Studying Product Competition Using Representation Learning (2005.10402v1)

Abstract: Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product-level competition for the millions of products available on e-commerce platforms. We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product-level competition, when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. In order for the product embeddings to be useful for firm strategic decision making, we leverage economic theories and causal inference to propose two modifications to Word2Vec. First of all, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Second, we combine these vectors with random utility-based choice models to forecast demand. To accurately estimate price elasticities, i.e., how demand responds to changes in price, we modify Word2Vec by removing the influence of price from the product vectors. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.

• The paper introduces Product2Vec to embed products from basket data for scalable competition analysis.
• It defines economic measures, complementarity and exchangeability, to distinguish product complements from substitutes.
• Empirical tests show a 94% reduction in computation time while enhancing demand forecasting accuracy.

An In-Depth Analysis of "Studying Product Competition Using Representation Learning"

The paper "Studying Product Competition Using Representation Learning" by Fanglin Chen et al. presents an innovative approach to analyzing product-level competition using a method coined as Product2Vec. The complexity of analyzing large-scale e-commerce markets, where products can easily count into millions, necessitates computational efficiency and accuracy. This paper proposes leveraging the representation learning framework, particularly the principles underlying Word2Vec, to develop embeddings that encapsulate product information efficiently.

Model Design and Methodology

Product2Vec Implementation:

Product2Vec adapts the model of Word2Vec, utilizing basket data as input to generate low-dimensional product embeddings. This transformation captures semantic relationships among products akin to how Word2Vec captures contextual relationships among words. By conceptualizing shopping baskets as sentences and products as words, Product2Vec maps products into a vector space to quantify similarities and relationships.

Economic Measures:

Two influential economic measures are introduced: complementarity and exchangeability. Complementarity captures the interaction between latent dimensions of products that are likely bought together, while exchangeability measures the tendency of products being substitutes by considering them within similar purchase contexts. This approach provides a mechanism to discern whether products act as complements or substitutes.

Competitive Analysis and Model Validation

The utility of Product2Vec is tested through its integration into choice models, aiming at precise estimation of price elasticities and demand forecasting. In handling large product catalogs, Product2Vec promises both scalability and speed by operating with reduced dimensionality compared to traditional approaches reliant on product fixed effects or observable attributes.

Theoretical consistency and predictive efficacy are demonstrated through empirical evaluations employing IRI’s scanner panel data. The paper reports significant reductions in computational time (94% faster) compared to traditional methods, while achieving increased out-of-sample hit rates. Notably, this approach retains accuracy without resorting to extensive manual annotations of product features, which are often infeasible and subjective.

Addressing Methodological Challenges

One methodological advancement presented in this paper is the treatment of potential price endogeneity. The authors employ an instrumental variable approach to account for unobserved demand shocks that may simultaneously influence prices and consumer choices. Furthermore, by modifying Product2Vec to incorporate price as an explicit vector dimension, the paper demonstrates a viable strategy to decouple product co-occurrence patterns driven by similar pricing, thus enabling more robust price elasticity estimation.

Comparison with Prevailing Models

Comparison with contemporary models such as SHOPPER highlights Product2Vec’s strengths in computational efficiency and predictive performance with fewer requisite parameters. The comparison underlines the model’s adeptness at maintaining accuracy while reducing computational resource demands, notably contrasting with SHOPPER’s extensive resource requirements.

Implications and Future Directions

The competitively advantageous features of Product2Vec, such as automated large-scale product analysis and latent attribute identification, bear significant implications for strategic marketing decisions. Enhanced demand forecasting and more precise consumer choice modeling offer measurable benefits in product line optimization, pricing strategy, and market segmentation.

The paper opens avenues for exploring deeper integration of unsupervised learning methods into traditional economic modeling of market structures. Future research pathways could involve further refinement of the model to address dynamic market environments and incorporate more complex consumer behavior patterns, potentially enriching the strategic utility of such tools in e-commerce and beyond.

In conclusion, this paper provides a rigorous depiction of how machine learning, through the specific lens of representation learning, can be adapted for nuanced, large-scale market analyses, establishing a framework that is both practically viable and theoretically robust.