Comparing AI-Based and Traditional Prospect Generating Methods
Open Access
- 17 October 2021
- journal article
- research article
- Published by Taylor & Francis Ltd in Journal of Promotion Management
- Vol. 28 (2), 160-174
- https://doi.org/10.1080/10496491.2021.1987973
Abstract
This contribution deals with a comparison of one AI based data mining tool and two traditional approaches utilized to collect and interpret data for prospect generation. Traditional prospect generation methods, like manual web search or using purchased data from external providers may involve high costs and efforts and are subject to failures and waste through outdated and untargeted data. In contrast, AI based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.Keywords
This publication has 26 references indexed in Scilit:
- Model-supported business-to-business prospect prediction based on an iterative customer acquisition frameworkIndustrial Marketing Management, 2013
- Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining techniqueExpert Systems with Applications, 2013
- Social Media’s Influence on Business-to-Business Sales PerformanceJournal of Personal Selling & Sales Management, 2012
- Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketingExpert Systems with Applications, 2012
- Managing For Successful Customer Acquisition: An ExplorationJournal of Marketing Management, 2006
- The evolution of the seven steps of sellingIndustrial Marketing Management, 2005
- Internet Marketing Communications in the Selling ProcessJournal of Promotion Management, 2003
- The Internet and Global Market ResearchJournal of Promotion Management, 2003
- Attracting New ClientsJournal of Promotion Management, 2000
- Qualifying sales leads: The tight and loose approachesIndustrial Marketing Management, 1988