Retail Analytics in the New Normal: The Influence of Artificial
Intelligence and the Covid-19 Pandemic
- URL: http://arxiv.org/abs/2312.00046v1
- Date: Mon, 27 Nov 2023 08:16:30 GMT
- Title: Retail Analytics in the New Normal: The Influence of Artificial
Intelligence and the Covid-19 Pandemic
- Authors: Yossiri Adulyasak, Maxime C. Cohen, Warut Khern-am-nuai, Michael
Krause
- Abstract summary: The COVID-19 pandemic has severely disrupted the retail landscape.
We discuss the opportunities that AI can offer to retailers in the new normal retail landscape.
- Score: 1.8434042562191815
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The COVID-19 pandemic has severely disrupted the retail landscape and has
accelerated the adoption of innovative technologies. A striking example relates
to the proliferation of online grocery orders and the technology deployed to
facilitate such logistics. In fact, for many retailers, this disruption was a
wake-up call after which they started recognizing the power of data analytics
and artificial intelligence (AI). In this article, we discuss the opportunities
that AI can offer to retailers in the new normal retail landscape. Some of the
techniques described have been applied at scale to adapt previously deployed AI
models, whereas in other instances, fresh solutions needed to be developed to
help retailers cope with recent disruptions, such as unexpected panic buying,
retraining predictive models, and leveraging online-offline synergies.
Related papers
- The Patterns of Digital Deception [0.0]
New data-analysis techniques have disrupted the balance of power between companies and their customers.
Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles.
The same tools are also used for ill -- to target vulnerable members of the population with scams specially tailored to prey on their weaknesses.
arXiv Detail & Related papers (2024-12-25T15:55:17Z) - Parcel loss prediction in last-mile delivery: deep and non-deep
approaches with insights from Explainable AI [1.104960878651584]
We propose two machine learning approaches, namely, Data Balance with Supervised Learning (DBSL) and Deep Hybrid Ensemble Learning (DHEL)
The practical implication of such predictions is their value in aiding e-commerce retailers in optimizing insurance-related decision-making policies.
arXiv Detail & Related papers (2023-10-25T12:46:34Z) - Causal Reasoning: Charting a Revolutionary Course for Next-Generation
AI-Native Wireless Networks [63.246437631458356]
Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native.
This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning.
We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
arXiv Detail & Related papers (2023-09-23T00:05:39Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Playing hide and seek: tackling in-store picking operations while
improving customer experience [0.0]
We formalize a new problem called Dynamic In-store Picker Problem routing (diPRP)
In this relevant problem - diPRP - a picker tries to pick online orders while minimizing customer encounters.
Our work suggests that retailers should be able to scale the in-store picking of online orders without jeopardizing the experience of offline customers.
arXiv Detail & Related papers (2023-01-05T16:35:17Z) - Augmented cross-selling through explainable AI -- a case from energy
retailing [0.0]
We analyze data on 220,185 customers of an energy retailer, predict cross-purchases with up to 86% correctness (AUC), and show that the XAI method SHAP provides explanations that hold for actual buyers.
We further outline implications for research in information systems, XAI, and relationship marketing.
arXiv Detail & Related papers (2022-08-24T09:51:52Z) - INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
We develop a new learning paradigm named INTERN.
By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability.
In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
arXiv Detail & Related papers (2021-11-16T18:42:50Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z) - The challenges and realities of retailing in a COVID-19 world:
Identifying trending and Vital During Crisis keywords during Covid-19 using
Machine Learning (Austria as a case study) [0.0]
It is recommended to opt for forecasting against trending based benchmark because auditing a future forecast puts more focus on seasonality.
The forecasting models provide with end-to-end, real time oversight of the entire supply chain.
arXiv Detail & Related papers (2021-05-10T18:31:45Z) - Adversarial Attacks on Machine Learning Systems for High-Frequency
Trading [55.30403936506338]
We study valuation models for algorithmic trading from the perspective of adversarial machine learning.
We introduce new attacks specific to this domain with size constraints that minimize attack costs.
We discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models.
arXiv Detail & Related papers (2020-02-21T22:04:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.