptimalCoef": 0.43,
"EndOptimalValue": 6,
"StartOptimalCoef": 0.14,
"StartOptimalValue": 2,
"Value": 1.0
}
}
Anti-spam words detection
Increase your delivery rate and avoid emails falling into spam folders.
Benefit from this method by removing specific spam words before sending your email.
These are words that trigger most spam filters and should be avoided in your emails.
Example input json
{
"ReplyTextBody": "Hi Jason, My name is Thyagarajan and I am Business Developer Manager at OPENBRACE. I found your profile on LinkedIn, really liked what your company do and decided to reach out. I might have something for you that potentially could become a new user aquisition channel for you, but that conversation can't begin until I listen to you first. Quick question : Are you currently looking for the new ways to grow your SaaS product sales? Warm regards, Bala, For Open Brace | +92 54 4280 8675 Click here to book an 15-minute free consu",
"ReplySubject": "Intro"
}
Result
{
"SpamWords":
{
"Click here": 1,
"Open": 1
}
}
Sales intent detection
For leads in your sales pipeline, use this method to detect prospects’ exact requests.
This can be requests to get pricing or marketing materials, mention of a specific person
to speak to, location, or the usual ‘How are you?’ conversation starter.
Example input json
{
"ReplyTextBody": "Hi Kathy\r\nYou can call me tomorrow Friday or over the weekend..\r\n\r\nKind regards,\r\nJulie\r\nJULIE ...",
"ReplySubject": "meeting request: julie + kathy",
"SentEmailBody": "Hi Julie,
Wanted to reach out to you as I know there is a lot of change happening in the market and specifically at Pacific Union.
I\u2019ve been through these transitions before and know they can lead to tremendous opportunities for agents affiliated with a stable, nationally recognized brand who has proven plans in place to grow their business.
We are currently looking to add a few select agents to our Los Angeles office and thought you may be a good fit.
Do you have time for a quick call later this week to discuss?",
"SentEmailsDate": "2018-08-12 11:29:33.205631",
"ReplyDate": "2018-09-27 22:58:49"
}
Result
{
"Category":"'Interested'"
}
Intelligent Email Sorting
Email Quality Check
