1. Why is price optimization critical for parcel networks? 

Pricing is the moment of truth when companies can capture the real value of their products and services. It is always challenging because with a lower price you leave money on the table but with a higher price you increase the risk of losing the business. 

In the specific case of parcel networks, pricing is particularly complex because the right price depends on multiple factors that vary from transaction to transaction: 

  • Cost is variable by pickup and delivery address, density, weight, service levels
  • Customer’s willingness to pay (WTP) vary according to revenue tier, industry, value of goods, local market context, etc

Moreover, parcel networks must find the right prices for millions of transactions/ contracts every year. And they do so with limited pricing staff and with basic tools.    
 


2. How can operators improve their pricing processes?  

 


Many operators still use basic pricing models and spreadsheets to manage very complex pricing decisions. A typical model consists in a standard tariff by weight band and destination zone and volume-based discounts. This approach does not take into account the shipment profile of the deal and other customer characteristics that influence costs and WTP. These “averaged” approaches are sub-optimal. And given the high number of decisions, pricing teams can only review a few in detail. This results in substantial profit leakage. 

Price optimisation software automatically calculate a target price for each deal based on its unique profile and help manage exception requests through a validation workflow providing relevant decision support insights to pricing analysts. The impact is 2 to 4 points of additional EBIT. 
 


3. What data are required for price optimization?

A price optimization system ideally gathers the following data:

  • Historical billed shipments
  • Fees, surcharges and rebates
  • Customer segmentation attributes 
  • Costs
  • Capacity utilisation
  • Historical quotes
  • Competitors’ prices 

This data is used to generate optimal pricing policies and quotes, as well as to monitor performance overtime. 
 


4. Can you provide some success stories of price optimization technology in parcel networks?  



Data Science has provided many insights that translated into quick wins for our customers, for example:

  • Win/loss analysis by weight band revealed that, for some products and destinations, the price should be increased for small packages and reduced for heavier packages. This immediately translated into an increase in margin and conversion
  • Customer price diagnostic using a regression-based fair price model enabled to differentiate price increases by customer. The outcome was a +1 % incremental net margin compared to previous price increase campaign.

Another example is the implementation of a quotation system that evaluates the profitability impact of a price vs volume trade-off. This enabled our customers to negotiate win/win contract extensions: shippers get a better price and operator increases its margin.   


5. What are the specific pricing challenges of the logistics industry in Asia?

E-commerce is growing at a very fast pace in Asia and digitalization is transforming business quicker than in other markets. This means that pricing will be even more critical to sustain profitable growth. A learning from other industries is that players investing in price optimisation technology on-board more profitable customers and outperform their peers that cannot select the right customers.

With upcoming digitalization, customers will request instant quotes and operators must be ready to automate their pricing process to be able to provide them in real-time. 

Another challenge is related to the rapid urbanisation in Asia. Transport accounts for more than 20% of air pollution in large cities. Harmful gas and particle emissions are in rapid growth due to the continuous increase in urban population that will represent 75% of world population in 2050. The economic and social costs are huge in terms of poor efficiency (long and inconvenient transports at peak hours due to congestion) and health problems (pollution is already responsible for 1 death out of 9).

The development of e-commerce is putting on the road more commercial vehicles for the delivery of parcels. Fixed prices are the predominant model for urban delivery and one of the causes for peaks and troughs in commercial vehicle utilisation. On-demand delivery models powered by IT platforms embarking dynamic pricing (enabling delivery price variation by season, day of week and time band) can play an important role for optimising vehicle capacity utilisation. The impact: less vehicles on the road and less pollution and congestion for the same level of activity.

These challenges are not specific to Asia but they will be more critical here due to the faster pace of growth and transformation.