At a Glance

A substantial portion of a retailer's catalog typically comprises long-tail SKUs with low sales frequency. One of our clients, a leading North American retailer, struggled to determine optimal promotional discount levels for its large product catalog, risking margin erosion or ineffective campaigns. Operand leveraged its LLM to provide precise discount recommendations that drive significant sales lift.

Introduction

The 'long tail' distribution, characterized by many unique items selling infrequently, poses a challenge in retail inventory management and pricing. Items selling less than one unit per day can constitute a majority of SKU counts yet remain difficult to analyze using standard econometric techniques requiring substantial historical data per item.

Consequently, decisions regarding pricing, promotion, and inventory for this segment often rely on heuristics or category-level assumptions. This can contribute to the accumulation of slow-moving stock, tying up capital, increasing holding costs, and often necessitating significant markdowns or write-offs.

Addressing the analytical limitations concerning low-volume SKUs is relevant for enhancing overall inventory productivity and financial performance. Attributing analysis failure simply to 'sparse data' overlooks potential methodological advancements.

The Advancement

To address the data sparsity challenge inherent in the long tail, Operand combines LLM functions with specific statistical techniques:

1. Semantic Similarity Clustering: LLMs process unstructured product data (titles, descriptions, attributes, reviews) to understand semantic characteristics. Clustering algorithms are then applied. This approach groups SKUs based on inferred similarities relevant to customer perception and demand, yielding clusters more informative for demand analysis than standard product hierarchies. This step identifies products likely exhibiting related demand patterns.

2. Group-Based Elasticity Modeling: Our LLM applies Bayesian hierarchical models within the semantically defined clusters. This technique allows for 'partial pooling' of information across clustered SKUs, leveraging data from higher-volume items to inform estimates for lower-volume ones within the same group. This process enables the generation of demand elasticity estimates even for SKUs with limited historical transaction data.

3. SKU-Level Decisions: A decision engine uses these elasticity estimates as inputs. This system generates recommendations for individual SKUs related to base pricing, promotional discounts, or markdown approaches (e.g., 'Maintain Price,' 'Suggested Promo Discount: 15%,' 'Consider Markdown Initiation'). Outputs include projected revenue lift and margin impacts.

4. Strategic Validation & Oversight: AI generates the quantitative recommendations; Operand experts provide final validation. They ensure recommendations align with broader inventory strategy, financial targets (e.g., margin thresholds), category lifecycle stage, and overall commercial objectives.

Case Study

An engagement with a North American retailer, provides an illustration of the methodology's application in a context involving sparse data.

• Application: Our LLMs performed semantic clustering on the relevant product set. They then built context-aware elasticity models for these clusters, generating specific discount recommendations optimized for lift and margin protection.

• Outcome: The promotion utilizing discount levels informed by this modeling achieved a +5 percentage point incremental sales lift (trend-adjusted), exceeding the client's target.

Conclusion

The difficulty traditional methods face in analyzing low-volume SKUs can lead to suboptimal management of a significant portion of retail inventory. The above engagement demonstrates that perceived limitations from sparse data can be addressed through analytical techniques leveraging LLMs. This enables improvements in gross margin contribution from previously under-analyzed SKUs, reducing the reliance on broad clearance actions and inventory write-offs.