Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis

A Web site's conversion rate (the proportion of visitors who complete a desired action) is an important competitive metric. Web retailers invest significant effort in managing functionalities that can attract and convert visitors. Retailers' decisions are often based on tradition or simply follow competitors' efforts. The absence of an informed decision-making process usually leads to significant overlap in marketing efforts and investment in functionalities. This paper uses the two-step clustering algorithm to profile Web retailers in terms of Web site functionalities and Web performance metrics using data on the top 500 U. S. Web retailers ranked by their 2006 annual sales. The study finds an essential set of functionalities and indicates the presence of complementarities among sets of functionalities associated with significantly different rates of conversion and monthly visitation. It also finds different profiles for Web-only retailers versus those that have traditional channels in addition to the Web. These results may be useful for retailers in their decisions on providing Web site functionalities and in managing their conversion rates and other related metrics.

[1]  Barry G. Silverman,et al.  Implications of buyer decision theory for design of e-commerce websites , 2001, Int. J. Hum. Comput. Stud..

[2]  Gurpreet Dhillon,et al.  TORKZADEH AND DHILLON Measuring Factors that Influence the Success of Internet , 2015 .

[3]  Pei-Chann Chang,et al.  A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry , 2006, Decis. Support Syst..

[4]  Chris Janiszewski,et al.  The Influence of Display Characteristics on Visual Exploratory Search Behavior , 1998 .

[5]  ChangPei-Chann,et al.  A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry , 2006 .

[6]  Nigel Wells,et al.  Finance with a personalized touch , 2000, CACM.

[7]  DeGross.  Errata Notes: User Heterogeneity and Its Impact on Electronic Auction Market Design: An Empirical Exploration , 2004 .

[8]  Wendy W. Moe,et al.  Capturing evolving visit behavior in clickstream data , 2004 .

[9]  Per E. Pedersen,et al.  An exploratory study of customers' perception of company web sites offering various interactive applications: moderating effects of customers' Internet experience , 2004, Decis. Support Syst..

[10]  Peter S. Fader,et al.  Dynamic Conversion Behavior at E-Commerce Sites , 2004, Manag. Sci..

[11]  James R. Van Scotter,et al.  Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth , 2004, Decis. Sci..

[12]  Dennis L. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments : Conceptual Foundations 1 ) , 1998 .

[13]  Eric J. Johnson,et al.  When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices , 2002 .

[14]  P.J. Denning,et al.  On learning how to predict , 1980, Proceedings of the IEEE.

[15]  Robert J. Kauffman,et al.  A Methodology for Business Value-Driven Website Evaluation: A Data Envelopment Analysis Approach , 2004 .

[16]  Jonathan W. Palmer,et al.  Web Site Usability, Design, and Performance Metrics , 2002, Inf. Syst. Res..

[17]  Georgios A. Dafoulas,et al.  Co-evolving E-tail and On-Line Communities: Conceptual Framework , 2007, Int. J. Electron. Commer..

[18]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[19]  Mark Keil,et al.  Understanding software project risk: a cluster analysis , 2004, Inf. Manag..

[20]  Naveen Donthu,et al.  Developing a Scale to Measure the Perceived Quality of An Internet Shopping Site ( SITEQUAL ) , 2005 .

[21]  Shintaro Okazaki,et al.  What do we know about mobile Internet adopters? A cluster analysis , 2006, Inf. Manag..

[22]  Steve Muylle,et al.  Online support for commerce processes and survivability of web retailers , 2004, Decis. Support Syst..

[23]  Ann E. Schlosser,et al.  Converting Web Site Visitors into Buyers: How Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase Intentions , 2006 .

[24]  Chang Liu,et al.  Web sites of the Fortune 500 companies: Facing customers through home pages , 1997, Inf. Manag..

[25]  Murugan Anandarajan,et al.  Profiling Web Usage in the Workplace: A Behavior-Based Artificial Intelligence Approach , 2002, J. Manag. Inf. Syst..

[26]  Younghwa Lee,et al.  Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach , 2006, Decis. Support Syst..

[27]  Ian Witten,et al.  Data Mining , 2000 .

[28]  Chris Janiszewski,et al.  The Influence of Avatars on Online Consumer Shopping Behavior , 2006 .

[29]  Sara Dolnicar,et al.  Using cluster analysis for market segmentation - typical misconceptions, established methodological weaknesses and some recommendations for improvement , 2003 .

[30]  John Hagel,et al.  The real value of on-line communities , 1999 .

[31]  Erik Brynjolfsson,et al.  Frictionless Commerce? A Comparison of Internet and Conventional Retailers , 2000 .

[32]  Marios Koufaris,et al.  The Effect of Web Site Perceptions on Initial Trust in the Owner Company , 2005, Int. J. Electron. Commer..

[33]  Wendy W. Moe,et al.  The Influence of Goal‐Directed and Experiential Activities on Online Flow Experiences , 2003 .

[34]  Selwyn Piramuthu,et al.  On learning to predict Web traffic , 2003, Decis. Support Syst..

[35]  YongSeog Kim,et al.  Weighted order-dependent clustering and visualization of web navigation patterns , 2007, Decis. Support Syst..

[36]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[37]  G Silvermanbarry,et al.  Implications of buyer decision theory for design of e-commerce websites , 2001 .

[38]  V. Mahajan,et al.  An analysis of e-business adoption and its impact on business performance , 2003 .

[39]  R. Bucklin,et al.  Modeling Purchase Behavior at an E-Commerce Web Site: A Task-Completion Approach , 2004 .

[40]  D. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations , 1996 .

[41]  GudigantalaNaveen,et al.  The effects of incorporating compensatory choice strategies in Web-based consumer decision support systems , 2007 .

[42]  J. Francis,et al.  Pirqual: a scale for measuring customer expectations and perceptions of quality in internet retailing , 2002 .

[43]  Tian Zhang,et al.  BIRCH: A New Data Clustering Algorithm and Its Applications , 1997, Data Mining and Knowledge Discovery.

[44]  Kannan Srinivasan,et al.  Modeling Online Browsing and Path Analysis Using Clickstream Data , 2004 .

[45]  Yong Jin Kim,et al.  Do visitors' interest level and perceived quantity of web page content matter in shaping the attitude toward a web site? , 2006, Decis. Support Syst..

[46]  Selwyn Piramuthu,et al.  Artificial Intelligence and Information Technology Evaluating feature selection methods for learning in data mining applications , 2004 .

[47]  Donald R. Jones,et al.  The effects of incorporating compensatory choice strategies in Web-based consumer decision support systems , 2007, Decis. Support Syst..

[48]  J. Carroll,et al.  A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering , 1997 .

[49]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[50]  Geert Wets,et al.  Segmentation of visiting patterns on web sites using a sequence alignment method , 2003 .

[51]  Yao Wang,et al.  A robust and scalable clustering algorithm for mixed type attributes in large database environment , 2001, KDD '01.

[52]  Richard T. Watson,et al.  WebQual: An Instrument for Consumer Evaluation of Web Sites , 2007, Int. J. Electron. Commer..