A strategic framework for artificial intelligence in marketing.

Posted on by

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

Mots-clés : intelligence artificielle, marketing stratégique, IA mécanique, IA pensante, IA sensible, segmentation, ciblage, positionnement, 4P/4C, personnalisation, relationnalisation

Objectif

L’article vise à développer un cadre stratégique en trois étapes (recherche-stratégie-action) pour intégrer l’intelligence artificielle (IA) dans le marketing, en distinguant trois types d’IA – mécanique, pensante, et sensible – et leurs applications spécifiques à chaque étape. Ce cadre cherche à guider les praticiens et à identifier les lacunes de recherche pour maximiser l’impact de l’IA en marketing.

Méthodologie

L’étude est conceptuelle et théorique, sans validation empirique directe. Les auteurs adoptent une approche multidisciplinaire en intégrant :

  • Revue de la littérature : Analyse des travaux sur les algorithmes d’IA, les réactions psychologiques des consommateurs, les impacts sociétaux, et les implications managériales (par exemple, Huang & Rust, 2018; Berger et al., 2020; Davenport et al., 2020).

  • Conceptualisation : Proposition d’un cadre basé sur un cycle marketing (recherche-stratégie-action), structuré autour des intelligences IA (mécanique pour standardisation, pensante pour personnalisation, sensible pour relationnalisation).

  • Illustration pratique : Utilisation d’exemples réels (par exemple, Amazon Prime Air, Affectiva) et de scénarios futurs, organisés selon les 4P/4C, pour démontrer l’application du cadre.

  • Synthèse des recherches existantes : Organisation des études précédentes dans le cadre proposé (Tableau 3) pour identifier les applications actuelles et les lacunes.

Résultats

  1. Cadre stratégique : Le cadre en trois étapes est articulé comme suit :

    • Recherche marketing : IA mécanique pour la collecte de données (par exemple, IoT), IA pensante pour l’analyse de marché (par exemple, prédictions de tendances), IA sensible pour la compréhension émotionnelle des clients (par exemple, analyses de sentiments).

    • Stratégie (STP) : IA mécanique pour la segmentation (reconnaissance des segments), IA pensante pour le ciblage (recommandation de segments), IA sensible pour le positionnement (résonance émotionnelle).

    • Action (4P/4C) : IA mécanique pour standardiser (par exemple, paiement automatique), IA pensante pour personnaliser (par exemple, recommandations Netflix), IA sensible pour relationnaliser (par exemple, chatbots empathiques).

  2. Applications pratiques : Le cadre est illustré par des exemples comme l’utilisation de l’IA mécanique pour la logistique (drones Amazon), de l’IA pensante pour les recommandations (Netflix), et de l’IA sensible pour l’engagement émotionnel (Affectiva).

  3. Lacunes de recherche : Peu d’études explorent l’IA sensible pour le positionnement, et les applications actuelles de l’IA sensible s’appuient souvent sur l’IA pensante en raison de son immaturité.

Limites

  1. Absence de validation empirique : Le cadre est théorique et nécessite des tests empiriques pour confirmer son efficacité (Huang & Rust, 2021).

  2. Immaturité de l’IA sensible : Les applications de l’IA sensible sont limitées par son développement technologique actuel.

  3. Généralisation : Le cadre ne tient pas compte des variations sectorielles ou culturelles dans l’adoption de l’IA.

Implications managériales

  • Recherche marketing : Utiliser l’IA mécanique pour automatiser la collecte de données à grande échelle, l’IA pensante pour analyser les tendances du marché, et l’IA sensible pour comprendre les émotions des clients.

  • Stratégie STP : Exploiter l’IA mécanique pour identifier les segments, l’IA pensante pour cibler les segments optimaux, et l’IA sensible pour créer des positionnements émotionnels.

  • Actions marketing : Adopter l’IA mécanique pour standardiser les processus, l’IA pensante pour personnaliser les offres, et l’IA sensible pour renforcer les relations client.

Références

Agarwal, R., Dugas, M., Gao, G., & Kannan, P. K. (2020). Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science, 48(1), 9–23. https://doi.org/10.1007/s11747-019-00692-4

Antons, D., & Breidbach, C. F. (2018). Big data, big insights? Advancing service innovation and design with machine learning. Journal of Service Research, 21(1), 17–39. https://doi.org/10.1177/1094670517738373

Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80–98. https://doi.org/10.1509/jmr.16.0015

Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553–1597. https://doi.org/10.1257/aer.103.5.1553

Avery, J. (2018). Artificial intelligence and branding: A snapshot into the future. The National Technology Readiness Survey. https://rockresearch.com/artificial-intelligence-snapshot-future

Avery, J., & Steenburgh, T. (2018). HubSpot and Motion AI: Chatbot-enabled CRM. Harvard Business School Case 518-067.

Balducci, B., & Marinova, D. (2018). Unstructured data in marketing. Journal of the Academy of Marketing Science, 46(4), 557–590. https://doi.org/10.1007/s11747-018-0581-x

Bauer, J., & Jannach, D. (2018). Optimal pricing in e-commerce based on sparse and noisy data. Decision Support Systems, 106, 53–63. https://doi.org/10.1016/j.dss.2017.12.002

Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020). Uniting the tribes: Using text for marketing insight. Journal of Marketing, 84(1), 1–25. https://doi.org/10.1177/0022242919873106

Blattberg, R. C., & Deighton, J. (1996). Manage marketing by the customer equity test. Harvard Business Review, 74(4), 136–144.

Chen, Y., Lee, J. Y., Sridhar, S., Mittal, V., McCallister, K., & Singal, A. G. (2020). Improving cancer outreach effectiveness through targeting and economic assessments: Insights from a randomized field experiment. Journal of Marketing, 84(3), 1–27. https://doi.org/10.1177/0022242920913026

Chintagunta, P., Hanssens, D. M., & Hauser, J. R. (2016). Editorial—Marketing science and big data. Marketing Science, 35(3), 341–342. https://doi.org/10.1287/mksc.2016.0993

Chung, T. S., Rust, R. T., & Wedel, M. (2009). My mobile music: An adaptive personalization system for digital audio players. Marketing Science, 28(1), 52–68. https://doi.org/10.1287/mksc.1080.0370

Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87. https://doi.org/10.1007/s11747-015-0441-x

Colgate, E., Wannasuphoprasit, W., & Peshkin, M. (1996). Cobots: Robots for collaboration with human operators. In Proceedings of the 1996 ASME International Mechanical Engineering Congress and Exposition (Vol. 58, pp. 433–439). New York, NY: ASME.

Cooke, A. D. J., & Zubcsek, P. P. (2017). The connected consumer: Connected devices and the evolution of customer intelligence. Journal of the Association for Consumer Research, 2(2), 164–178. https://doi.org/10.1086/690940

Daabes, A. S. A., & Kharbat, F. F. (2017). Customer-based perceptual map as a marketing intelligence source. International Journal of Economics and Business Research, 13(4), 360–379. https://doi.org/10.1504/IJEBR.2017.084299

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

Davis, E., & Marcus, G. (2015). Commonsense reasoning and commonsense knowledge in artificial intelligence. Communications of the ACM, 58(9), 92–103. https://doi.org/10.1145/2701413

Dekimpe, M. G. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), 3–14. https://doi.org/10.1016/j.ijresmar.2019.09.001

Deming, W. E. (1986). Out of the crisis. Cambridge, MA: MIT Press.

Donthu, N., & Rust, R. T. (1989). Estimating geographic customer density using kernel density estimation. Marketing Science, 8(2), 191–203. https://doi.org/10.1287/mksc.8.2.191

Drew, J. H., Mani, D. R., Betz, A. L., & Datta, P. (2001). Targeting customers with statistical and data-mining techniques. Journal of Service Research, 3(3), 205–219. https://doi.org/10.1177/109467050133002

Dzyabura, D., & Hauser, J. R. (2011). Active machine learning for consideration heuristics. Marketing Science, 30(5), 801–819. https://doi.org/10.1287/mksc.1110.0660

Dzyabura, D., & Hauser, J. R. (2019). Recommending products when consumers learn their preference weights. Marketing Science, 38(3), 417–441. https://doi.org/10.1287/mksc.2018.1147

Espino, A. (2019). Artificial intelligence: A snapshot into the future. The National Technology Readiness Survey. https://rockresearch.com/artificial-intelligence-snapshot-future

Feng, J., Li, X., & Zhang, X. (2019). Online product reviews-triggered dynamic pricing: Theory and evidence. Information Systems Research, 30(4), 1107–1123. https://doi.org/10.1287/isre.2019.0850

Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62–73.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019

Gabel, S., Guhl, D., & Klapper, D. (2019). P2V-MAP: Mapping market structures for large retail assortments. Journal of Marketing Research, 56(4), 557–580. https://doi.org/10.1177/0022243719833613

Gali, N., Camprubi, R., & Donaire, J. A. (2017). Analyzing tourism slogans in top tourism destinations. Journal of Destination Marketing & Management, 6(3), 243–251. https://doi.org/10.1016/j.jdmm.2016.04.004

Gopinath, D. (2019). Human + machine: How content analytics delivers unsurpassed value to advertisers. MSI Lunch Lecture, September 25.

Grewal, D., Morys, S., & Levy, M. (2018). The evolution and future of retailing and retailing education. Journal of Marketing Education, 40(1), 85–93. https://doi.org/10.1177/0273475318755838

Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfält, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96–113. https://doi.org/10.1007/s11747-019-00697-z

Guo, J., Zhang, W., Fan, W., & Li, W. (2018). Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. Journal of Management Information Systems, 35(4), 1121–1153. https://doi.org/10.1080/07421222.2018.1541413

Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7–18. https://doi.org/10.1509/jmkr.41.1.7.25084

Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20–38. https://doi.org/10.1016/j.ijresmar.2018.09.009

Hewett, K., Rand, W., Rust, R. T., & van Heerde, H. J. (2016). Brand buzz in the echoverse. Journal of Marketing, 80(3), 1–24. https://doi.org/10.1509/jm.15.0033

Hoffman, D. L., & Novak, T. P. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178–1204. https://doi.org/10.1093/jcr/ucx105

Huang, M.-H., & Rust, R. T. (2017). Technology-driven service strategy. Journal of the Academy of Marketing Science, 45(6), 906–924. https://doi.org/10.1007/s11747-017-0545-6

Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459

Huang, M.-H., & Rust, R. T. (2020). A framework for collaborative artificial intelligence in marketing. Journal of Retailing. https://doi.org/10.1016/j.jretai.2020.03.001

Huang, M.-H., Rust, R. T., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. https://doi.org/10.1177/0008125619863436

Humphreys, A., & Wang, R. J.-H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274–1306. https://doi.org/10.1093/jcr/ucx104

Kelly, S. D. (2019). What computers can’t create. MIT Technology Review, 122(2), 68–75.

Kim, S. Y., Schmitt, B. H., & Thalmann, N. M. (2019). Eliza in the uncanny valley: Anthropomorphizing consumer robots increases their perceived warmth but decreases liking. Marketing Letters, 30(1), 1–12. https://doi.org/10.1007/s11002-019-09485-9

Kirkpatrick, K. (2020). Tracking shoppers. Communications of the ACM, 63(2), 19–21. https://doi.org/10.1145/3371427

Kotler, P., & Keller, K. L. (2006). Marketing management (12th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317

Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.1287/mnsc.2018.3093

Lauterborn, B. (1990). New marketing litany: Four Ps passé: C-words take over. Advertising Age, 61(41), 26.

Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105–5131. https://doi.org/10.1287/mnsc.2017.2902

Lehmann, D. R. (2020). The evolving world of research in marketing and the blending of theory and data. International Journal of Research in Marketing, 37(1), 27–42. https://doi.org/10.1016/j.ijresmar.2019.10.001

Leung, E., Paolacci, G., & Puntoni, S. (2018). Human versus machine: Resisting automation in identity-based consumer behavior. Journal of Marketing Research, 55(6), 818–831. https://doi.org/10.1177/0022243718818422

Lewis, T. G., & Denning, P. J. (2018). Learning machine learning. Communications of the ACM, 61(12), 24–27. https://doi.org/10.1145/3286868

Liebman, E., Saar-Tsechansky, M., & Stone, P. (2019). The right music at the right time: Adaptive personalized playlists based on sequence modeling. MIS Quarterly, 43(3), 765–786. https://doi.org/10.25300/MISQ/2019/14750

Liu, X. (2020). De-targeting to signal quality. International Journal of Research in Marketing, 37(2), 386–404. https://doi.org/10.1016/j.ijresmar.2019.11.006

Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388. https://doi.org/10.1287/mksc.2015.0972

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013

Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines versus humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing—Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005

McDuff, D., & Czerwinski, M. (2018). Designing emotionally sentient agents. Communications of the ACM, 61(12), 74–83. https://doi.org/10.1145/3186591

Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535–556. https://doi.org/10.1177/0022243718822827

Netzer, O., Lemaire, A., & Herzenstein, M. (2019). When words sweat: Identifying signals for loan default in the text of loan applications. Journal of Marketing Research, 56(6), 960–980. https://doi.org/10.1177/0022243719852959

Neumann, N., Tucker, C. E., & Whitfield, T. (2019). Frontiers: How effective is third-party consumer profiling? Evidence from field studies. Marketing Science, 38(6), 918–926. https://doi.org/10.1287/mksc.2019.1188

Ng, I. C. L., & Wakenshaw, S. Y. L. (2017). The Internet-of-Things: Review and research directions. International Journal of Research in Marketing, 34(1), 3–21. https://doi.org/10.1016/j.ijresmar.2016.11.003

Ordenes, F. V., Ludwig, S., De Ruyter, K., Grewal, D., & Wetzels, M. (2017). Unveiling what is written in the stars: Analyzing explicit, implicit, and discourse patterns of sentiment in social media. Journal of Consumer Research, 43(6), 875–894. https://doi.org/10.1093/jcr/ucw070

Pitt, C., Mulvey, M., & Kietzmann, J. (2020). Quantitative insights from online qualitative data: An example from the health care sector. Psychology & Marketing, 37(9), 1246–1258. https://doi.org/10.1002/mar.21380

Power, B. (2017). How Harley-Davidson used artificial intelligence to increase New York sales leads by 2,930%. Harvard Business Review. https://hbr.org/2017/05/how-harley-davidson-used-predictive-analytics-to-increase-new-york-sales-leads-by-2930

Pulles, N. J., & Hartman, P. (2017). Likeability and its effect on outcomes of interpersonal interaction. Industrial Marketing Management, 66, 56–63. https://doi.org/10.1016/j.indmarman.2017.06.008

Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26. https://doi.org/10.1016/j.ijresmar.2019.08.002

Rust, R. T., Rand, W., Huang, M.-H., Stephen, A. T., Brooks, G., & Chabuk, T. (2020). Real-time brand reputation tracking using social media. Journal of Marketing, 85(2), 21–43. https://doi.org/10.1177/0022242920967913

Simester, D., Timoshenko, A., & Zoumpoulis, S. I. (2020). Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Science, 66(6), 2495–2522. https://doi.org/10.1287/mnsc.2019.3380

Soleymanian, M., Weinberg, C. B., & Zhu, T. (2019). Sensor data-driven dynamic pricing: Evidence from the auto insurance industry. Management Science, 65(8), 3433–3450. https://doi.org/10.1287/mnsc.2018.3118

Sutton, S. (2018). How AI helped one retailer find new customers. Harvard Business Review. https://hbr.org/2018/06/how-ai-helped-one-retailer-find-new-customers

Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1–20. https://doi.org/10.1287/mksc.2018.1123

Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64–78. https://doi.org/10.1007/s11747-019-00693-3

Valls, J.-F., Sureda, J., & Valls-Tuñon, G. (2018). Attracting British tourists in the Mediterranean: A data mining approach to segmenting satisfaction components. Information & Management, 55(2), 145–159. https://doi.org/10.1016/j.im.2017.05.008

van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272

Varki, S., Cooil, B., & Rust, R. T. (2000). Modeling fuzzy data in qualitative marketing research. Journal of Marketing Research, 37(4), 480–489. https://doi.org/10.1509/jmkr.37.4.480.18792

Wang, Q., Li, B., & Singh, P. V. (2018). Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis. Information Systems Research, 29(2), 273–291. https://doi.org/10.1287/isre.2017.0735

Wang, Y., Mo, D. Y., Ramamurthy, D., & Hebert, M. (2017). Learning to model the tail. In 31st Conference on Neural Information Processing Systems (NIPS).

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119

Yadav, M. S., & Pavlou, P. A. (2020). Technology-enabled interactions in digital environments: A conceptual foundation for current and future research. Journal of the Academy of Marketing Science, 48(1), 132–136. https://doi.org/10.1007/s11747-019-00702-3