<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Faced with increased competitive pressures from online businesses and rapidly changing consumer behaviors, traditional businesses with an online channel as well as a brick and mortar presence increasingly turn to data science and artificial intelligence to make better decisions and enhance their operations. The changing market dynamics are heavily influenced by the overwhelming adoption of mobile phones and data connections. As a result, consumers are better informed, make more real time purchase decisions, and share their experience through review sites and social media. Businesses leverage this large amount of user generated content by extracting the most useful pieces of information from it with text analytics and machine learning algorithms that identify patterns and consumer sentiment at scale. They use the critical pieces of customer feedback to market their products and services better, attract more customers, and offer them a better experience. Four uncertainties and challenges that these business encounter are the following: (1) how to improve online reputation; (2) discoverability and engagement levels of online traffic; (3) how to measure and improve the customer experience; (4) monitoring and benchmarking against competitors. The direct benefits of using Data Science and Artificial Intelligence to address those challenges are: use of standard metrics to measure strengths and weaknesses in online reputation; understanding patterns of customer behavior; listening to customer feedback at scale and extracting actionable insights directly applicable in improving operations; taking corrective actions to avoid losing customers to competitors. This paper will also analyze the competitive landscape in the field of Data Analytics and Insights solutions based on consumer generated feedback. Additionally, it will illustrate a series of case studies across multiple industries, with the purpose of exposing best practices that can be leveraged by businesses in their decision-making process.</p></abstract>

Faced with increased competitive pressures from online businesses and rapidly changing consumer behaviors, traditional businesses with an online channel as well as a brick and mortar presence increasingly turn to data science and artificial intelligence to make better decisions and enhance their operations. The changing market dynamics are heavily influenced by the overwhelming adoption of mobile phones and data connections. As a result, consumers are better informed, make more real time purchase decisions, and share their experience through review sites and social media. Businesses leverage this large amount of user generated content by extracting the most useful pieces of information from it with text analytics and machine learning algorithms that identify patterns and consumer sentiment at scale. They use the critical pieces of customer feedback to market their products and services better, attract more customers, and offer them a better experience. Four uncertainties and challenges that these business encounter are the following: (1) how to improve online reputation; (2) discoverability and engagement levels of online traffic; (3) how to measure and improve the customer experience; (4) monitoring and benchmarking against competitors. The direct benefits of using Data Science and Artificial Intelligence to address those challenges are: use of standard metrics to measure strengths and weaknesses in online reputation; understanding patterns of customer behavior; listening to customer feedback at scale and extracting actionable insights directly applicable in improving operations; taking corrective actions to avoid losing customers to competitors. This paper will also analyze the competitive landscape in the field of Data Analytics and Insights solutions based on consumer generated feedback. Additionally, it will illustrate a series of case studies across multiple industries, with the purpose of exposing best practices that can be leveraged by businesses in their decision-making process.

Improvements in business operations and customer experience through data science and Artificial Intelligence

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Faced with increased competitive pressures from online businesses and rapidly changing consumer behaviors, traditional businesses with an online channel as...

Improvements in business operations and customer experience through data science and Artificial Intelligence

Online veröffentlicht: 15. Juni 2018

Seitenbereich: 804 - 815

DOI: https://doi.org/10.2478/picbe-2018-0072

Schlüsselwörter
Online Reputation Management, Artificial Intelligence, Big Data, Text Analytics, Customer Experience, insights, competitive reporting, data-driven decisions

© 2018 Cristian-Constantin Popescu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Improvements in business operations and customer experience through data science and Artificial Intelligence

Online veröffentlicht: 15. Juni 2018

Seitenbereich: 804 - 815

DOI: https://doi.org/10.2478/picbe-2018-0072

SchlüsselwörterOnline Reputation Management, Artificial Intelligence, Big Data, Text Analytics, Customer Experience, insights, competitive reporting, data-driven decisions

© 2018 Cristian-Constantin Popescu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Online Reputation Management, Artificial Intelligence, Big Data, Text Analytics, Customer Experience, insights, competitive reporting, data-driven decisions