Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F21%3A10247518" target="_blank" >RIV/61989100:27510/21:10247518 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0268401221000505?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0268401221000505?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijinfomgt.2021.102357" target="_blank" >10.1016/j.ijinfomgt.2021.102357</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management

Popis výsledku v původním jazyce

One type of data-driven innovations in management is data-driven decision making. Confronted with a big amount of data external and internal to their organization&apos;s managers strive for predictive data analysis that enables insight into the future, but even more for prescriptive ones that use algorithms to prepare recommendations for current and future actions. Most of the decision-making techniques use deterministic machine learning (ML) techniques but unfortunately, they do not take into account the variety and volatility of decision-making situations and do not allow for a more flexible approach, i.e., adjusted to changing environmental conditions or changing management priorities. A way to better adapt ML tools to the needs of decision-makers is to use swarm intelligence ML (SIML) methods that provide a set of alternative solutions that allow matching actions with the current decision-making situation. Thus, applying SIML methods in managerial decision-making is conceptualized as a company capability as it allows for systematic alignment of allocating resources decisions vis-a -vis changing decision-making conditions. The study focuses on the customer churn management as the area of applying SIML techniques to managerial decision-making. The objectives are twofold: to present the specific features and the role of SIML methods in customer churn management and to test if a modified SIML algorithm may increase the effectiveness of churn-related segmentation and improve decision-making process. The empirical study uses publicly available customer data related to digital markets to test if and how SIML methods facilitate managerial decision-making with regard to customers potentially leaving the company in the context of changing conditions. The research results are discussed with regard to prior studies on applying ML techniques to decision-making and customer churn management studies. We also discuss the place of presented analytical approach in the literature on dynamic capabilities, especially big data-driven capabilities. (C) 2021 Elsevier Ltd

Název v anglickém jazyce

Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management

Popis výsledku anglicky

One type of data-driven innovations in management is data-driven decision making. Confronted with a big amount of data external and internal to their organization&apos;s managers strive for predictive data analysis that enables insight into the future, but even more for prescriptive ones that use algorithms to prepare recommendations for current and future actions. Most of the decision-making techniques use deterministic machine learning (ML) techniques but unfortunately, they do not take into account the variety and volatility of decision-making situations and do not allow for a more flexible approach, i.e., adjusted to changing environmental conditions or changing management priorities. A way to better adapt ML tools to the needs of decision-makers is to use swarm intelligence ML (SIML) methods that provide a set of alternative solutions that allow matching actions with the current decision-making situation. Thus, applying SIML methods in managerial decision-making is conceptualized as a company capability as it allows for systematic alignment of allocating resources decisions vis-a -vis changing decision-making conditions. The study focuses on the customer churn management as the area of applying SIML techniques to managerial decision-making. The objectives are twofold: to present the specific features and the role of SIML methods in customer churn management and to test if a modified SIML algorithm may increase the effectiveness of churn-related segmentation and improve decision-making process. The empirical study uses publicly available customer data related to digital markets to test if and how SIML methods facilitate managerial decision-making with regard to customers potentially leaving the company in the context of changing conditions. The research results are discussed with regard to prior studies on applying ML techniques to decision-making and customer churn management studies. We also discuss the place of presented analytical approach in the literature on dynamic capabilities, especially big data-driven capabilities. (C) 2021 Elsevier Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Information Management

ISSN

0268-4012

e-ISSN

—

Svazek periodika

60

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

102357

Kód UT WoS článku

000684843400009

EID výsledku v databázi Scopus

2-s2.0-85105346840