Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework
| Autor: | Vittoria Marino, Riccardo Resciniti, Mario D’Arco, Letizia Lo Presti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Marketing
Decision support system consumer analytics Index (economics) business.industry lcsh:Marketing. Distribution of products 05 social sciences Economics Econometrics and Finance (miscellaneous) Big data Data science Data-driven Management of Technology and Innovation consumer analytics data-driven decision support systems marketing analytics 0502 economics and business data-driven 050211 marketing Marketing analytics lcsh:HF5410-5417.5 marketing analytics business Publication decision support systems 050203 business & management |
| Zdroj: | Innovative Marketing, Vol 15, Iss 4, Pp 102-115 (2019) |
| Popis: | Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|