How artificial intelligence can enable data classification for market sizing - Insights from applications in practice
Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Information Management Data Insights |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096824000600 |
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| author | L. Stallings P. Bhat J. Jacobs K. Lynch Q. Risch |
| author_facet | L. Stallings P. Bhat J. Jacobs K. Lynch Q. Risch |
| author_sort | L. Stallings |
| collection | DOAJ |
| description | Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description. |
| format | Article |
| id | doaj-art-70dfa729e76f4c198ef7b732774cc9e9 |
| institution | Kabale University |
| issn | 2667-0968 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-70dfa729e76f4c198ef7b732774cc9e92024-12-13T11:07:02ZengElsevierInternational Journal of Information Management Data Insights2667-09682024-11-0142100271How artificial intelligence can enable data classification for market sizing - Insights from applications in practiceL. Stallings0P. Bhat1J. Jacobs2K. Lynch3Q. Risch4Digital Technology, Raytheon, Dallas, TX, United StatesDigital Technology, Raytheon, Dallas, TX, United StatesCorresponding author.; Digital Technology, Raytheon, Dallas, TX, United StatesDigital Technology, Raytheon, Dallas, TX, United StatesDigital Technology, Raytheon, Dallas, TX, United StatesDetermining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.http://www.sciencedirect.com/science/article/pii/S2667096824000600Competitive intelligenceMarket intelligenceInformation classificationMarket sizingMachine learning |
| spellingShingle | L. Stallings P. Bhat J. Jacobs K. Lynch Q. Risch How artificial intelligence can enable data classification for market sizing - Insights from applications in practice International Journal of Information Management Data Insights Competitive intelligence Market intelligence Information classification Market sizing Machine learning |
| title | How artificial intelligence can enable data classification for market sizing - Insights from applications in practice |
| title_full | How artificial intelligence can enable data classification for market sizing - Insights from applications in practice |
| title_fullStr | How artificial intelligence can enable data classification for market sizing - Insights from applications in practice |
| title_full_unstemmed | How artificial intelligence can enable data classification for market sizing - Insights from applications in practice |
| title_short | How artificial intelligence can enable data classification for market sizing - Insights from applications in practice |
| title_sort | how artificial intelligence can enable data classification for market sizing insights from applications in practice |
| topic | Competitive intelligence Market intelligence Information classification Market sizing Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667096824000600 |
| work_keys_str_mv | AT lstallings howartificialintelligencecanenabledataclassificationformarketsizinginsightsfromapplicationsinpractice AT pbhat howartificialintelligencecanenabledataclassificationformarketsizinginsightsfromapplicationsinpractice AT jjacobs howartificialintelligencecanenabledataclassificationformarketsizinginsightsfromapplicationsinpractice AT klynch howartificialintelligencecanenabledataclassificationformarketsizinginsightsfromapplicationsinpractice AT qrisch howartificialintelligencecanenabledataclassificationformarketsizinginsightsfromapplicationsinpractice |