Artificial Intelligence in Retail

2022 ◽  
pp. 202-230
Author(s):  
Renu Sharma ◽  
Mamta Mohan ◽  
Prabha Mariappan
Keyword(s):  
Artificial Intelligence ◽  
Visual Search ◽  
Search Engine ◽  
Cost Effective ◽  
Retail Sector ◽  
Recommendation Algorithm ◽  
Predictive Algorithm ◽  
Customer Lifetime ◽  
Lifetime Value

This chapter gives an overview of how artificial intelligence is used by the retail sector to enhance customer experience and to improve profitability. It provides information about the role of the pandemic in stimulating AI adoption by retailers. It deliberates on how AI tools help retailers to engage customers online and in stores. Firms gain better understanding of customers, design immersive experiences, and enhance customer lifetime value using cost-effective technology solutions. It discusses popular AI algorithms like recommendation algorithm, association algorithm, classification algorithm, and predictive algorithm. Popular applications in retail include chatbots, visual search, voice search engine optimisation, in-store assistance, and virtual fitting rooms.

scholarly journals THE MEDIATION EFFECT OF INERTIA ON SERVICE DURATION

2019 ◽  
Vol 3 (1) ◽  
pp. 1-14
Author(s):  
Jonathan Lee ◽  
Heungsun Hwang ◽  
An Tran ◽  
Astrid Keel
Keyword(s):  
Structural Equation ◽  
Equation Model ◽  
Mediation Effect ◽  
Mediating Role ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Key Driver ◽  
Retention Practices ◽  
Service Duration

Firms devote large amounts of resources toward customer retention practices since relationship duration is a key driver in enhancing customer lifetime value. We posit that customer inertia plays an important role in determining service duration. In analysing service duration, we incorporate inertia into existing models that feature customer satisfaction, loyalty, and switching costs. A structural equation model is used to show the effect of latent mediation effect of inertia. We find that the mediating role of inertia is significant and the latent interaction effect of loyalty and inertia on service duration is also significant.

The Role of Marketer-Generated Content in Customer Engagement Marketing

2019 ◽  
Vol 83 (6) ◽  
pp. 21-42 ◽  
Author(s):  
Matthijs Meire ◽  
Kelly Hewett ◽  
Michel Ballings ◽  
V. Kumar ◽  
Dirk Van den Poel
Keyword(s):  
Social Media ◽  
Customer Lifetime Value ◽  
Emotional Content ◽  
Purchase Behavior ◽  
Customer Engagement ◽  
Online Content ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Digital Engagement

Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few studies have investigated firms’ ability to influence the sentiment of customers’ digital engagement. Many firms track buyers’ offline interactions, design online content to coincide with customers’ experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms’ social media engagement initiatives surrounding customers’ experiential interaction events in influencing the sentiment of customers’ digital engagement. Results indicate that marketers can influence the sentiment of customers’ digital engagement beyond their performance during customers’ interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment’s role as a leading indicator for customer lifetime value.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

Sign in / Sign up

Export Citation Format

Share Document