Delivering Price Increases


A mid-tier Transport Service company with annual revenue of $200M and a 4% year-over-year growth rate was questioning their pricing structure. Its pricing was always based on distance travelled and weight regardless of material or market. Traditionally, when passing prices increases, the company relied on a “peanut butter” spread method. Each Courier Lane received the same percent price increase regardless of capacity utilization. Interestingly, Back Haul capacity utilization was running at half of Head Haul (Figure 1).

The RML Approach

Our approach focused on three main areas:

Build the Strategy

Where should we take the price increase? How much should it be?

Set List Price

How do we manage the effective price through List and Discounts?

Execute, Monitor, Report, Adjust

What training is needed to create a sustainable change?

The Challenge

Despite frequent regular price increases, the client had realized no change to net sales per pound (Figure 2). To address this, the client needed a demand sensitivity tool to understand which routes were the most price-sensitive as well as a model to evaluate customer willingness to pay.

1. Build the Strategy

The team approached the project on two fronts:

  1. A demand share tool was utilized to determine how each lane reacted to price increases (Figure 3). It discovered that each lane demand sensitivity varied greatly, thereby invalidating the “peanut butter” spread pricing method. With each lane modelled, we created a segmented lane pricing strategy.
Lane demand share
Figure 3. 1% increase in price would impact A556 the greatest
  1. The sensitivity analysis found that the probability of sale for Head Haul and Back Haul trips was highly dependant on price per pound (Figure 4). We sought to optimize Back Haul capacity by increasing competitiveness of rates in certain markets.
Direction demand sensitivity
Figure 4. Lane A143 has limited back haul potential after $1.25 per pound

2. Set List Price

To aid in setting the correct list price, a willingness to pay model was built to evaluate each individual lane. Figure 5 shows 18% of customers are willing to pay more than $2.00 per pound. The willingness to pay model was used to set each list price.

In conjunction with the new pricing strategy, the team developed new customer-relevant offerings. Figure 6 displays the new value-added offerings to clients on a for-charge basis.

value added offerings

3. Execute, Monitor, Report & Adjust

Revenue Management Labs went on to do training with the internal sales team on:

  • demand sensitivity tool
  • willingness to pay tool
  • demand share tool
  • effectively communicating list price changes and the value-added offerings

The client built these tools into their sales process to ensure compliance.

The Result

+4.7% Net Sales Per Pound

Following the project, the client experienced a 4.7% increase in net sales per pound (Figure 7).

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