How organisations generate and use customer insight

Said, Emanuel; Macdonald, Emma K.; Marcos, Javier

doi:10.1080/0267257x.2015.1037785

Cited by 23 publications

(17 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instrumental application entails the application of insight in specific, direct ways to resolve a particular dilemma that pertains to a current opportunity (Macdonald, Wilson, and Konuş2012). Conceptual use entails using customer insight for the purpose of enlightenment, influencing choices and behaviors more indirectly than instrumental use without taking relatively immediate tangible action (Said et al 2015). Symbolic use includes applying customer insight to justify and sustain previously held positions and using insight to justify subsequent actions.…”

Section: Related Work Customer Insightsmentioning

confidence: 99%

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Salminen¹,

Mustak²,

Corporan³

et al. 2022

Journal of Interactive Marketing

View full text Add to dashboard Cite

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.

show abstract

Section: Related Work Customer Insightsmentioning

confidence: 99%

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Salminen¹,

Mustak²,

Corporan³

et al. 2022

Journal of Interactive Marketing

View full text Add to dashboard Cite

show abstract

“…the respective companies' business model selection and development). Since the second-hand economy is currently in an embryonic phase -characterised by diversity, rapid development, and global involvement -an exploratory multiple-case study approach was appropriate (e.g., Battistella et al, 2017;Reinartz et al, 2011;Said et al, 2015). Such an approach was chosen over a single case study, because the aim was to generate an overall understanding of the phenomenon, rather than a deep understanding of one company's business model (Eisenhardt, 1989).…”

Section: Data Collectionmentioning

confidence: 99%

A typology of second-hand business models

Yrjölä

Hokkanen

Saarijärvi

2021

Journal of Marketing Management

View full text Add to dashboard Cite

With the rise of new markets, in which products and resources are reused and redistributed, marketers must find new ways to orient and manage their business models. This study addressed one such development-the secondhand economy-which lengthens the lifespan of used goods through redistribution of their ownership. While previous research has analysed the secondhand economy mostly from a consumer perspective, a systematic understanding of value creation in this realm is lacking; hence, this study used a business perspective to construct a typology of secondhand business models. This resulting typology serves as an important analytical tool for understanding the variety of ways in which companies engage in the secondhand economy. The paper concludes with implications for managers who are either threatened by emerging secondhand business models, considering entering the secondhand market, or already engaged in secondhand commerce.

show abstract

“…Although the big potential of social media data, firms still experience several issues in gaining relevant and usable knowledge from big data (He et al , 2017; Said et al , 2015; del Vecchio et al , 2020) or might use analytics of doubtful reliability for their decision-making (Ransbotham et al , 2015). Furthermore, as big data approaches tend to gather data through machine-driven mechanical algorithms, they cannot grasp the more deep, subjective and hedonic aspects which indeed lie at the basis of experiential knowledge (Thompson, 2019; Reid and Duffy, 2018).…”

Section: Theoretical Background Of the Studymentioning

confidence: 99%

Defining customer experiential knowledge and its dimensions: a conceptualization starting from a netnographic study of specialty coffee blogs

Ietto

Pascucci

Gregori

2021

JKM

View full text Add to dashboard Cite

Purpose This paper aims to develop a theoretical framework for the conceptualization of customer experiential knowledge (CEK) by logically combining its different dimensions into one coherent explanatory concept. Drawing on the integration of the literature on customer experience, customer knowledge management and customer insights acquisition, supported by adequate empirical evidence, the framework provides a systematic, comprehensive and accurate understanding of CEK which, could contribute to the identification of relevant customer experience insights useful for customer knowledge management. Design/methodology/approach The analysis follows an inductive/deductive interpretative approach and it is based on a netnography of specialty coffee bloggers’ narratives in relation to their sustainability practices. Findings The paper identifies the following six types of CEK: normative, subcultural, epicurean, transcendental, subcultural and symbolic. Accordingly, CEK is defined as the knowledge tacitly possessed by customers in relation to how they live their consumption experiences according to a body of heterogeneous socio-cultural contextual factors (ethos, norms and symbols) and subjective influences (emotions, ingenuity, instincts and senses) deeply embedded into the narrative of a consumption experience. Originality/value While CEK has been largely observed and acknowledged, it has not been yet adequately addressed by existing research. The provision of a conceptual definition of CEK which emphasizes its different dimensions will be of use to both academics and practitioners to better identify and categorize the different manifestations of CEK when undertaking empirical observations or managerial decisions.

show abstract

How organisations generate and use customer insight

Cited by 23 publications

References 53 publications

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

A typology of second-hand business models

Defining customer experiential knowledge and its dimensions: a conceptualization starting from a netnographic study of specialty coffee blogs

Contact Info

Product

Resources

About