From Anchored to Agile: How Dynamic AI Pricing Keeps You Flexible During Disruption

Imagine you've spent hours planning a complex shipping route, carefully factoring in your latest port congestion data, fuel costs based on the day’s market rate data, and calculated insurance premiums for the voyage duration. The next day as you sit down at your desk, wham! The a major shipping lane has closed due to a new geopolitical crisis. Your carefully calculated budget is way off and your static pricing model is struggling to keep up.

In shipping, climate change and geopolitical turmoil create a constantly shifting pricing environment. Yet, static AI pricing models rely on historical data,  limiting their ability to adapt to real-time market dynamics and respond effectively to sudden changes in demand, supply, or competitor pricing strategies.  However, static AI pricing models, anchored to historical data, expose freight, logistics, and marine insurance enterprises to significant risk by the core of their business: pricing. 

The encouraging news is that solutions are at hand. But, let's examine the underlying cause of this rigidity first.

What are AI static pricing models? 

AI static pricing models, as the name suggests, rely on fixed parameters, historical data, and predefined rules to determine prices. These models use machine learning (ML) algorithms to analyze historical data, identify patterns, and create pricing rules based on cost, demand, competition, and seasonality.

They have four things in common:

1. Fixed parameters: The models use a set of predefined variables and weights to calculate prices, which remain constant until manually updated.
2. Historical data: Pricing decisions are based on analysis of historical sales data, market trends, and customer behavior.
3. Periodic updates: They need constant updating to incorporate new data and adjust prices accordingly.
4. Rule-based: They often use a series of if-then rules to determine prices based on specific conditions or thresholds.

But what’s the problem? Well, AI static pricing models can't offer the real-time insights needed in today's volatile market. It’s kind of like the earliesrt ChatGPT. Its understanding of pricing and predictive capabilities is always limited to the information from its last update.

That’s not good when a curveball gets thrown at the global supply chain. 

The Red Sea crisis: A perfect storm for static pricing

The following was the general reality for many shippers worldwide anchored to a static AI pricing model, due to longer trade routes caused by the Red Sea Crisis.

• Inaccurate Pricing: Still using outdated data, the static AI model underestimates fuel consumption, emission rates, operational expenses, and overall voyage costs, leading to underpricing and eroding profit margins.
• Missed Opportunities: The crisis causes a surge in demand for alternative routes, but AI models can’t take advantage of the window to adjust prices, costing the shipper potential revenue.
• Heightened Risk: The altered route brings new variables like unpredictable weather, factors static models can't incorporate into pricing fast enough, increasing exposure to financial losses.

What are AI dynamic pricing models?

A shipper with an AI dynamic pricing model, would responsd to the Red Sea Crisis differently since the dynamic model continuously ingests real-time data on fuel prices, port congestion, weather conditions, and geopolitical events.

Let's follow the journey of Shipper B, a company using an AI dynamic pricing model, as they navigate the challenges posed by the Red Sea Crisis.

1. Immediate adjustment to pricing: Shipper B's dynamic pricing model immediately recalculates prices as soon as the crisis hits, factoring in real-time fuel costs, route lengths, and piracy risks. As a result, their pricing remains profitable.
2. Rapid risk assessment: By analyzing the new route's historical and real-time data on piracy, weather patterns, and other risk factors, the dynamic model quickly incorporates these risks into its pricing algorithm so Shipper B can price its services accurately.
3. Proactive risk management: Shipper B takes control of the situation by analyzing real-time data and predicting potential delays and hazards.
4. Optimized operations: Leveraging advanced algorithms, the dynamic pricing model continuously analyzes multiple cost factors and operational constraints in real-time, enabling Shipper B to know which contracts to accept, how to manage logistics, and when to allocate resources.
5. Dynamic price optimization: The AI dynamic pricing model capitalizes on increased demand by adjusting prices in real-time while ensuring market competitiveness.
6. Scenario planning and risk mitigation: The model simulates different scenarios based on real-time data, enabling Shipper B to plan for potential disruptions and develop contingency plans. 
7. Continuous learning and adaptation: Throughout the crisis, the dynamic model continues to learn from new data and refine its pricing strategies. This allows Shipper B to stay agile and adapt as the situation unfolds while, minimizing financial losses and maintaining customer satisfaction.

Stargo’s AI dynamic pricing model

We understand that adopting AI technology may seem like a big step. But the benefits are transformative. Throughout the Red Sea Crisis, Stargo's GenAI demonstrated its value by boosting price request from 30% to near 100% accuracy, while dramatically reducing manual processing time from 60 hours to 3.6 seconds. Book a personalized demo to experience AI powered dynamic pricing firsthand. We'd love to meet you.