Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns. Issue 10 (15th February 2021)

• Record Type: Journal Article
• Authors: Tang, Zhongjun; Wang, Tingting; Cui, Junfu; Han, Zhongya; He, Bo
• Is Part Of: Management decision. Volume 59:Issue 10(2021)
• Page Start: 2528
• Page End: 2548
• Publication Date: 2021-02-15
• Subjects: Experiential product with short life cycle -- Sales prediction -- Interval prediction -- Code category system -- Attribute relationship pattern -- Pattern network graph; Management -- Periodicals; 658.403
• DOI: 10.1108/MD-03-2020-0320
• Languages: English
• ISSNs: 0025-1747

Abstract

Abstract : Purpose: Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM). Design/methodology/approach: Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017. Findings: The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production. Originality/value: Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.