First, Prophet uses AI to extract the pain points addressed in your blog article
These pain points are assigned to specific paragraphs
Prophet collects the email of your reader and starts tracking
From their scroll and click behaviour, Prophet assigns pain points in a paragraph to the reader to build a profile
Using this profile, Prophet uses AI to send hyper-personalized emails
Try it for yourself*
*This demo illustrates the usefulness of data that can be derived from a single article. Prophet also weights the identified pain points for each reader based off their behaviour and blends this with their entire reading history. This is not illustrated in this demo.
Edit me
Now let's try writing an email from this.
Prophet can attain response rates as high as 30%. The industry average is ~1%.
How is this possible?
Data quality > quantity
Relevant
＼(＾O＾)／
Real-time analysis of your blog traffic provides intent data just seconds old.
(╯°□°)╯︵ ┻━┻
Intent signals scraped from public profiles are 6+ months stale. When was the last time you updated your LinkedIn profile?
Specific
＼(＾O＾)／
Prophet's pain point based intent data is hyper-specific to your business's unique value proposition(s).
(╯°□°)╯︵ ┻━┻
Limited to ~2,500 intent topics that try to fit the millions of pain points all businesses solve in the world.
Warm
＼(＾O＾)／
Warmish — recipients of Prophet's emails are already familiar with your brand from your blog. By delivering valuable content, you've already built a positive relationship with the lead prior to the first email.
(╯°□°)╯︵ ┻━┻
Your cold email is the first time they hear of you.
Private
＼(＾O＾)／
Recipients actively opt-in to Prophet's emails through the content gate.
(╯°□°)╯︵ ┻━┻
Scrapes the internet, which is privacy invasive and highly annoying
Accurate
＼(＾O＾)／
We guarantee our signals are accurate. As they are built off your own site traffic and content, you can audit the accuracy yourself.
(╯°□°)╯︵ ┻━┻
Who knows how they got their data and how accurate it is?
