Marketing analytics using anonymized and fragmented tracking data

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works

Fast unfolding of communities in large networks

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works

Specification Tests in Econometrics

Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820 · see citing works

Introductory Econometrics: A Modern Approach

Wooldridge, J. M. (1999) · Cited by 14,354 · see citing works

Gephi: An Open Source Software for Exploring and Manipulating Networks

Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675 · see citing works

Networks

Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595 · see citing works

Network Analysis in the Social Sciences

Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009) · Science · doi:10.1126/science.1165821 · Cited by 4,280 · see citing works

Networks: an introduction

Esmark, K., Orning, H. J., & Hermanson, L. (2011) · Choice Reviews Online · doi:10.5860/choice.48-3328 · Cited by 2,950 · see citing works

The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions

Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002) · Journal of Marketing · doi:10.1509/jmkg.66.2.120.18470 · Cited by 2,257 · see citing works

An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior

Krishna, A. (2011) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2011.08.003 · Cited by 1,768 · see citing works

Big Data: New Tricks for Econometrics

Varian, H. R. (2014) · The Journal of Economic Perspectives · doi:10.1257/jep.28.2.3 · Cited by 1,588 · see citing works

Marketing Analytics for Data-Rich Environments

Wedel, M., & Kannan, P. K. (2016) · Journal of Marketing · doi:10.1509/jm.15.0413 · Cited by 1,261 · see citing works

The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization1

Awad, N. F., & Krishnan, M. S. (2006) · MIS Quarterly · doi:10.2307/25148715 · Cited by 1,252 · see citing works

Handbook of consumer psychology

Snyder, M., Stukas, A. A., & Clary, E. G. (2008) · Cited by 1,214 · see citing works

The Future of Retailing

Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017) · Journal of Retailing · doi:10.1016/j.jretai.2016.12.008 · Cited by 1,195 · see citing works

The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling

Kimball, R., & Ross, M. (1996) · Thammasat University Digital Collections · Cited by 1,177 · see citing works

Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness

Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015) · Journal of Retailing · doi:10.1016/j.jretai.2014.09.005 · Cited by 868 · see citing works

Does In-Store Marketing Work? Effects of the Number and Position of Shelf Facings on Brand Attention and Evaluation at the Point of Purchase

Chandon, P., Hutchinson, J. W., Bradlow, E. T., & Young, S. H. (2009) · Journal of Marketing · doi:10.1509/jmkg.73.6.1 · Cited by 815 · see citing works

A study of retrospective and on-line event detection

Yang, Y., Pierce, T., & Carbonell, J. (1998) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/290941.290953 · Cited by 732 · see citing works

When Should a Retailer Create an Exciting Store Environment?

Kaltcheva, V. D., & Weitz, B. A. (2005) · Journal of Marketing · doi:10.1509/jmkg.2006.70.1.107 · Cited by 691 · see citing works

An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets

Ghose, A., & Yang, S. (2009) · Management Science · doi:10.1287/mnsc.1090.1054 · Cited by 684 · see citing works

Shopper-Facing Retail Technology: A Retailer Adoption Decision Framework Incorporating Shopper Attitudes and Privacy Concerns

Inman, J. J., & Nikolova, H. D. (2017) · Journal of Retailing · doi:10.1016/j.jretai.2016.12.006 · Cited by 612 · see citing works

Impact of color on marketing

Singh, S. N. (2006) · Management Decision · doi:10.1108/00251740610673332 · Cited by 598 · see citing works

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

(2004) · doi:10.1145/1014052 · Cited by 555 · see citing works

Modeling Online Browsing and Path Analysis Using Clickstream Data

Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. (2004) · Marketing Science · doi:10.1287/mksc.1040.0073 · Cited by 540 · see citing works

Economics in the age of big data

Liran Einav, B., & Levin, J. (2014) · Science · doi:10.1126/science.1243089 · Cited by 530 · see citing works

The Role of Big Data and Predictive Analytics in Retailing

Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017) · Journal of Retailing · doi:10.1016/j.jretai.2016.12.004 · Cited by 516 · see citing works

Event detection from time series data

Guralnik, V., & Srivastava, J. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312190 · Cited by 480 · see citing works

Innovations in Shopper Marketing: Current Insights and Future Research Issues

Shankar, V., Inman, J. J., Mantrala, M. K., Kelley, J. E., & Rizley, R. (2011) · Journal of Retailing · doi:10.1016/j.jretai.2011.04.007 · Cited by 442 · see citing works

An exploratory look at supermarket shopping paths

Larson, J., Bradlow, E. T., & Fader, P. S. (2005) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2005.09.005 · Cited by 396 · see citing works

When Should a Retailer Create an Exciting Store Environment?

Kaltcheva, V. D., & Weitz, B. A. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.1.107.qxd · Cited by 350 · see citing works

The Effect of In-Store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies

Hui, S. K., Inman, J. J., Huang, Y., & Suher, J. (2013) · Journal of Marketing · doi:10.1509/jm.11.0436 · Cited by 348 · see citing works

The influence of tactile input on the evaluation of retail product offerings

Grohmann, B., Spangenberg, E. R., & Sprott, D. E. (2007) · Journal of Retailing · doi:10.1016/j.jretai.2006.09.001 · Cited by 303 · see citing works

Mobile Targeting

Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2013) · Management Science · doi:10.1287/mnsc.2013.1836 · Cited by 301 · see citing works

Positive Consumer Contagion: Responses to Attractive Others in a Retail Context

Argo, J., Dahl, D. W., & Morales, A. C. (2008) · Journal of Marketing Research · doi:10.1509/jmkr.45.6.690 · Cited by 289 · see citing works

Path Analysis of Buyer Behavior under Conditions of Crowding

Harrell, G. D., Hutt, M. D., & Anderson, J. C. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700105 · Cited by 286 · see citing works

Modeling Purchase Behavior at an E-Commerce Web Site: A Task-Completion Approach

Sismeiro, C., & Bucklin, R. E. (2004) · Journal of Marketing Research · doi:10.1509/jmkr.41.3.306.35985 · Cited by 284 · see citing works

The Effect of Examining Actual Products or Product Descriptions on Consumer Preference

McCabe, D. B., & Nowlis, S. M. (2003) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp1304_10 · Cited by 284 · see citing works

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval

(1998) · doi:10.1145/290941 · Cited by 266 · see citing works

Consumer Contamination: How Consumers React to Products Touched by Others

Argo, J., Dahl, D. W., & Morales, A. C. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.2.81 · Cited by 259 · see citing works

Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining

(1999) · doi:10.1145/312129 · Cited by 253 · see citing works

The Relationship Between Consumers’ Tendencies to Buy Compulsively and Their Motivations to Shop and Buy on the Internet

Kukar-Kinney, M., Ridgway, N. M., & Monroe, K. (2009) · Journal of Retailing · doi:10.1016/j.jretai.2009.05.002 · Cited by 250 · see citing works

Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing

Bucklin, R. E., & Sismeiro, C. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2008.10.004 · Cited by 241 · see citing works

Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Hui, S. K., Fader, P., & Bradlow, E. T. (2009) · Marketing science (Providence, R.I.) · doi:10.1287/mksc.1080.0400 · Cited by 205 · see citing works

How consumers perceive product appearance: The identification of three product appearance attributes

Blijlevens, J., Creusen, M. E. H., & Schoormans, J. P. L. (2009) · Swinburne figshare (Swinburne University of Technology) · Cited by 153 · see citing works

Generating Website Traffic

Ilfeld, J. S., & Winer, R. S. (2002) · Journal of Advertising Research · doi:10.2501/jar-42-5-49-61 · Cited by 139 · see citing works

Deconstructing the “First Moment of Truth”: Understanding Unplanned Consideration and Purchase Conversion Using In-Store Video Tracking

Hui, S. K., Huang, Y., Suher, J., & Inman, J. J. (2013) · Journal of Marketing Research · doi:10.1509/jmr.12.0065 · Cited by 136 · see citing works

Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools

Merzdovnik, G., Huber, M., Buhov, D., Nikiforakis, N., Neuner, S., Schmiedecker, M., & Weippl, E. R. (2017) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2017.26 · Cited by 134 · see citing works

Research Note—The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality

Hui, S. K., Fader, P. S., & Bradlow, E. T. (2008) · Marketing Science · doi:10.1287/mksc.1080.0402 · Cited by 119 · see citing works

Internet of Things: security and privacy implications

Maras, M. (2015) · International Data Privacy Law · doi:10.1093/idpl/ipv004 · Cited by 108 · see citing works

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics

Kivinen, J., Williams, C., & Heess, N. (2014) · Cited by 93 · see citing works

Banner advertising: Measuring effectiveness and optimizing placement

Sherman, L., & Deighton, J. (2001) · Journal of Interactive Marketing · doi:10.1002/dir.1011 · Cited by 92 · see citing works

Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences

Nagarajan, M., Gomadam, K., Sheth, A., Ranabahu, A., Mutharaju, R., & Jadhav, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04409-0_52 · Cited by 91 · see citing works

The location and allocation of products and product families on retail shelves

Russell, R. A., & Urban, T. L. (2008) · Annals of Operations Research · doi:10.1007/s10479-008-0450-y · Cited by 54 · see citing works

A graph-theoretic approach to extract storylines from search results

Kumar, R., Mahadevan, U., & Sivakumar, D. (2004) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014078 · Cited by 54 · see citing works

The digital future – A challenge for privacy?

Weber, R. H. (2015) · Computer law & security review · doi:10.1016/j.clsr.2015.01.003 · Cited by 53 · see citing works

Prediction in Economic Networks

Dhar, V., Geva, T., Oestreicher-Singer, G., & Sundararajan, A. (2014) · Information Systems Research · doi:10.1287/isre.2013.0510 · Cited by 52 · see citing works

The Third Wave of Marketing Intelligence

Burke, R. R. (2009) · Springer eBooks · doi:10.1007/978-3-540-72003-4_10 · Cited by 42 · see citing works

Measuring the Effectiveness of Location-Based Pull Advertising: A Randomized Field ExperimentPreprint

Molitor, D., Reichhart, P., Spann, M., & Ghose, A. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2645281 · Cited by 36 · see citing works

Sensory Factors and Consumer Behavior

Peck, J., & Childers, T. L. (2007) · Cited by 30 · see citing works

Validating Bluetooth logging as metric for shopper behaviour studies

Phua, P., Page, B., & Bogomolova, S. (2014) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2014.10.009 · Cited by 29 · see citing works

The Future of Ad Blocking: An Analytical Framework and New TechniquesPreprint

Storey, G., Reisman, D., Mayer, J. R., & Narayanan, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08568 · Cited by 26 · see citing works

Effective advertising

(1927) · American Journal of Potato Research · doi:10.1007/bf02884295 · Cited by 9 · see citing works

A Statistical Model for Event Sequence Data.

Heins, K., & Stern, H. S. (2014) · PubMed · Cited by 4 · see citing works

The future of social media in marketing

Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893 · see citing works

Machine learning and AI in marketing–Connecting computing power to human insights

Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602 · see citing works

Digital Analytics: Modeling for Insights and New Methods

Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T. H. A., & Potapov, D. B. (2020) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2020.04.003 · Cited by 277 · see citing works

Innovation analytics: Leveraging artificial intelligence in the innovation process

Kakatkar, C., Bilgram, V., & Füller, J. (2019) · Business Horizons · doi:10.1016/j.bushor.2019.10.006 · Cited by 217 · see citing works

Privacy threats with retail technologies: A consumer perspective

Pizzi, G., & Scarpi, D. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102160 · Cited by 124 · see citing works

A retrospective review of the first 35 years of the International Journal of Research in Marketing

Donthu, N., Reinartz, W., Kumar, S., & Pattnaik, D. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.10.006 · Cited by 115 · see citing works

Cookie Banners and Privacy Policies: Measuring the Impact of the GDPR on the Web

Kretschmer, M., Pennekamp, J., & Wehrle, K. (2021) · ACM Transactions on the Web · doi:10.1145/3466722 · Cited by 95 · see citing works

Analytics Capability in Marketing Education: A Practice-Informed Model

Kurtzke, S., & Setkute, J. (2021) · Journal of Marketing Education · doi:10.1177/02734753211042404 · Cited by 52 · see citing works

Eye tracking technology to audit google analytics: Analysing digital consumer shopping journey in fashion m-retail

Tupikovskaja-Omovie, Z., & Tyler, D. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102294 · Cited by 45 · see citing works

The internet of everything: implications of marketing analytics from a consumer policy perspective

Petrescu, M., Krishen, A. S., & Bui, M. (2020) · Journal of Consumer Marketing · doi:10.1108/jcm-02-2019-3080 · Cited by 41 · see citing works

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semeradova, T., & Weinlich, P. (2019) · IEEE Access · doi:10.1109/access.2019.2948401 · Cited by 39 · see citing works

The Impact of Artificial Intelligence Use on the E-Commerce in Romania

Micu, A., Micu, A., Geru, M., Căpățînă, A., & Muntean, M. (2021) · Amfiteatru Economic · doi:10.24818/ea/2021/56/137 · Cited by 39 · see citing works

The Usage of Artificial Intelligence in Digital Marketing: A Review

Hassan, A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72080-3_20 · Cited by 35 · see citing works

Can data analytics raise marketing agility?-A sense-and-respond perspective

Tarn, D. D. C., & Wang, J. (2022) · Information & Management · doi:10.1016/j.im.2022.103743 · Cited by 31 · see citing works

Economic consequences of online tracking restrictions: Evidence from cookies

Miller, K. M., & Skiera, B. (2023) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2023.10.001 · Cited by 30 · see citing works

Refocusing loyalty programs in the era of big data: a societal lens paradigm

Stourm, V., Neslin, S. A., Bradlow, E. T., Breugelmans, E., Chun, S. Y., Gardete, P. M., Kannan, P. K., Kopalle, P. K., Park, Y.-H., Amariles, D. R., et al. (2020) · Marketing Letters · doi:10.1007/s11002-020-09523-x · Cited by 30 · see citing works

Big Data-Driven Marketing enabled Business Performance : A Conceptual Framework of Information, Strategy and Customer Lifetime Value

Tripathi, A., Bagga, T., Sharma, S., & Vishnoi, S. K. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377156 · Cited by 29 · see citing works

Marketing 4.0 analytics in the B2B sector: a state-of-the-art review and integrated framework

Mahdiraji, H. A., Arabi, H. S., & Vrontis, D. (2023) · Journal of Strategic Marketing · doi:10.1080/0965254x.2023.2273501 · Cited by 18 · see citing works

Two Decades of Research on Consumer Behaviour and Analytics

Jayawardena, N. S., Behl, A., Ross, M., Quach, S., Thaichon, P., Pereira, V. E., Nigam, A., & Le, T. T. (2022) · Journal of Global Information Management · doi:10.4018/jgim.313381 · Cited by 18 · see citing works

Decisions of pricing and delivery-lead-time in dual-channel supply chains with data-driven marketing using internal financing and contract coordination

Xu, S., Tang, H., & Huang, Y. (2023) · Industrial Management & Data Systems · doi:10.1108/imds-07-2022-0436 · Cited by 17 · see citing works

How in-store sensor technologies can help retailers to understand their customers: overview on two decades of research

Knof, M., Stock‐Homburg, R., & Schurer, J. (2023) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2023.2273256 · Cited by 16 · see citing works

Marketing analytics acceptance: using the UTAUT, perceived trust, personal innovativeness in information technology and user attitude

Twum, K. K., & Yalley, A. A. (2024) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-01-2022-0001 · Cited by 16 · see citing works

An analysis of perfume packaging designs on consumer’s cognitive and emotional behavior using eye gaze tracking

Modi, N., & Singh, J. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18715-w · Cited by 13 · see citing works

Managing Change Related to Consumer Privacy Laws: Targeting and Personal Data Use in a More Regulated Environment

Mueller, S., Taylor, C., & Mueller, B. (2022) · Media and Change Management · doi:10.1007/978-3-030-86680-8_15 · Cited by 6 · see citing works

The Impact of User-Generated Marketing on Creating Greater Audience Connections and Brand Loyalty

Gabelaia, I., & McElroy, J. W. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-53598-7_35 · Cited by 8 · see citing works

Entrepreneurial performance and marketing analytics: the role of new product innovation

Lin, F., & Eng, T. (2024) · Journal of Small Business and Enterprise Development · doi:10.1108/jsbed-06-2023-0253 · Cited by 8 · see citing works

An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

Parameshwarappa, P., Chen, Z., & Koru, G. (2020) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.2020070105 · Cited by 7 · see citing works

IMPROVING THE QUALITY OF MARKETING ANALYTICS SYSTEMS

Osaysa, E. K. (2022) · International Journal for Quality Research · doi:10.24874/ijqr16.01-08 · Cited by 7 · see citing works

Enhancing User Experience in Fashion m-Retail: Mapping Shopping User Journey Using Google Analytics, Eye Tracking Technology and Retrospective Think Aloud Interview

Tupikovskaja-Omovie, Z. (2022) · Fashion Practice · doi:10.1080/17569370.2022.2129466 · Cited by 5 · see citing works

Marketing Analytics and Consumer Behavior: A Systematic Literature Review for Future Research Agenda

Mahajan, Y., Mahajan, V., & Kapse, M. (2024) · Springer eBooks · doi:10.1007/978-981-97-2902-9_7 · Cited by 4 · see citing works

Economic Damage of Cookie Lifetime RestrictionsPreprint

Miller, K. M., & Skiera, B. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3046254 · Cited by 4 · see citing works

Optimal Decision-Making in a Green Supply Chain Duopoly: A Comparative Analysis of Subsidy Strategies with Data-Driven Marketing

Yao, Y., Geng, S., Chen, J., Shen, F., & Tang, H. (2025) · Mathematics · doi:10.3390/math13060965 · Cited by 3 · see citing works

Using latent profile analysis to advance job demands–resources theory: An empirical demonstration in the sales context

Beeler, L. L., Mullins, R. R., & Habel, J. (2026) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-025-01140-2 · Cited by 2 · see citing works

Shared analytical capabilities in business networks

Penttinen, V., & Frösén, J. (2022) · Industrial Marketing Management · doi:10.1016/j.indmarman.2022.08.018 · Cited by 3 · see citing works

Internet of Everything in the Realm of Sustainable Marketing Post COVID-19

Poddar, B., Sethia, H., Jadon, A., & Azhar, M. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-1339-8.ch007 · Cited by 2 · see citing works

Big data or big brother: When is recontacting the digital customer OK?

Bai, Y., Wang, Y., & Ren, X. (2025) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-025-01101-9 · Cited by 1 · see citing works

Acciones para inducir las compras en el comportamiento de los consumidores de la tienda Giorgio G.

Ayala, A. D. V., González, J. C. V., & Martínez, S. E. V. (2021) · Horizontes de la Contaduría en las Ciencias Sociales · doi:10.25009/hccs.v0i11.15 · Cited by 1 · see citing works

A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity DataPreprint

Parameshwarappa, P., Chen, Z., & Koru, G. (2019) · arXiv (Cornell University) · doi:10.13016/m2xhkr-8arv · Cited by 1 · see citing works

Use of customer satisfaction survey in analytical marketing of a research institute

Dulewski, M., & Jonek-Kowalska, I. (2020) · Scientific Papers of Silesian University of Technology Organization and Management Series · doi:10.29119/1641-3466.2020.148.8

Opportunities of data-driven decision making during the product innovation process

Dömer, A. (2020)

An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

Parameshwarappa, P., Chen, Z., & Koru, G. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8954-0.ch032

Use of customer satisfaction survey in analytical marketing of a research institute

Dulewski, M., & Jonek-Kowalska, I. (2020) · Scientific Papers of Silesian University of Technology Organization and Management Series · doi:10.29119/1641-3466.2020.145.6

Information Privacy and Security Associated with Healthcare Technology Use

Amin, M. A. S. (2023) · doi:10.12794/metadc2179310

On the Transition from Traditional Retail to Cloud-Supported E-Commerce: A Design Science Project

Daase, C., Volk, M., Staegemann, D. G., & Turowski, K. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-64755-0_9

Revisión sistemática de literatura sobre capacidades de analítica del cliente

Maya-Restrepo, M. A., Rave, J. I. P., & Echavarría, F. G. (2024) · Cuadernos de Administración · doi:10.25100/cdea.v40i79.13472

تأثير تبنى مفهوم قواعد البيانات الضخمه Big data على جودة الخدمه الإلكترونية دراسة تطبيقية على مواقع الأخبار الرياضية الإلكترونية فى مصر

ابراهيم, أ. م. س. ع., & سليمان, ه. س. (2024) · Al-Maǧallaẗ Al-Akādīmiyyaẗ Lil Buḥūṯ Al-Tiğāriyyaẗ Al-Muʿāṣiraẗ · doi:10.21608/ajcjc.2024.279783.1143

Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques

Narra, B., Vattikonda, N., Buddula, V. K. R., & Patchipulusu, H. H. S. (2025) · International Journal of Advanced Research in IT and Engineering · doi:10.63665/ijarite.v10i08.01

The Impact of Data-Driven Marketing on Decision-Making: Tools and Challenges

Mansiel, I., Remondes, J., Reis, J. L., de Lima, A. P., & Teixeira, S. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-3081-3_28

Using Social Network Analysis to Analyze Eye-Tracking Behavior Data in Education Science

Yang, P. (2025) · Lecture notes in social networks · doi:10.1007/978-3-031-85240-4_3

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