How to Increase Retail Sales
ssHow to Increase Sales in Retail with Market Basket Analysis
Marko Svetina, Jože Zupančič Merkur d.d. C. na Okroglo 7, Naklo, Slovenia marko.svetina@merkur.si University of Maribor, Faculty of Organizational Sciences Kidričeva cesta 55a, Kranj, Slovenia joze.zupancic@fov.uni-mb.si Abstract: This paper investigates market basket analysis as an important component of analytical CRM in retail organizations. It presents the case of the company Merkur d.d., Slovenia, a trading company dealing in items for home improvement. The business intelligence system and market basket methodology used in Merkur are described. Use of market basket analyses in Merkur is explained and analysed. In particular, the paper addresses issues such as sales promotion campaigns, placement of goods in retail stores, education of salespeople, offering system solutions and segmentation of customers. The discussed topics are explained using practical examples and guidelines for adequate business decisions. Our study demonstrated that market basket analyses are useful for Merkur, but a better direct marketing strategy must be defined and implemented. Keywords: business intelligence, market basket analysis, cross-sell, up-sell, related sales, retail, merchandising, sales campaign, CRM
1 Introduction
One of the challenges for companies that have invested heavily in customer data collection is how to extract important information from their vast customer databases and product feature databases, in order to gain competitive advantage. Market basket analysis (also known as association rule mining) is one of the data mining methods (Berry and Linoff, 2004) focusing on discovering purchasing patterns by extracting associations or co-occurrences from a store’s transactional data. Several aspects of market basket analysis have been studied in academic literature, such as using customer interest profile and interests on particular features of the product for the product development and one-to-one
Marko Svetina, Jože Zupančič Merkur d.d. C. na Okroglo 7, Naklo, Slovenia marko.svetina@merkur.si University of Maribor, Faculty of Organizational Sciences Kidričeva cesta 55a, Kranj, Slovenia joze.zupancic@fov.uni-mb.si Abstract: This paper investigates market basket analysis as an important component of analytical CRM in retail organizations. It presents the case of the company Merkur d.d., Slovenia, a trading company dealing in items for home improvement. The business intelligence system and market basket methodology used in Merkur are described. Use of market basket analyses in Merkur is explained and analysed. In particular, the paper addresses issues such as sales promotion campaigns, placement of goods in retail stores, education of salespeople, offering system solutions and segmentation of customers. The discussed topics are explained using practical examples and guidelines for adequate business decisions. Our study demonstrated that market basket analyses are useful for Merkur, but a better direct marketing strategy must be defined and implemented. Keywords: business intelligence, market basket analysis, cross-sell, up-sell, related sales, retail, merchandising, sales campaign, CRM
1 Introduction
One of the challenges for companies that have invested heavily in customer data collection is how to extract important information from their vast customer databases and product feature databases, in order to gain competitive advantage. Market basket analysis (also known as association rule mining) is one of the data mining methods (Berry and Linoff, 2004) focusing on discovering purchasing patterns by extracting associations or co-occurrences from a store’s transactional data. Several aspects of market basket analysis have been studied in academic literature, such as using customer interest profile and interests on particular features of the product for the product development and one-to-one
References: Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: for Marketing, Sales and Customer Relationship Management (second edition), Hungry Minds Inc., 2004 SYSTEMS INTEGRATION 2005 427 MARKO SVETINA, JOŽE ZUPANČIČ Chen, Y.-L., Tang, K., Shen, R.-J., Hu, Y.-H.: “Market basket analysis in a multiple store environment”, Decision Support Systems (article in press), 2004, accessed through www.ScienceDirect.com Limitedbrands: Achieving Greater Efficiencies with Market Basket Analysis, Microstrategy World 2004 Conference, Miami, 2004 Microstrategy: Business Intelligence in the Retail Industry, Microstrategy World 2003 Conference, Las Vegas, 2003 Microstrategy Web Site: http://www.microstrategy.com/Software/, Microstrategy, 2005 Merkur Commercial Analytical System - KAS, internal document, Merkur, 2005 Merkur Web Site: http://www.merkur.si/ang/podj.html, Merkur, 2005 Svetina, Marko: Izdelava in uporaba market basket analiz, http://www.muson.net/Konferenca_login.asp?mni=12, Konferenca MUS 2004, Ljubljana, 2004 Svetina, Marko: Komercialni analitski sistem v podjetju Merkur d.d., Konferenca Poslovna inteligenca in upravljanje odnosov s strankami, Ljubljana, 2002 Vindevogel, B., Van den Poel, D., Wets, G.: “Why promotion strategies based on market basket analysis do not work” (article in press), Expert Systems with Applications, 2005, accessed through www.ScienceDirect.com Weng, S.-S., Liu, J.-L.: “Feature-based recommendations for one-to-one marketing”, Expert Systems with Applications, Vol. 26, 2004, pp. 493-508. 428 SYSTEMS INTEGRATION 2005