Optimizing Shipping Prices

The company was in the logistics and transportation industry, with a sizeable B2B business servicing multiple industries. The company had clear policies in place but was struggling with compliance.

Industries: Logistics, B2B

Services: Pricing Dispersion, Price Elasticity Modeling, Price Optimization

Data: Size: 5-10 GB, 20 Tables; Cleanliness: 7 out of 10

Business Challenge

The company was in a very challenging business environment. The logistics market is highly competitive and relatively low-margin. Besides the handful of large, global players, who benefit from economies of scale, there are thousands of small, nimble and local companies.

In addition, B2B logistics is typically contract-driven, whereby customers try to lock in a specific price for at least 12 months. To make matters worse, prices are typically based on expected usage.

Lastly, the sales team had significant power over price negotiations, with little oversight from the rest of the business.

What We Did

Solution Development

The company’s full rates were determined by its operational constraints so the project focused on optimizing the discount rates given to each customer. To do, we built a pricing model that would predict a customer’s discount rate based on its intrinsic characteristics (e.g. region, industry, size). Due to the unique properties of discount rates (bound between 0 and 1), we built a model using a logistic regression.

Solution Implementation

We followed the typical approach to develop a regression model: pre-analysis of the main drivers to be included, model implementation, sensitivity testing by running the model on various subsets of the data. This approach highlighted significant pricing issues with a specific segment of customers.

Results

The model was, first, thoroughly tested by the business on a handful of small but critical use cases. This “live” test was instrumental in fine-tuning the model before its full-scale implementation. The model uncovered significant opportunities with existing customers. It was also implemented to price new customers from then on.

Real Results. Proven Success.

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