Big Data-enabled Customer Relationship Management: A holistic approach

Zerbino, Pierluigi; Aloini, Davide; Dulmin, Riccardo; Mininno, Valeria

doi:10.1016/j.ipm.2017.10.005

Cited by 107 publications

(77 citation statements)

References 145 publications

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“…Innovation plays a pivotal role in sustaining firms' long-term survival and profitability (Zerbino, Aloini, Dulmin, & Mininno, 2018). However, when firms are not able to master all the required knowledge resources, they may open their innovation process to external partners in order to tap into their strategic resources (Gassmann, Enkel, & Chesbrough, 2010).…”

Section: Introductionmentioning

confidence: 99%

How governance mechanisms in family firms impact open innovation choices: A fuzzy logic approach

Pellegrini

Lazzarotti

2019

Creat Innov Manage

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The inbound open innovation (OI) process consists of the opening of the innovation funnel to the contribution of external partners, with the aim of tapping into their knowledge. This opportunity is particularly valuable for family firms (FFs), which often have insufficient resources. However, the literature on the OI behaviour of family firms is not conclusive. Indeed, both the behavioural theory (BT) and the resource‐based view (RBV) posit negative and positive outcomes regarding openness choices in FFs. We claim that these contrasting results can be reconciled if, according to the literature, FFs are seen as a heterogeneous breed, whose differences are determined by different levels of participation of non‐family members in the governance mechanisms. Indeed, non‐family members can act in favour of OI. However, previous literature has failed to satisfactorily grasp the nuances of this heterogeneity. We suggest that it is necessary to adequately operationalize the heterogeneity concept by means of fuzzy logic. We conducted a survey on 178 Italian FFs. Results show that the involvement of non‐family members affects the extent to which firms draw on knowledge from external partners. Specifically, the higher the involvement of non‐family members, the more there is collaboration with vertical partners along the supply chain.

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Section: Introductionmentioning

confidence: 99%

How governance mechanisms in family firms impact open innovation choices: A fuzzy logic approach

Pellegrini

Lazzarotti

2019

Creat Innov Manage

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“…As noted by several scholars, the market impact of automation and data-driven methods is enormous and transformative (Erevelles et al, 2016;Loebbecke and Picot, 2015;Zerbino et al, 2018). While this impact is discussed in other works that explain how big data, machine learning and artificial intelligence are shaping industries (Wang et al, 2018), this research provided an example of the power of those technologies in one specific problemone that many brands and organizations struggle with.…”

Section: Classifying Online Corporate Reputationmentioning

confidence: 79%

Classifying online corporate reputation with machine learning: a study in the banking domain

Rantanen

Salminen

Ginter

et al. 2019

INTR

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Purpose User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.

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“…There is a plethora of alternative online analytics tools (e.g., Google Analytics, SimilarWeb), services (e.g., comScore, HitWise) and metrics (Boghrati et al 2017;Clarke & Jansen 2017;Järvinen & Karjaluoto 2015) that one can employ to understand customers. Moreover, companies have gained access to individual-level data that performs well for many marketing purposes, including customer relationship management (Zerbino et al 2018), providing tailored experiences and recommendations (Ronen, Yom-Tov & Lavee 2016), one-to-one targeting of online ads (Miralles-Pechuán, Ponce & Martínez-Villaseñor 2018), and enabling the creation of sophisticated customer segments Rundle-Thiele, Dietrich & Kubacki 2017). Although prevalent in many fields, the use of automated techniques is especially pertinent in the field of digital marketing which is shifting toward a higher degree of personalization, microtargeting, and one-to-one marketing (Bleier & Eisenbeiss 2015).…”

Section: Introductionmentioning

confidence: 99%

Are personas done? Evaluating their usefulness in the age of digital analytics

Salminen¹,

Jansen²,

An³

et al. 2018

Pers Stud

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In this critique, we conceptually examine the use of personas in an age of availability of large-scale online analytics data. Based on the criticism and benefits outlined in prior work, we formulate the major arguments for and against the use of personas, analyze these arguments, and demonstrate areas for the productive employment of personas by leveraging digital analytics data in their creation. From our review of the prior literature and the given availability of online customer data, our key tenet is that personas are located between aggregated and individual statistics. At their best, personas capture the coverage of the customer base attributed to aggregated data representations while retaining the interpretability of individual-level analytics. Persona creation benefits from both novel computational techniques and data sources. To demonstrate this, we propose and implement automatically generated personas primarily based on quantitative data. We also review key persona validation issues and examine how these issues can be addressed with automated persona generation using real user data from online analytics platforms. Finally, we outline areas of future research in the persona domain within the field of digital marketing and advertising. Overall, to survive in the rapidly developing marketing industry and online customer analytics, personas must evolve by adopting new practices. There are implications for this evolution of personas in a variety of domains, including design, content creation, and digital marketing.

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Big Data-enabled Customer Relationship Management: A holistic approach

Cited by 107 publications

References 145 publications

How governance mechanisms in family firms impact open innovation choices: A fuzzy logic approach

How governance mechanisms in family firms impact open innovation choices: A fuzzy logic approach

Classifying online corporate reputation with machine learning: a study in the banking domain

Are personas done? Evaluating their usefulness in the age of digital analytics

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