TechVantage has deep expertise in applying Analytics, Machine learning and AI to the Sales data in the transportation and logistics industry. We strongly believe we can provide you with some data-driven state-of-the-art solutions to increase your revenue, optimize your costs in the Sales transactions. We helped the logistics companies in Sales Compensation Calculation module effectively.

Case studies

  • Predicting Quota
  • Rate Discovery Analysis for Supply Chain Shippers

The client is a leading transportation and logistics company with operations in North America


Problem Statement


The client paid commissions to its sales agents based on the amount of sales generated against the quota set for that quarter.


The business wanted to automatically set the quota amount for each sales agent against each customer for the next quarter based on their past performance.




Built a machine learning model to optimize quota setting. The predicted data was visualized using Tableau enabling the client to set different quotas for different budget requirements.



Enabled optimized quota setting process without human intervention and thus saved costs. Custom views for in-depth analysis of their data for easy tracking and arriving at better insights.


Problem Statement


Analyze the US freight market to benchmark freight rates so that Shippers and carriers can optimize their pricing using the benchmark




The obtained dataset was first cleansed and prepared to obtain a standardized format. After various validation checks were performed on this data to identify outliers, the outliers were removed from the model. The cleansed data was then moved to SPSS where the regression was performed at a 95% confidence interval


A linear regression model was used to estimate the freight rates that were then compared against the actual rates to give a benchmark (market study). A LP model was developed for lane matching which identified the dead miles during a transaction and realized potential where collaborating with non-competitive shippers could reduce it.




For the TL model it was identified that distance was the most influential factor driving the overall costs, followed by fuel and geography.


Lane by lane cost prediction was obtained which enabled the client to gauge the shipper’s performance for each transaction, and use it as a benchmark for further contract negotiation on that lane.


Lane matching helped client suggest shipper collaboration to reduce the dead miles and improve the overall cost savings.

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