We source our data by visiting (crawling) a business’ website, and use machine learning and algorithms to extract relevant data.
For example:
Email Addresses & Phone Numbers
We find them on the company’s website. They’re publicly available, making it easy to answer the question “how did you find my email”, if a prospect asks you that.
We don’t guess or try email patterns, which is the main reason why our users see lower bounce rates compared to other tools.
Company Name, Employee Names and Job Titles
We trained Machine Learning (ML) models to extract this data. After extracting the raw data, we process it and use multiple data points to clean and and score each data point. We then keep and include in Soleadify only the data we’re confident in.
Company Locations
We trained machine learning (ML) models to extract all the addresses found on a company’s website.
There usually are a lot of addresses on a company’s website, so right after extracting them we assign a score based on multiple factors. That’s how we also identify the “main location” of a company.
Business Category
We make an analysis of the text we find on the website, which is then processed by a ML model which knows to identify over 600 business categories.
This has the advantage that we can always expand how granular our categorisation is. So if you need us to add another category, please let us know 😁
In order to find these, we look at different URLs, network requests, and cookies, and we have, for example, a database of technologies we can detect.
