Product attribute extraction is an growing field in e-commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Understanding the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business. With the rise of sponsored products and their increasing prominence in online marketplaces, retailers face new challenges in effectively managing their product assortment in the presence of sponsored products. Remarkably, previous research in assortment planning largely overlooks the existence of sponsored products and their potential impact on overall recommendation effectiveness. Instead, they commonly make the simplifying assumption that all products are either organic or non-sponsored. This research gap underscores the necessity for a more thorough investigation of the assortment planning challenge when sponsored products are in play. We formulate the assortment planning problem in the presence of sponsored products as a combinatorial optimization task. The ultimate objective is to compute an assortment plan that optimizes expected revenue while considering the specific requirements of placing sponsored products strategically.

Impact Analytics has announced an extension of its partnership with PVH Corp., one of the world's largest and most admired fashion firms, to empower PVH's omnichannel business across North America, with the help of AI driven solutions for assortment planning and price optimization. The COVID-19 pandemic, the rise of e-commerce, and supply chain crisis, have made it imperative for retailers to work with intelligent, automated, and edge-aware solutions. IA's AssortSmart and PriceSmart solutions are fueled by Ada, Impact Analytics' cutting-edge AI & ML guided forecasting engine with robust predictive algorithms. With these solutions, PVH can intelligently improve forecast accuracies, and bring speed and agility to detecting and responding to changing customer preferences. "We are excited to partner with Impact Analytics in leveraging AssortSmart and PriceSmart," said Joe Todaro, EVP Retail Operations for Tommy Hilfiger North America at PVH .

Assortment planning, an important seasonal activity for any retailer, involves choosing the right subset of products to stock in each store.While existing approaches only maximize the expected revenue, we propose including the environmental impact too, through the Higg Material Sustainability Index. The trade-off between revenue and environmental impact is balanced through a multi-objective optimization approach, that yields a Pareto-front of optimal assortments for merchandisers to choose from. Using the proposed approach on a few product categories of a leading fashion retailer shows that choosing assortments with lower environmental impact with a minimal impact on revenue is possible.

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$, by considering a nested multinomial logit model with $M$ nests and $N$ items per nest. Our policy simultaneously learns customers' choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the regret at the order of $\tilde{O}(\sqrt{MNT}+MN^2)$, where $M$ is the number of nests and $N$ is the number of products in each nest. We further provide a lower bound result of $\Omega(\sqrt{MT})$, which shows the optimality of the upper bound when $T>M$ and $N$ is small. However, the $N^2$ term in the upper bound is not ideal for applications where $N$ is large as compared to $T$. To address this issue, we further generalize our first policy by introducing a discretization technique, which leads to a regret of $\tilde{O}(\sqrt{M}T^{2/3}+MNT^{1/3})$ with a specific choice of discretization granularity. It improves the previous regret bound whenever $N>T^{1/3}$. We provide numerical results to demonstrate the empirical performance of both proposed policies.

Today's shoppers have developed a new standard of behaviors, preferences and expectations for all of their retail experiences. Responsible with meeting these elevated needs, retailers seek to overcome the hurdles of legacy technology and captivate each of their customers on a 1:1 basis. In the past, consumer value expectations were centered on three variables: cost, choice, and convenience. With instant gratification available at the push of the button, customers are looking to have more control of their purchase journey and are seeking out personalized shopping experiences. Accustomed to the instant access of e-commerce shopping, customers are looking for opportunities to skip the line and have direct communication channels to ask, troubleshoot, and of course, shop.

Steeped in the language of digital commerce, today's shoppers have developed a new standard of behaviors, preferences and expectations for all of their retail experiences. Responsible with meeting these elevated needs, retailers need to overcome the hurdles of legacy technology and captivate each of their customers on a 1:1 basis. In the past, consumer value expectations were centered on three variables: cost, choice and convenience. With instant gratification available at the push of the button, customers are looking to have more control of their purchase journey and are seeking out personalized shopping experiences. Real-Time Engagement Accustomed to the instant access of e-commerce shopping, customers are looking for opportunities to skip the line and have direct communication channels to ask, troubleshoot and, of course, shop.

We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation model takes a form common in assortment planning applications: each arriving customer is offered an assortment consisting of a subset of all possible offerings; we observe only the assortment and the customer's single choice. In this paper we propose a mixture choice model with a natural underlying low-dimensional structure, and show how to estimate its parameters. In our model, the preferences of each customer or segment follow a separate parametric choice model, but the underlying structure of these parameters over all the models has low dimension. We show that a nuclear-norm regularized maximum likelihood estimator can learn the preferences of all customers using a number of observations much smaller than the number of item-customer combinations. This result shows the potential for structural assumptions to speed up learning and improve revenues in assortment planning and customization. We provide a specialized factored gradient descent algorithm and study the success of the approach empirically.