Introduction

Unanticipated shifts in macroeconomic variables can materially alter the financial performance benchmarks set by businesses. Tariffs, logistics cost fluctuations, or currency movements directly impact product landed costs and can shift consumer demand patterns.

Standard corporate processes for adjusting pricing strategies in response often involve analytical cycles that extend over weeks or months. These processes typically rely on periodic cost updates and static analytical models which may not fully capture complex market dynamics, such as cross-product demand elasticities or competitor pricing responses.

The lag time inherent in these traditional methods can result in periods where pricing is misaligned with current market conditions and cost structures, impacting profitability.

The Advancement

Operand's framework utilizes LLMs to create more dynamic pricing strategy:

1. Continuous Landed Cost Calculation: We configure LLMs to monitor and process data from multiple sources (e.g., tariff databases, freight indices, FX markets, logistics updates). This enables the systematic calculation of estimated landed costs per SKU on a frequent basis (e.g., daily), providing more current cost inputs for pricing decisions compared to standard quarterly or monthly updates.

2. Dynamic Elasticity Estimation and Segmentation: Our LLMs develop Bayesian statistical models to estimate price elasticities. These models integrate internal sales data with external data points. This approach allows for the estimation of own-price and cross-price elasticities even with potentially sparse data. The outputs facilitate SKU segmentation based on estimated price sensitivity and margin contribution, often visualized in an Elasticity–Margin Quadrant:

   
   

   Quadrant 1 (High Elasticity / Low Margin): Suggests high price sensitivity and limited cost buffer. Requires careful consideration, potentially involving sourcing reviews.

   Quadrant 2 (High Elasticity / High Margin): Suggests sensitivity but available margin. Potential for strategic pricing actions relative to competitors.

   Quadrant 3 (Low Elasticity / Low Margin): Suggests lower sensitivity but limited buffer. May tolerate cost pass-through.

   Quadrant 4 (Low Elasticity / High Margin): Suggests lower sensitivity and high buffer. Offers flexibility in absorbing costs or funding actions in other quadrants

   
   

3. Competitor Response Modeling: An auxiliary LLM function analyzes publicly available competitor information (e.g., press releases, financial disclosures, observed price changes) to develop probabilistic forecasts of potential competitor pricing actions. This output serves as an additional input into the pricing decision process, designed to anticipate market dynamics rather than solely reacting to observed changes.

4. Systematic Playbook Generation: Based on the cost inputs, elasticity segmentation, and competitor forecasts, the system generates formatted price recommendations suitable for integration with ERP or e-commerce systems. This facilitates faster implementation of approved pricing adjustments.

5. Expert Validation & Strategic Alignment: AI generates the quantitative recommendations and playbook elements; Operand experts provide the final validation. They ensure the proposed actions align with overarching brand strategy, inventory position, competitive realities, and channel considerations.

Illustrative Examples

The following hypothetical scenarios demonstrate potential applications of the framework:

• Scenario 1: Grocery Retail Response to Input Cost Inflation

  Situation: A grocery retailer faces simultaneous supplier cost increases on canned tomatoes (+12%) and gourmet sauces (+8%).

  Framework Application: Elasticity analysis indicates canned tomatoes are highly elastic (-4.8) with a 14% margin, while gourmet sauces are less elastic (-1.1) with a 36% margin. Competitors are projected to pass through most of the cost increases.

  Potential Action: The framework might suggest holding the price on tomatoes (absorbing the cost increase on a key value item) while increasing the price on gourmet sauces by +9% (passing through more than the cost increase on a less sensitive item). This aims to balance overall margin objectives with category-specific competitive positioning.

  
  

• Scenario 2: Footwear Brand Navigating New Tariffs

  Situation: A footwear brand faces a new 15% tariff impacting both high-volume sport sandals (est. elasticity -5.2, margin 28%) and lower-volume, high-margin collectible sneakers (est. elasticity -0.9, margin 55%).

  Framework Application: Competitor analysis suggests rivals will implement a full 15% price increase on comparable sandals.

  Potential Action: The system could recommend a +5% price increase on the core sport sandal (partially absorbing the tariff to potentially gain volume share, given the high elasticity) and an +18% increase on the collectible sneakers (leveraging low elasticity to potentially improve overall margin mix). This illustrates a potential asymmetric pricing response funded across SKUs with different strategic roles.

Conclusion

Incorporating real-time data processing and advanced modeling techniques enabled by LLMs enhances the responsiveness and precision of pricing strategies, particularly in volatile operating environments. This framework facilitates a shift from periodic, reactive price adjustments towards a more continuous, data-informed process considering costs, demand elasticity, and competitive context simultaneously.