From a Cost-Plus Pricing Strategy to AI-Powered Quotation

Today, Logistics Service Providers (LSPs) face increasing pressure to update their pricing strategies to stay ahead in the market and ensure long-term growth. For many, this means pivoting away from a cost-plus pricing strategy. Yet, many still rely on traditional cost-plus methods in the face of constantly evolving challenges.

Pricing strategy is crucial for the success of any business, particularly in competitive sectors like logistics.  Intense competition, seasonal demand fluctuation and changing customer expectations mean that LSPs must differentiate themselves through clear, flexible pricing and customised solutions. With slim profit margins and escalating expenses, optimising pricing becomes vital for profitability. 

Here, we’ll outline the pros and cons of cost-plus pricing in the logistics industry to better understand its suitability. From here, we propose alternatives to a purely cost-plus approach, powered by AI technology. 

Why does the cost-plus pricing strategy endure?

Cost-plus remains the prevailing strategy in logistics pricing, particularly in road transportation. This approach involves calculating the total cost of delivering a service and then adding a markup to establish the selling price. Essentially, the selling price is determined by incorporating a predetermined profit margin into the cost of service. 

It’s a straightforward method with its own set of advantages and disadvantages. One significant advantage of cost-plus pricing is its simplicity. It’s easy to understand and implement, requiring just two key metrics: the cost and the desired margin to calculate the price. This simplicity makes it accessible to businesses of various sizes and levels of expertise. 

Additionally, cost-plus pricing offers stability and predictability. By ensuring that overheads are covered and maintaining a positive profit margin, businesses can set their markup to achieve their required rate of return on each sale. Moreover, this approach helps mitigate risks in volatile markets.

However, despite its benefits, a cost-plus pricing strategy has notable limitations, particularly if it’s the sole pricing strategy employed. One key drawback is its narrow focus on costs and desired markup, overlooking other strategic factors. For instance, when entering a new market where competition is intense, relying solely on a cost-plus pricing strategy may hinder a company's ability to achieve its business objectives. This is especially the case in mature markets where strong brand recognition and customer loyalty may further compound the strategic limitations of a cost-plus pricing strategy.

This approach fails to consider important factors such as competitor pricing strategies, brand reputation, and customer perceptions, potentially resulting in missed opportunities or reduced market share. Therefore, while cost-plus pricing offers simplicity and stability, businesses need to complement it with additional pricing strategies to remain competitive and adaptable in dynamic market environments.

Overcoming cost-plus limitations with AI-based dynamic pricing

Pricing is a crucial tool for achieving strategic business objectives, whether it's maximising revenue by offering competitive freight rates or enhancing margins through value-based pricing. This emphasis on optimising pricing strategies places it at the forefront of discussions in LSP boardrooms. 

Leaders recognise the importance of three key elements in an effective pricing strategy: speed, scalability, and intelligence. A well-executed pricing strategy should:

1. Provide rapid or instant quotes. Customers expect swift responses, and an efficient pricing strategy should deliver quotes rapidly, if not instantly.
2. Scale without additional investment. The pricing process should be scalable, requiring no extra investment per quote, allowing for seamless expansion without proportional increases in costs.
3. Smart pricing. Pricing decisions should be informed by comprehensive data analysis to offer the best price for a specific client at a given time, ensuring competitiveness and maximising revenue potential.

In today's business landscape, traditional approaches like cost-plus pricing may fall short in effectively addressing these demands. The emergence of artificial intelligence (AI) presents a transformative solution, enabling LSPs to optimise operations and implement more agile pricing strategies.

The agility and precision afforded by AI enable LSPs to stay ahead of market trends, anticipate customer needs and proactively adjust pricing strategies accordingly. This approach not only enhances revenue generation but also fosters customer loyalty and retention. Let’s look closer at these advantages.

Benefits of AI-driven dynamic pricing

• Real-time price adjustments based on real market conditions. AI algorithms enable businesses to swiftly respond to temporary imbalances in supply and demand, allowing for more responsive and competitive pricing strategies, and leveraging seasonal demand forecasting. For instance, if there's a sudden surge in demand due to events such as a carrier strike, AI algorithms can automatically adjust prices to reflect the increased willingness of customers to pay, thereby maximising revenue potential.
• Differentiated pricing for different customer segments. By analysing various factors such as willingness to pay, price sensitivity and seasonal demand patterns, AI can categorise customers into distinct segments or cohorts. This segmentation enables LSPs to implement targeted pricing strategies, optimising margins and conversion rates for each customer group.
• One customer, one price. AI-powered dynamic pricing allows businesses to establish personalised pricing arrangements on a one-to-one basis. Through advanced algorithms, individualised pricing agreements can be tailored to enhance customer satisfaction and loyalty while maximising revenue potential.
• Boosted conversion rates with instant quotes. AI-powered instant quoting capabilities streamline the sales process, significantly improving conversion rates. By providing customers with instant quotes, businesses reduce the risk of losing potential leads to faster competitors. Additionally, real-time feedback on pricing inquiries enables continuous optimisation of pricing strategies, ensuring competitiveness in the market. This is a particularly useful tool in the spot quote workflow.
• Pricing expertise stored in the AI engine. By centralising pricing expertise within AI engines, businesses can mitigate risks associated with personnel turnover. Retaining valuable pricing knowledge within the organisation safeguards against disruptions caused by key personnel departures, such as sales or operations managers and maintains operational continuity, particularly during peak periods or holidays.

Reconfiguring the cost-plus pricing strategy for a new era

While information technology has played a significant role in streamlining operations over the past decades, the emergence of AI heralds a new era of computing capabilities, presenting further opportunities for LSPs to refine their pricing strategies.

However, transitioning from a traditional cost-plus pricing strategy to AI pricing optimisation requires a deliberate and phased approach. It entails organisational transformation and the gradual implementation of increasingly sophisticated technologies. Each company must chart its unique path, adapting processes, teams and tools accordingly.

At Ontruck AI Tech, we guide businesses through this transformative journey towards adopting advanced pricing systems focused on profit maximisation. Our expertise spans both the procurement and sales sides of the equation. We invite you to request a meeting with our experts to explore real-world use cases and understand our approach. Click here to organise your demo.