AI Logistic Optimizing Model Development
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The development of an AI model for a logistics company specializing in transfers between European countries.
Our Client
Our client operates a logistics company specializing in facilitating relocations between European countries. As their lead inflow became increasingly overwhelming, manual processing became unmanageable, resulting in missed high-quality lead opportunities.
To address this challenge, the client opted for AI model development to optimize logistics operations in their Customer Relationship Management (CRM) system. This implementation of AI in the logistics business's primary objective was to provide actionable business intelligence by processing and categorizing leads based on various criteria and predicting conversion likelihood.
- Web App
- Admin Interface
- Backend Development
- Frontend Development
- Product Design
- Project Management
- Business Analytics
- QA
Logistics
6-8 months
Team Composition
Challenge
Initially, our client possessed a customer database along with financial data related to revenue and expenses. Our task was to train a model to uncover correlations within this data and categorize incoming inquiries based on these correlations.
The primary hurdle of the AI model development for categorizing leads was the need for a substantial volume of historical data to train the AI model effectively. Despite collecting data over several years of business operation, it remained insufficient for robust AI model development for the logistics company. However, our commitment to serving our clients drove us to seek a solution. We arrived at a conclusion to create a data synthesizer.
Solution
We conducted a comprehensive solution analysis and recognized that the raw data alone would not suffice for AI model development to optimize logistic leads. Consequently, we decided to synthesize data and leverage our prior experience in this domain.
The approach in logistics software development services yielded favorable outcomes. Thanks to this newly generated dataset, we developed an AI model that categorizes customer leads based on distinct patterns. Notably, these patterns were not of human origin but rather stemmed from the AI, which possessed a multitude of patterns. Categorizing leads according to AI-driven regularities significantly enhanced prediction accuracy, as demonstrated in our case study.
Technology Stack Used:
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The Algorithm’s Operation Can Be Summarized as Follows:
- The customer leavesa request on the website
- The request is processed by the AI on the server
- AI matches the request with historical data
- The query is categorized based on the results of the analysis
- The classified lead is sent to CRM for evaluation by administrators
The AI model development for the logistics business and the refined classification process resulted in qualitative improvements:
- The client effectively resolved the issue of managing an overwhelming lead volume.
- High-quality leads with a high probability of conversion were no longer overlooked
- The client's customer base expanded, leading to increased profitability.
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