Enterprise
Automated Email Sorting
We helped a German debt collection company sort and prioritize important emails in their overcrowded inbox using Make and OCR technologies, with a process that is extensible through AI.
We helped a German debt collection company sort and prioritize important emails in their overcrowded inbox using Make and OCR technologies, with a process that is extensible through AI.
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A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
A German debt collection company was facing a major challenge: the daily influx of several emails meant that over 4,000 unread messages were piling up in their inbox. As debt collection cases can involve not only debtors themselves, but also lawyers, counselors and the police, many of these emails contained critical information and deadlines. However, due to the volume of incoming messages, very important emails were sometimes lost - a significant problem for the timely processing of cases.
The company already had a structured folder system in Microsoft Outlook to sort emails according to priorities and responsibilities. However, the challenge was to automate this process in order to work faster and more efficiently.
To solve this problem, we developed a customized automated workflow with Make. We combined various methods for classifying and categorizing emails. Our aim was to filter out the most important messages at an early stage and assign them the highest priority.
First, all incoming emails and the entire email thread were analyzed in order to better understand the context. To do this, we defined conditions and criteria that enable automated sorting. This included searching for specific keywords such as “police”, which were used as indicators for particularly important emails. By setting individualized keywords, the process was able to recognize specific email addresses or law firms in order to sort these messages into the appropriate folders. Emails from debt advisors, for example, could be assigned directly to a folder with other lawyer emails, even if the word “lawyer” itself did not appear in the email.
Using the hierarchical workflow logic, emails from the most important senders, such as the police, were also immediately identified and sorted into a folder first. This structure enabled our customer to immediately recognize which cases required the most attention so that no important deadlines were missed.
Another problem was that a lot of important information was not contained in the email text itself, but in PDF attachments. To solve this, we used OCR (Optical Character Recognition) technology from natif.ai, which was able to read and analyze text from attachments. This meant that relevant information could be captured directly and the emails correctly assigned.
In order to ensure a clear assignment, the emails and their attachments were also searched for reference numbers or file numbers. If an email did not contain a reference number, a follow-up email was automatically sent within the workflow in which the sender was asked to provide a reference number.
However, to avoid an accumulation of automated messages, this follow-up email was only sent once. If there was no response with the required information, the email was still moved to the appropriate folder.
Thanks to our customized automation solution and the combination of several technologies such as OCR, rule-based classification and intelligent follow-ups, the debt collection company was able to clean up its inbox and significantly optimize its email sorting.
This solution was also designed to be easily extended with artificial intelligence. An AI, for example in the form of a 0CodeKit endpoint, could analyze additional cases and better understand particularly difficult contexts.
While our Make workflow is already based on keywords, senders and attachments, an AI could fully analyze and classify the content of the emails - even if common keywords are missing. This combination of automation and AI would allow companies to work even more efficiently in the future and concentrate fully on the really important cases.
