在台灣，過去四十年間產業發展和變革揭示了從傳統產業到網路世代的巨大轉變，眾多的傳統產業陸續轉型從產到銷，從B2B到零售模式的拓展，最大的差異來自於買賣模式的改變，從接單交貨到面對消費者的銷售行為，商業模式的改變非常不易，很多中小企業崛起又殞落，如同浪花般的前仆後繼。而網路的發展帶來了新的通路思維，零售從單純的實體延伸到了網路電商，進而更多新的銷售型態加入，全通路的概念就此成為每一個企業決策者的挑戰。歷經疫情，線上線下的消費行為變化更大。而企業慣用的媒體也在這個時候有了新的變革，開發顧客與及行銷溝通的成本倍增。這些衝擊讓企業重新思考必須逆轉舊思維，所有的策略與計畫都該從消費者洞悉展開。
本研究的核心是探討如何以數據科學協助企業在營運管理及行銷溝通中，以創新的思維取代傳統的方式，透過數據科學，企業能夠更充分地掌握消費者行為，從而實現更長效的競爭力，不僅僅是單向的商品輸出。在網際網路以及智慧型載具覆蓋率如此高的時代，最重要的部分是如何藉由這些工具，在顧客跟銷售端之間建構出緊密的溝通網絡，彙整每一個接觸點所收集到的信息。從企業核心價值、品牌內涵、商品特性、功能、組合，一直到促銷規劃，如果這些資訊在銷售的過程裡，能夠十分完整地被收集進而分析，即是營運戰略的利基點，當然，這些信息可以多元發展成為數位行銷工具的應用，在操作中反覆獲得大量的顧客行為數據，進而解構顧客決策路徑，即可提升營銷的精準度。如果品牌經營者能夠優化及提升。
此外，研究中提出了零售的銷售，商品及顧客三個面向的數據分析，以可視化工具，具體清楚的呈現顧客和商品的靜態與動態變化，例如商品購買的關聯分析和顧客消費演化的購買漏斗圖形。通過這些工具，企業能夠更深入地了解顧客的購買決策，並快速做出更有利的商業決定。進一步地探討在全通路時代，企業如何通過數據科學和創新經營來獲得差異化的競爭優勢。深度探討零售數據和顧客決策路徑可以為企業提供一個不容易被超越的競爭門檻。在大數據的時代，企業需要重新思考和策略，從消費者洞悉出發，以應對顧客購買行為的快速變化。

Over the past forty years in Taiwan, the development and transformation of industries have revealed a significant shift from traditional sectors to the digital age. Many traditional industries have transitioned from production to sales, expanding from B2B to retail models. The biggest change has been in the buying and selling methods, moving from order fulfillment to direct consumer sales. This shift in business models has been challenging, with many small and medium-sized enterprises rising and falling like waves. The growth of the internet has introduced new channel concepts, with retail extending from physical stores to e-commerce, and the idea of omnichannel marketing has become a challenge for every decision-maker. The pandemic has further altered online and offline consumer behaviors, with the cost of customer development and marketing communication increasing significantly. These impacts have forced businesses to rethink and reverse old mindsets, with strategies and plans needing to start from consumer insights.
The core of this study is how data science can help businesses in operational management and marketing communication, replacing traditional methods with innovative thinking. Through data science, companies can better grasp consumer behavior, achieving sustained competitiveness beyond just one-way product output. In an era of high internet and smart device penetration, the key is building a tight communication network between customers and sales, collecting and analyzing information from every touchpoint. Operational strategies can gain an edge by thoroughly collecting and analyzing information about core values, brand content, product characteristics, features, combinations, and promotion plans. Utilizing digital marketing tools and repeated customer behavior data can help deconstruct customer decision-making pathways, enhancing the precision of marketing efforts.
Additionally, the study proposes data analysis in retail covering three aspects: sales, products, and customers. Visualization tools clearly present the static and dynamic changes of customers and products, like product purchase association analysis and the evolution of customer purchase funnels. These tools allow businesses to understand customer purchase decisions more deeply and make more advantageous business decisions. The study explores how businesses in the omnichannel era can gain differentiated competitive advantages through data science and innovative management. A deep dive into retail data and customer decision paths can provide a competitive threshold not easily surpassed by others. In the era of big data, businesses need to rethink and strategize from a consumer insight perspective, to respond to the rapid changes in customer buying behavior.

Abbasimehr, Hossein, Aram Bahrini (2022), “An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation,” Expert Systems with Applications, 192 (4), 116373.
Agrawal, Rakesh, Imielinski Tomasz, Swami Arun (1993), “Mining association rules between sets of items in large databases,” ACM SIGMOD International Conference on Management of Data, 22 (2), 207-16.
Ajibade, Samuel, Adediran Anthonia (2016), “An Overview of Big Data Techniques in Data Mining,” International Journal of Computer Science and Information Technology Research, 4 (3), 105-13.
Bayardo, Roberto J., Agrawal Rakesh (1999), “Mining the most interesting rules,” 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 145-54.
Bederson, Benjamin B., Shneiderman Ben (2003), “The Craft of Information Visualization: Readings and Reflections,” Morgan Kaufmann Publishers Inc.
Blackmore, Susan (2000), “The Meme Machine,” Oxford University Press, 75–76.
Bock, WJ (1989), “Principles of biological comparisons,” Acta Morphol Neerl Scand, 27 (1-2), 17-32.
Bock, WJ. (2007), “Explanations in evolutionary theory,” Journal of Zoological Systematics and Evolutionary Research, 45 (2), 89-103.
Brigham, Tara J. (2016), “Feast for the eyes: an introduction to data visualization,” Med Ref Serv Q., 35 (2), 215-23.
Chakrabarti, Soumen, Ester Martin, Fayyad Usama, Gehrke Johannes, Han Jiawei, Morishita Shinichi, Piatetsky-Shapiro Gregory (2006), “Wang W. Data mining curriculum: A proposal (Version 1.0),” Intensive Working Group of ACM SIGKDD Curriculum Committee.
Chen, Guoning (1987), “A Short Introduction on Data Visualization,” University of Houston, <http://www2.cs.uh.edu/~gnawali/courses/cosc6397-f13/intro-visualization.pdf>.
Clatworthy, Simon D. (2011), “Service innovation through touch-points: Development of an innovation toolkit for the first stages of new service development,” International Journal of Design, 5 (2).
Cleveland, Mark, Papadopoulos Nicolas, Laroche Michel (2011), “Identity, demographics, and consumer behaviors: International market segmentation across product categories,” International Marketing Review, 28 (3), 244-66.
Cronin, Jr. J., Taylor Steve (1992), Measuring service quality: A reexamination and extension,” Journal of Marketing, 56 (3), 55–68.
Daft, Richard L. (2004), “Organization theory and design (8th ed.),” Ohio: South-Western.
Davidow, Moshe (2003), “Organizational responses to customer complaints. what works and what doesn’t,” Journal of Service Research, 5 (3), 225–50.
Dawkins, Richard (1976), “The Selfish Gene,” Oxford University Press.
Deloitte, (2022), “Global Marketing Trends: Put Customers First,” <https://deloitte.wsj.com/articles/2022-global-marketing-trends-put-customers-first-01635352262>.
Deng, Qian, Messinger Paul R. (2022), “Dimensions of brand-extension fit,” International Journal of Research in Marketing, 39 (3), 764-87.
Diana, Chiara, Pacenti Elena, Tassi Roberta (2009), “Visualtiles: Communication tools for (service) design,” In: Proceedings of ServDes, 59 (6), 65–76.
Dodds, William B., Monroe Kent, Grewal Dhruv (1991), “Effects of price, brand, and store information on buyers’ product evaluations,” Journal of Marketing Research, 28 (3), 307-19.
Dubinsky, Alan J. (2013), “A Factor Analytic Study of the Personal Selling Process,” Journal of Personal Selling and Sales Management, 1 (1), 26-33.
Fader, Peter S., Hardie Bruce G.S., Shang Jen (2009), “Customer-base analysis in a discrete-time noncontractual setting,” Marketing Science, 29 (6), 1086–1108.
Felsenstein, Joseph (2004), “Inferring Phylogenies,” Sinauer Associates.
Few, Stephen (2004), “Selecting the right graph for your message,” Perceptual Edge.
Følstad, Asbjørn, Kvåle K. (2018), “Customer journeys: a systematic literature review,” Journal of Service Theory and Practice, 28 (2), 196–227.
Francken, Dick A. (1983), “Postpurchase consumer evaluations, complaint actions and repurchase behavior,” Journal of Economic Psychology, 4 (3), 273–90.
Fullerton, Gordon (2005), “The impact of brand commitment on loyalty to retail service brands,” Canadian Journal of Administrative Sciences, 22 (2), 97–110.
Gloppen, Judith (2009), “Service design leadership,” In: Proceedings of ServDev, 77–72.
Grewal, Dhruv, Roggeveena, Anne L., Nordfält Jens (2017), “The future of retailing,” Journal of Retailing, 93 (1), 1-6.
Griva, Anastasia, Bardaki Cleopatra, Pramatari Katerina, Papakiriakopoulos Dimitris (2018), “Retail business analytics: Customer visit segmentation using market basket data,” Expert Systems with Applications, 100, 1–16.
Gronholdt, Lars, Martensen Anne, Kristensen Kai (2000), “The relationship between customer satisfaction and loyalty: Cross-industry differences,” Total Quality Management, 11 (4-6), 509–14.
Hahsler, Michael, Chelluboina Sudheer (2011), “Visualizing association rules in hierarchical groups, Symposium on Interface,” Conference: In 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms.
Hahsler, Michael, Hornik Kurt, Reutterer Thomas (2006), “Implications of probabilistic data modeling for mining association rules,” In Spiliopoulou, M., Kruse, R., Borgelt, C., Nuernberger, A., Gaul, W., (Ed.) From Data & Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, 598-605.
Havel, Petr, Gupta Manomeet, Podaras Athanasios (2022), “Association Rules Mining Regarding the Value of Business Intelligence Solutions,” TEM Journal, 11 (3), 1399-1405.
Heike, Hofmann, Siebes Arno, Wilhelm Adalbert F. (2000), “Visualizing association rules with interactive mosaic plots,” 6th ACM SIGKDD International Conference on Knowledge Discovery in Database, 227-35.
Hite, Robert E., Bellizzi Joseph A. (2013), “Differences In the Importance of Selling Techniques Between Consumer and Industrial Salespeople,” Journal of Personal Selling and Sales Management, 5 (2), 19-30.
Holmlid, Stefan, Evenson Shelley (2008), “In: Hefley, B., Murphy, W. (eds.) Bringing Service Design to Service Sciences,” Management and Engineering, 341–45.
Hughes, Arthur (1994), “Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-based Marketing Program,” Probus Pub. Co, Chicago.
Hung, Kuo-Chueh (2014), “An Evolution Framework Cross-Border Mergers and Acquisitions by Living Systems Approach,” Ph.D. dissertation.
Hunt, Kenneth A., Keaveney Susan M., Lee Moonkyu (1995), “Involvement, attributions, and consumer responses to rebates,” Journal of Business and Psychology, 9 (3), 273–97.
Jansen, Bernard J., Schuster Simone (2011), “Bidding on the buying funnel for sponsored search campaigns,” Journal of Electronic Commerce Research, 12 (1), 1–18.
Jianchao, Han, An Aijun, Cercone Nick (2000), “CViz: An Interactive Visualization System for Rule Induction,” in Hamilton, H. J. (Ed.) Canadian AI 2000, LNCS 1822, 214-26.
Jones, Thomas O., Sasser W. Earl (1995), “Why satisfied customers defect,” Harvard Business Review, 73 (6), 88–99.
Kaila, Sunanda (2020), “How can businesses leverage data analytics to influence consumer purchase journey at various digital touchpoints?,” Journal of Psychosocial Research, 15 (2), 699–714.
Kankainen, Anu, Vaajakallio Kirsikka, Kantola Vesa, Mattelmäki Tuuli (2012), “Storytelling group – a co-design method for service design,” Behaviour & Information Technology, 31 (3), 221–30.
Keller, Kevin L. (2001), “Building customer-based brand equity,” Marketing Management, 10 (2), 14–19.
Klemettinen, Mika, Mannila Heikki, Ronkainen Pirjo, Toivonen Hannu, Verkamo A. Inkeri (1994), “Finding interesting rules for large sets of discovered association rules,” 3rd International Conference on Information and Knowledge Management, 401–407.
Kotler, Phillip (1999), “Marketing Management: Analysis, Planning, Implementation and Control,” Prentice Hall, New Jersey.
Kotler, Phillip (2003), “Marketing Management,” Prentice Hall, New Jersey.
LaBarbera, Priscilla A., Mazursky David (1983), “A longitudinal assessment of consumer satisfaction/dissatisfaction: The dynamic aspect of the cognitive process,” Journal of Marketing Research, 20 (4), 393–404.
McAvoy, Alex, Allen Benjamin (2021), “Fixation probabilities in evolutionary dynamics under weak selection,” Journal of Mathematical Biology, 82.
McCoy, Dakota (2018), “Evolutionary Change,” In: Shackelford, T., Weekes-Shackelford, V. (eds) Encyclopedia of Evolutionary Psychological Science. Springer, Cham.
Meroni, Anna, Sangiorgi Daniela (2011), “Design for Services,” Gower, Surrey.
Midway, Stephen R. (2020), “Principles of Effective Data Visualization,” <https://doi.org/10.1016/j.patter>.
Moncrief, William C., Marshall Greg W. (2005), “The evolution of the Seven Steps of Selling,” Industrial Marketing Management, 34 (1), 13-22.
Monroe, Kent B. (1990), “Pricing: Making Profitable Decisions,” McGraw Hill, New York.
Nengsih, Warnia (2015), “A comparative study on market basket analysis and apriori association technique,” 3rd IEEE International Conference on Information and Communication Technology (ICoICT).
Netzer, Oded, Lattin James M., Srinivasan V. (2008), “A hidden markov model of customer relationship dynamics,” Marketing Science, 27 (2), 185–204.
Nichita, Mirela, Vulpoi Marcel, Toader Georgiana (2013), “Knowledge management and customer relationship management for accounting services companies,” Chinese Business Review, 12 (6), 435–42.
Patrício, Lia, Fisk Raymond P., e Cunha, J.F., Constantine Larry (2011), “Multilevel service design: from customer value constellation to service experience blueprinting,” Journal of Service Research, 14 (2), 180–200.
Lamey, Lien, Deleersnyder Barbara, Steenkamp Jan-Benedict E.M. (2018), “Dekimpe, M.G.: New product success in the consumer packaged goods industry: A shopper marketing approach,” International Journal of Research in Marketing, 35 (3), 432–52.
Lavidge, Robert J., Steiner Gary A. (1961), “A model for predictive measurements of advertising effectiveness,” Journal of Marketing, 25 (6), 59–62.
Letunic, Ivica, Bork Peer (2018), “20 years of the SMART protein domain annotation resource,” Nucleic Acids Res., 4 (46), 493–96.
Lewis, E. St. Elmo (1908), “Financial Advertising. (The History of Advertising),” Levey Brothers, Indianapolis.
Lin, Y., C Liang, K Li, C Cheng, H Liang, JS Heh (2013), “Brand angel: an on-job-training eBook for fashion brands," National Computer Symposium (NCS).
Lin, Y., C Liang, JS Heh (2013), "From eLearning to eShopping," Taiwan E-Learning Forum (TWELF).
Lovelock, Christopher, Wright Lauren (2003), “Principles of Service Marketing and Management,” Prentice Hall, New Jersey.
Pearson, Helen (2006), "Genetics: What is a gene," Nature, 441 (7092), 398–401.
Post, Frits H., Nielson Gregory M. Bonneau Georges-Pierre (2002), “Data Visualization: The State of the Art 2002,” Kluwer International Series in Engineering and Computer Science, 713.
Reichheld, Frederick F., Sasser W. Earl (1990), “Zero defections: Quality comes to services,” Harvard Business Review, 68 (5), 105–11.
Rezende, S.O., Oliveira R.B.T., Felix L.C.M., Rocha C.A.J. (1998), “Visualization for knowledge discovery in database,” Information and Communications Technologies, 19, 81-95.
Rodrigues, Marcos, Körting Thales S., de Queiroz G.R., Sales Camila P, da Silva L. A. R. (2020), "Detecting Center Pivots In Matopiba Using Hough Transform And Web Time Series Service," IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile, 189-194.
Runthecompany, (2020), “Sale Funnel tools and insights,” <https://www.runthecompany.com/getting-sales/sales-funnel/>.
Sadiku, Matthew, Shadare Adebowale, Musa Sarhan, Akujuobi Cajetan (2012), “Data Visualization,” International Journal of Engineering Research and Advanced Technology, 2 (12), 11-16.
Shook, Beth, Nelson Katie, Aguilera Kelsie, Braff Lara (2019), “Explorations: An Open Invitation To Biological Anthropology,” American Anthropological Association.
Smiciklas, Mark (2012), “The Power of Infographics: Using Pictures to Communicate and Connect with Your Audiences,” Indianapolis:Que Pub.
Stamps, Judy A., Bell Alison M. (2021), “Combining information from parental and personal experiences: Simple processes generate diverse outcomes,” PLOS ONE, 16, 7.
Stickdorn, Marc, Schneider Jackob (2010), “This Is Service Design Thinking: Basics, Tools, Cases,” Amsterdam: BIS Publishers.
Sturm, Richard A., Frudakis Tony N. (2004), "Eye colour: portals into pigmentation genes and ancestry," Trends in Genetics, 20 (8), 327–32.
The Nielsen Company, (2018), “Anticipate and connect to your customers' retail needs,” from http://www.nielsen.com/uk/en/solutions/Retail-Analytics.html (accessed on 20 October 2019).
Townsend, William W. (1924), “Bond Salesmanship,” H. Holt, New York.
Tsiros, Michael, Mittal Vikas (2000), “Regret: A model of its antecedents and consequences in consumer decision making,” Journal of Consumer Research, 26 (4), 401–17.
Tufte, Edward R. (2001), “The Visual Display of Quantitative Information,” Connecticut: Graphics Press.
Umesh, U. N. and Martin Kagan (2015), “Data Visualization in Marketin,” Journal of Marketing Management, 3 (2), 39-46.
Usama, Fayyad, Piatetsky-Shapiro Gregory, Smyth Padhraic (1993), “From data mining to knowledge discovery in databases,” AI Magazine, 17 (3), 1-34.
Verhoef, Peter C., Lemon Katherine N., Parasuraman A., Roggeveen, Anne, Tsiros, Michael, Schlesinger Leonard A (2009), “Customer experience creation: Determinants, dynamics and management strategies,” Journal of Retailing, 85 (1), 31–41.
Viegas, Fernanda, Wattenberg Martin (2011), “How to make data look sexy,” CNN.
Visscher, Peter M., Hill William G., Wray Naomi R. (2008), "Heritability in the genomics era — concepts and misconceptions," Nature Reviews Genetics, 9 (4), 255–66.
Whittle, Susan R., Foster Morris (1991), “Customer profiling: getting into your customer’s shoes,” Management Decision, 27 (6).
Woodruff, Robert B. (1997), “Customer value: The next source of competitive advantage,” Journal of the Academy of Marketing Science, 25, 139–53.
Yang, Li (2003), “Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates,” International Conf. on Computational Science and Its Applications, 21-30.
Zeithaml, Valarie A. (1988), “Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence,” Journal of Marketing, 52 (3), 2–22.
Zomerdijk, Leonieke G., Voss Chis (2010), “Service design for experience-centric services,” Journal of Service Research, 13 (1), 67–82.
Zomerdijk, Leonieke G., Voss Christopher A. (2010), “Nsd processes and practices in experiential services,” Journal of Product Innovation Management, 28 (1), 63–80.