At Bit Datamining (BDM), we are striving to make most of our customers data by coming up with automated data driven solutions for increased efficiencies. In the realm of business-to-business (B2B) decision making, the rise of AI has brought about a major shift in the way decisions are made and implemented. AI algorithms are capable of analyzing massive amounts of data in real-time, providing valuable insights that would otherwise be impossible for a human to detect. Here are success stories originating in integrating BDM state of the art ML software solutions:
Machine learning (ML) driven automated extraction of semi-structured data from emails
By using ML algorithms to analyze large volumes of email data, organizations can quickly extract relevant information and turn it into actionable insights.
For example, ML algorithms can be trained to identify and extract specific data elements, such as customer names, addresses, and order numbers, from incoming emails. This information can then be used to automatically populate customer databases, streamline order processing, and improve customer experience.
However, there are also some challenges associated with ML-driven automated extraction of semi-structured data from emails. One of the biggest challenges is ensuring the accuracy and reliability of the extracted data. ML algorithms are only as good as the data they are trained on, and any errors or inaccuracies in the training data can negatively impact the performance of the algorithms.
BDM was selected by MBS Source, a comprehensive marketplace for securitized products, to address this challenge is the particular case of emails containing semi-structured intraday trading information. While the existing tools already provided automation aid for data entry, BDM identified, prototyped, and implemented a solution based on machine learning models to automatically collect key data points from emails. As a result, time consuming activities where automated leading to more robust results, reduced processing time (from 8h/day to minutes), and more accurate hence valuable key information being extracted (99% accuracy on sensitive data features).
Automated Supply Chain Management
One of the most successful use cases of AI-facilitated decision making in B2B contexts is in the area of supply chain management. Companies like Walmart have leveraged AI algorithms to optimize their supply chain operations, reducing waste and increasing efficiency.
By analyzing data on inventory levels, demand patterns, and shipping times, AI algorithms can make real-time recommendations to suppliers and logistics teams, helping to ensure that products are delivered on time and at the right pace, ensuring efficient exploitation of warehouses.
One of the first projects at BDM was tackling the safety stock forecast for a major auto-parts distributor in Romania, with a central warehouse and 70+ other warehouse distributed in the country and in the neighboring countries as well. We delivered a state of the art ML pipeline for demand forecasting that is currently exploited successfully by our partner.
Customer Segmentation and Personalization
AI algorithms can also be used to help organizations make informed decisions about customer engagement and marketing strategies. By analyzing data on customer behavior, preferences, and buying patterns, organizations can segment their customers and develop personalized marketing strategies that are tailored to the specific needs of each segment. This can lead to increased customer engagement, improved customer loyalty, and higher sales.
As an example, BDM was selected by a major auto-parts distributor in Romania to provide a solution for extending the offering of their network of warehouses. Basically, warehouse sales managers were complaining about not stocking a sufficiently diverse set of parts in sufficient volumes. "If we would have it, it would sell" was their main argument. As the industry lives on a very thin margin of operation, deciding what to ship, where to ship, and in what quantities is a sensitive topic at the core of their business. We built a dedicated ML pipeline for a customer segmentation and opportunity detection engine exploiting it, leading to identification of scored potential opportunities that is currently in use. On top of that, an automated optimal stock mix was built to insure efficient use of the ware houses based on local demand.