Just the Headlines

Short on time? Here are the key facts.

  • A Chicago based 3PL wanted a way to quickly analyze why quotes were won or lost.
  • The Aptitive data science team built a machine learning solution to determine which factors most impacted a quotes success and to easily analyze new quotes based on these factor.
  • The client now can adjust their quotes based on these factors in order to increase their win rate. Based on their current conditions, even a slight increase can result in millions in revenue.

Overview

The math is simple, the more quotes a 3PL wins, the more money they make. The ability to win those quotes relies on a number of factors beyond just the quoted cost. In order for a quote to win, factors like these need to be taken into account.

  • Time between when a quote is created to when it expires (business hours)
  • Quoted Cost
  • Total Quotes for Customer since 2016
  • Total Load Miles for Customer since 2016
  • Total Number of Loads for Customer since 2016
  • Total Load Weight for Customer since 2016
  • Avg Market Line Haul Rate
  • Avg Market Price Aggressiveness
  • Avg Market Number of Reports
  • Avg Market Number of Companies
  • Month
  • DnB Annual Sales Categories
  • Day of the week

What if you could use data to determine which of these factors impacted the win rate the most or which have little to no effect on the win rate? By understanding these factors, a 3PL could make more informed decisions on quote creation leading them to increase their win rate.

When a Chicago based 3PL came to us with this challenge, Aptitive brought in our data science team to explore how to use data science to increase 3PL quote win rate (and profits).

Industry

Transportation and Logistics

Featured Technologies

R
Snowflake

The Goal

The goal of this project was to analyze our 3PL client’s internal and external data to understand which factors had the greatest impact on the quote being won or lost. We also wanted to help our client leverage this information moving forward by predicting the win rate of current quotes. Ultimately the client wanted a solution to optimize quotes, increase win rate and profits.

The Solution

The complexity and amount of data that needed to be analyzed required a solution beyond standard analytical capabilities. The Aptitive data science team built a high performing and computationally inexpensive Random Forest model to enable prediction analytics capabilities. This 3PL data science model was built with data from an existing internal Snowflake data warehouse as well as external industry data for our clients’ customers.

“Improving the win rate relies on a 3PL’s ability to identify and analyze the top internal and external factors that influence which quotes are won and which are lost. Aptitive built a machine learning model to enable our client to quickly combine and analyze these factors to measure their influence on win rate.”

The Outcome

This solution identified 6 significant factors that greatly impact the likelihood that a quote would be won. Armed with this, their team can focus on those 6 factors when creating quotes to increase the likelihood of a win. Factors that had a moderate or slight impact were also identified to give the team a more thorough understanding of the impact.

In addition enabling the team to optimize quotes with this information, the 3PL predictive analytics solution can also be used moving forward to predict the likelihood that a quote will be won. By having this information, our client can adjust their quotes in order to increase their win rate, where even a slight increase can turn around a massive return on investment.

The Bottom Line

In the initial stages of this project, Aptitive’s analysis found that even a conservative 1% increase in win rate had the potential to increase profits by 9 million USD for our client.

Redwood

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