Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

Sundsøy, Pål; Bjelland, Johannes; Iqbal, Asif; Pentland, Alex; Montjoye, Yves-Alexandre de

doi:10.1007/978-3-319-05579-4_45

Cited by 34 publications

(10 citation statements)

References 12 publications

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“…Assunção et al [7] propose that Big Data analysis is the key to enterprises gaining competitive advantage by helping them understand consumer behavior, segment customer groups, provide customized services, and gain more potential customers. The experimental study of Sundsøy et al [8] confirms that in mobile network marketing, operators use Big Data-driven methods to segment customers and achieve accurate marketing, which improves the conversion rates of potential customers. Enterprises can therefore identify these consumers and then increase consumer conversion rates through Big Data marketing, which in turn increases product sales.…”

Section: Introductionmentioning

confidence: 90%

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

Gao

Xie

et al. 2019

Sustainability

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The development of Big Data technology initiates an emerging research question of whether and how to invest in Big Data business for supply chain members to establish sustainable competitive edge. The aim of our study was to assess investment in Big Data business and its sustainable effects on supply chain coordination. We considered a two-stage supply chain with one supplier and one retailer who may or may not invest in Big Data business. Five decision-making modes were proposed based on the investment portfolios. The impacts of Big Data business on the profit of the supply chain and its members were analyzed and it was confirmed that a coordination scheme could achieve supply chain coordination. The results indicated that when the Big Data cost met a certain threshold, the profit of the supply chain and its members would increase whether supply chain members choose to invest in Big Data business individually or jointly. A reasonable cost allocation of Big Data business between supply chain members was provided when both members invest in Big Data. In addition, after the members invested jointly, a revenue-sharing contract could be applied to perfectly coordinate the supply chain.

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Section: Introductionmentioning

confidence: 90%

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

Gao

Xie

et al. 2019

Sustainability

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“…Numerous studies demonstrated that a key way to increase the effectiveness of DM strategies is the application of DS techniques in this industry ( Braverman, 2015 , Dremel et al, 2020 , Sundsøy et al, 2014 ). For example, Kelleher and Tierney (2018) argued that DS can increase the effectiveness of DM by improving issues such as (i) companies’ management of the information collected from users; (ii) the type and source of data from the companies’ datasets, and (iii) application of new data analysis and innovative techniques to create knowledge ( Palacios-Marqués et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Saura

2021

Journal of Innovation & Knowledge

327

131

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In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.

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“…Ensemble methods have been proven as powerful algorithms when applied to large-scale telecom datasets [14] [6]. They combine predictions of several base classifiers built with a given learning algorithm in order to improve robustness over a single classifier.…”

Section: B Modelsmentioning

confidence: 99%

“…Recent advances in Deep Learning [1] [2] have made it possible to extract high-level features from raw sensory data, leading to breakthroughs in computer vision [9][10] [11] and speech recognition [12] [13]. It seems natural to ask whether similar techniques could also be beneficial for useful prediction tasks on mobile phone data, where classic machine learning algorithms are often under-utilized due to time-consuming country and domain-specific feature engineering [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applied to Mobile Phone Data for Individual Income Classification

Sundsøy¹,

Bjelland²,

Reme³

et al. 2016

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

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Deep learning has in recent years brought breakthroughs in several domains, most notably voice and image recognition. In this work we extend deep learning into a new application domain-namely classification on mobile phone datasets. Classic machine learning methods have produced good results in telecom prediction tasks, but are underutilized due to resource-intensive and domain-specific feature engineering. Moreover, traditional machine learning algorithms require separate feature engineering in different countries. In this work, we show how socioeconomic status in large de-identified mobile phone datasets can be accurately classified using deep learning, thus avoiding the cumbersome and manual feature engineering process. We implement a simple deep learning architecture and compare it with traditional data mining models as our benchmarks. On average our model achieves 77% AUC on test data using location traces as the sole input. In contrast, the benchmarked state-of-the-art data mining models include various feature categories such as basic phone usage, top-up pattern, handset type, social network structure and individual mobility. The traditional machine learning models achieve 72% AUC in the best-case scenario. We believe these results are encouraging since average regional household income is an important input to a wide range of economic policies. In underdeveloped countries reliable statistics of income is often lacking, not frequently updated, and is rarely fine-grained to sub-regions of the country. Making income prediction simpler and more efficient can be of great help to policy makers and charity organizations-which will ultimately benefit the poor.

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Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

Cited by 34 publications

References 12 publications

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

Deep Learning Applied to Mobile Phone Data for Individual Income Classification

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