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Automated competitor analysis using big data analytics: Evidence from the fitness mobile app business

Liang Guo (Neoma Business School, Mont Saint Aignan, France)
Ruchi Sharma (School of Strategy and Leadership, Neoma Business School, Mont Saint Aignan, France)
Lei Yin (Neoma Business School, Mont Saint Aignan, France)
Ruodan Lu (University of Cambridge, Cambridge, UK)
Ke Rong (Business School, Bournemouth University, Bournemouth, UK)

Business Process Management Journal

ISSN: 1463-7154

Article publication date: 5 June 2017




Competitor analysis is a key component in operations management. Most business decisions are rooted in the analysis of rival products inferred from market structure. Relative to more traditional competitor analysis methods, the purpose of this paper is to provide operations managers with an innovative tool to monitor a firm’s market position and competitors in real time at higher resolution and lower cost than more traditional competitor analysis methods.


The authors combine the techniques of Web Crawler, Natural Language Processing and Machine Learning algorithms with data visualization to develop a big data competitor-analysis system that informs operations managers about competitors and meaningful relationships among them. The authors illustrate the approach using the fitness mobile app business.


The study shows that the system supports operational decision making both descriptively and prescriptively. In particular, the innovative probabilistic topic modeling algorithm combined with conventional multidimensional scaling, product feature comparison and market structure analyses reveal an app’s position in relation to its peers. The authors also develop a user segment overlapping index based on user’s social media data. The authors combine this new index with the product functionality similarity index to map indirect and direct competitors with and without user lock-in.


The approach improves on previous approaches by fully automating information extraction from multiple online sources. The authors believe this is the first system of its kind. With limited human intervention, the methodology can easily be adapted to different settings, giving quicker, more reliable real-time results. The approach is also cost effective for market analysis projects covering different data sources.



This research is supported by “the ‘Innovation Method Fund of China’ from Ministry of Science and Technology of China (MOST) under Project No. 2016IM010200”, the National Natural Science Foundation of China (Grant No. 71402051), and “Cyrus Tang Young Scholar Awards”. All errors are of the authors.


Guo, L., Sharma, R., Yin, L., Lu, R. and Rong, K. (2017), "Automated competitor analysis using big data analytics: Evidence from the fitness mobile app business", Business Process Management Journal, Vol. 23 No. 3, pp. 735-762.



Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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