Logistics pricing can be a crucial factor in the profitability of any supply chain. By reimagining the pricing cycle and taking five steps to reform the entire procedure, companies can maximize value and increase profits. In comparison to other sectors, pricing strategies in logistics have been comparatively underdeveloped – but that doesn’t have to be the case.
Reimagining the pricing cycle can be the most impactful lever for profitability in the logistics sector, and five steps to reform the entire pricing cycle can maximize the value.
Pricing strategies in logistics have been less mature than in other sectors. Fragmentation and a historically low degree of digitization have been challenges in developing more complex approaches. However, recent investments in data infrastructure have now opened a window for more comprehensive pricing transformation.
Logistics companies that transform their pricing could increase revenue by 2 to 4 percent, translating to as much as a 30 to 60 percent increase in operating profit. However, achieving this upside requires a strategy that tackles the entire pricing cycle. Here, we make the case for reforming pricing and outline a five-step process to achieve it.
Time to reap the full reward of data infrastructure investments
Digitization in logistics has historically been low. In addition, fragmentation in the sector, the complexity in different goods, and the absence of a strong global industry association to lead standardization and pricing transparency have led most companies to consider themselves price-takers. Companies have traditionally taken a “cost plus” approach to pricing.
However, logistics companies have recently made significant investments in technology and data infrastructure. We observe leaders upgrading legacy systems (for instance, to best-in-class integrated transportation management systems), streamlining digital architecture, removing data silos (for instance, among their transport management, inventory management, and enterprise resource planning systems), and adding new business insight capabilities on top of legacy systems.
Logistics companies now face a well-timed starting point for more sophisticated pricing strategies. Disruptors are also active in the market: logistics technology has attracted significant investor interest, and the sector continues to be strong, despite the pandemic. An example of an industry disruptor is Freights, whose application programming interfaces allow a variety of providers to share real-time pricing data, providing increased transparency to the market.
The upside from pricing strategy is significant and swift. Recent experience tells us that pricing has the highest and fastest impact on profitability of all available improvement levers. Logistics companies that transform their pricing strategy can typically expect a revenue boost of 2 to 4 percent—which translates to roughly a 30 to 60 percent EBIT2 margin improvement
For example, a leading global air cargo carrier underwent a comprehensive yield-management and pricing transformation. The carrier had experienced pressure on yields and margins, but pricing improvements are now expected to boost margin by two percentage points. Some initiatives—such as investing in digital solutions and changing company-wide mindsets—will take time to deliver, but the impact from pricing improvements is generally faster than from other levers due to several quick wins in pricing. In fact, results have already been rapid, and the carrier realized one percentage point of margin increase within the first year of the transformation program.
The COVID-19 crisis has intensified the urgency to improve pricing strategies and may have increased the value at stake. This is a challenging environment for the industry, and full recovery is likely to take approximately three to five years. A strategic and analytics-based pricing transformation can generate positive returns—though uncertainty remains around prices, volume forecasts, and premium services.
Ambiguity and price fluctuations have made dynamic pricing more valuable. For example, air freight prices from Asia to Europe doubled during the first months of the pandemic but then returned to precrisis levels.3 This kind of uncertainty will remain business as usual for some time to come. Therefore, mature companies with real-time pricing systems have a significant decision-making advantage.
Further volume surges, even if temporary, are possible at the early stages of recovery. At the start of the crisis, many procurement tenders and contract negotiations were delayed; these may eventually resurface. If they do, logistics companies with agile pricing operations will be better suited to ride this wave.
Increased premium-service demand during the pandemic indicates a higher willingness to pay. For example, a leading global container logistics company increased its share of total spot-cargo volumes from 24 percent in January to 53 percent in October 2020. We also see a willingness by major companies to pay for low-carbon transport services.
Pricing in logistics is not ‘one size fits all’
Long-term contracts and spot cargo require different pricing strategies. This means that logistics companies cannot apply a single approach to pricing their services. Most logistics companies have a mixture of different contract durations, and the ratio of each varies by type of company .This mix of long-term, medium-term, and spot contracts affects optimal pricing strategies. Additionally, each segment of logistics has its own specific challenges:
• Trucking companies have a substantial number of transactions and nodes in their networks. They operate hundreds or thousands of trucks, and less-than-truckload companies have to consolidate shipments, introducing another layer of complexity. In addition, all companies must also optimize backhaul utilization—that is, not driving back empty whenever possible—and on-time performance. To manage this granularity, trucking companies constantly adapt their networks and routes while maintaining hundreds of rate cards. Using powerful analytics for such calculations can not only offer more consistent and reliable results but also free up the sales team from manually managing pricing to focus on client management.
1. Optimize contract mix in the medium and long term
The first step for most companies is to optimize long-term baseload contract volumes and more volatile, potentially higher-margin spot volumes. Generating the optimal mix is a structured two-step process: first, creating a retrospective portfolio by combining past data on prices and contract structure with a benchmark market index and a measure of risk tolerance; second, calculating historical returns for contract and spot market shares using price-sensitivity models and plotting simulated margins against volume volatility. The combinations that exhibit the highest reward for a given level of risk represent the efficient frontier from which companies can select their optimal contract mix.
Since this model is based on historical data, companies could also develop a demand-forecasting model that builds in expectations on future price developments that allows, for example, choosing a portfolio from the efficient frontier with a higher spot-cargo exposure when their market expectations are bullish.
The impact of optimizing contract mix can be substantial. In recent months, an air cargo carrier aiming to optimize its utilization built a complex forecasting model to determine spot cargo demand. The model pulled together traditional internal and external datasets (such as market volumes by lane) as well as nontraditional data such as online searches and exchange rates. Surprisingly, online search volumes for consumer electronics or certain foreign-exchange rates improved the model’s ability to predict near-term air cargo demand. This new model improved the accuracy of demand forecasts, allowing the company to improve their capacity allocation between spot and contract markets. The impact from improved demand forecasting has led to a 3 percent improvement in the bottom line.
2. Set contract prices by reflecting value
Once the optimal contract mix has been identified, companies must develop value-based pricing guidance that reflects the value that their product or service is bringing to their customers, rather than their own costs and margin expectations. Capturing this value means charging more for superior performance and highly differentiated routes; adjusting prices for niche customers with specialized or high-value goods; and offering add-on services.
This value-based pricing strategy can have a considerable impact on margins. A leading global transporter implemented a number of value pricing techniques by using advanced algorithms to gradually adjusting margins across customer groupings such as trade lane, customer size, or the type of cargo being shipped. This, together with other measures like value pricing capability building in the sales team, improved their EBIT margin by more than two percentage points. Even small improvements on just one lever can sometimes have outsize effects; a large transport company is on track to achieve tens of millions in additional revenue just by ensuring that the high costs of serving small customers is fully reflected in their pricing.
The appropriate method of value-based pricing depends on the size of the contract. Setting up a deal factory may be the best approach for very large contracts but is too resource intensive for smaller accounts. Dynamic deal scoring—a more data-driven, tool-based approach to grouping customers may be more appropriate for smaller contracts.
Supporting the sales force on large negotiation through deal factories. For contracts that make up a large share of a logistics company’s revenue, key decision makers can assemble a “war room”—in which they have access to extensive data on factors such as customer history, price sensitivity, and the strategic importance of the contract—and develop a detailed understanding of competitor offerings, red lines, and potential concessions. By the end of the process, the negotiating team should have a clear idea of both the target price and the negotiation strategy. A deal factory of several such war rooms could provide support to sales teams on the most critical contracts.
Improving consistency on smaller contracts through dynamic deal scoring. Dynamic deal scoring is an automated, data-driven tool that supports sales representatives in pricing and discount decisions for smaller contracts. Machine learning can match each new deal with a comparable segment of historical deals and provide value-based rationale on appropriate pricing. For example, if customers shipping pharmaceuticals on a particular trade lane have historically accepted only comparatively low discounts, a logistics company should enter negotiations with a relatively high price tag. This process ensures consistent pricing for similar customers while putting the final decision in the hands of the sales or pricing team.
A large transport company in the Americas recently underwent a rapid pricing diagnostic exercise. A dynamic deal-scoring model enabled leadership to identify numerous historical deals that could have achieved higher margins. The company immediately piloted new pricing guidelines and revamped its sales messaging, incentive structure, and training in value selling. Fully implemented, the new pricing logic and capabilities will drive a revenue expansion of around 2 percent that will directly convert to bottom line impact.
3. Improve spot pricing through decision support systems
To capture the considerable upside potential of pricing fluctuations, companies should continuously optimize spot prices based on market and product and the likelihood to fill the capacity. Moreover, more digitally enabled pricing also reduces costs and thus potentially enables companies to also serve very small contracts, such as single shipments on a particular lane.
For smaller or irregular accounts, a dynamic price-management engine can help optimize prices while reducing sales costs. Advanced analytics engines can adjust quotes by combining real-time internal and external data (such as inventory, time of booking, weather, and market rates) with strategic parameters (such as the comparative importance of maximizing utilization and preserving price) and basic information about the customer and request. In online channel environments, experimental A/B testing allows to test prices with different parameters, e.g., validities, surcharges and therefore capture direct market feedback from customers. These processes reduce management and sales costs, eliminate more-subjective considerations, and improve client experience.
A European postal operator implemented new end-to-end parcel pricing, applying value-based pricing and implementing dynamic deal scoring techniques for large and medium-size accounts. For smaller accounts, it optimized product pricing based on customer grouping (including by industry) and a granular understanding of small-account service costs. This differentiated pricing allowed the company to capture a 10 percent EBIT margin improvement.
4. Reduce margin leakage
Once companies have the right mix of contracts, at the right prices, improving implementation and reducing margin leakage hold considerable upside potential. In our experience, this final step can yield up to one-third of the total impact of a pricing transformation. As such, reducing margin leakage typically shows results most rapidly, since it generally does not require significant new capabilities or new negotiations. Instead, it centers on enforcing actions indirectly related to price: ensuring appropriate penalties are charged and paid, executing service-level agreements, and recouping rebates when volume minimums are not met, among other activities.
Reducing margin leakage can yield up to one-third of the total impact of a pricing transformation.
Companies should audit their contracts, item by item, and ensure that all contractual charges and fees are invoiced. The careful maintenance of cost catalogs is the basis of transparency over cost and margins, but this process is time-consuming and repetitive. Therefore, logistics companies could implement robotic process automation, which can also further reduce errors in the charging or waiving of surcharges.
A top ten container shipping line used a range of these measures to improve its collection of contractual charges. In addition, monthly performance calls to sales staff and account managers reiterated the importance of executing contracts exactly as written. These efforts started to show results within weeks and eventually delivered tens of millions of dollars in bottom-line impact.
5. Get the organization in shape to get the price right
In our experience, focusing solely on pricing analytics—the “logic” of pricing—will not be enough to capture the full value at stake. Logistics companies may need to support and operationalize pricing and sales processes on the ground by developing more comprehensive programs around the following:
• Significant mindset changes may be necessary to gain buy-in. Sales teams may hesitate to remake pricing programs, so a targeted communications campaign can enhance understanding that even complex pricing transformations often require much less effort than winning an equivalent amount of additional volume.
• Incentives for sales teams influence their behavior more than simple pricing guidance. If incentives are not aligned to margins, then sales teams may not take the risks required to steer pricing increases. Prioritizing incentives for revenue over incremental price in commission structures is one example of misalignment.
• Critical value-selling capabilities are usually underdeveloped in logistics. Organizations could help their sales teams to understand and quantify the factors that contribute to a customer’s willingness to pay—and to be able to articulate them against the next best alternative.
• Processes, organizational structure, and performance management must reflect new analytics-driven, value-focused pricing approaches. In addition, stakeholders such as a head of pricing analytics should be involved in commercial approval processes—or should be appointed if they do not yet have a place in the organization.
• Finally, the right tools, systems, and infrastructure will best support a pricing transformation. The analytical approach needs to be tailored and connected to legacy systems and, most importantly, the information needs to be easily accessible to the front line. Lastly, tools, systems and infrastructure need to be augmented by a mindset shift towards an analytics-driven organization.