This second article is about social shopping communities and modelling behaviour with clickstream data from Rainer Olbrich and Christian Holsing from the marketing department of the German University of Hagen
Olbrich, Rainer and Holsing, Christian (2011) Modeling Consumer Purchasing Behavior in Social Shopping Communities with Clickstream Data International Journal of Electronic Commerce, Volume 16 Number 2, Winter 2011-12, pp. 15-40.
- Social shopping is about connecting consumers and shopping together Parallel to the increasing importance of the Internet as a shopping channel, the advent of Web 2.0 is rapidly moving the online landscape into a truly consumer-driven era. In the area of e-commerce, this results in a linkage of online shopping and social networking, initiating a new form of e-commerce, that of social shopping that connects consumers and lets them discover, share, recommend, rate, and purchase products.
- Third-party infomediary sites such as Polyvore, host social shopping communities (SSCs) [a “virtual community of consumption”] offering social shopping features such as user generated ratings, recommendation lists, tags, styles (pinboard collages) and user profiles that can initiate or simplify purchase decisions. These user benefits have lead to a high user growth rate for SSCs (Polyvore attracts more than 6 million unique visitors per month). For brands, such as Nike and Coach SSCs, this means are a solution for increasing product awareness (e.g. through style contests)
- Key features of Social Shopping Communities
- First launched about five years ago
- Target mainly women and focus on hard-to-compare soft-goods such as fashion and lifestyle product
- Include direct shopping features such as search and filter (category, gender, brand, price, and shop) as well social shopping features (recommendation lists, ratings, tagging, and ‘styles’ – pin boarded collages, and critically user profiles allowing people to follow fellow shoppers with similar tastes)
- User content (UGC) is a key part of the business model for SSC services, because users add value to the service with their own content.
- Affiliate business model, receive fees from the participating online shops for each click-out (pay per click) and actual sales (pay per sale).
- Effective social shopping features include (measured using clickstream data (web logging software output) of 2.73 million sessions);
- Social shopping features such as user ratings and tagging are positively correlated to click-outs (from a product page to purchase page – either onsite or on infomediaries such as Polyvore). Retailers delivering a good experience should therefore actively encourage shoppers to rate and tag them, as should manufacturers and brands onsite and on infomediaries
- However social shopping features such as lists and styles (pinboard collages) are negatively correlated to click-outs – but they do increase site stickiness. This may be due to information overload, psychological reactance (being turned off by being forced to use them), and the fact that these social features are attract casual browsers rather than goal-oriented shoppers.
- Although some social features do not drive e-commerce traffic, they may help reach and retain a new shopper profile, people (mostly women) who ‘shop for fun’ and use shopping for entertainment. These social shoppers have high economic value – registered social shoppers are more likely to click-out than average
- Sticky social features may therefor be useful for customer acquisition and customer loyalty, even if they don’t drive transactions. Moreover, these sticky social features may enhance advertising revenue on social shopping communities due to increased time on site (exposure to ads). Retailers and manufacturers should consider running style contests for new products pre-launch to gauge potential demand
- Overall social shopping features should be combined with price filtering, ordering and comparison as this is correlated to click-outs
- The following social shopping hypotheses were confirmed
- Hypothesis 1: the longer the view time [a proxy for consumer involvement – perceived personal relevance], the greater the likelihood of a click-out. TRUE
- Hypothesis 2: the longer the average view time per page, the lower the likelihood of a click-out [due to goal-oriented as opposed to exploratory shopping] TRUE
- Hypothesis 3: the more frequently product-detail sites are visited, the lower the likelihood of a click-out. TRUE
- Hypothesis 4: the longer the average view time per product-detail site, the greater the likelihood of a click-out. TRUE
- Hypothesis 5: the more frequently each direct shopping feature (brand, category, search field, gender, price, sales, and shop) is used, the lower the likelihood of a click-out (indicative of exploratory shopping). TRUE (except price)
- Hypothesis 6: the more frequently the home page is visited, the lower the likelihood of a click-out (indicative of exploratory shopping). TRUE
- Hypothesis 7a: the higher the overall average product rating, the greater the likelihood of a click-out. TRUE
- Hypothesis 7b: the higher the overall average shop rating, the greater the likelihood of a click-out. TRUE
- Hypothesis 8a: the more frequently lists are used, the lower the likelihood of a click-out (indicative of exploratory shopping). TRUE
- Hypothesis 8b: the more frequently styles are used, the lower the likelihood of a click-out (indicative of exploratory shopping). TRUE
- Hypothesis 9: the more frequently tags are used, the greater the likelihood of a click-out (useful for goal-oriented shopping). TRUE
- Hypothesis 10: the more frequently user profiles are used, the lower the likelihood of a click-out (indicative of exploratory shopping). TRUE
Commentary: We love the concept of social shopping sites as infomediaries – providing trustworthy information and guidance (often user generated) to inform exploratory shopping and shopping decisions (see McKinsey book ‘Net Worth‘ for more on infomediaries), and their utility in driving brand awareness, gauging demand as well as attracting and retaining ‘social shoppers’ (customer acquisition and loyalty) people, usually women, who shop for fun and entertainment.
Rainer Olbrich and Christian Holsing
ABSTRACT: Social shopping communities (SSCs) evolve from a linkage of social networking and online shopping. Apart from direct shopping features in shopbots (e.g., search fields), SSCs additionally offer user-generated social shopping features. These include recommendation lists, ratings, styles (i.e., assortments arranged by users), tags, and user profiles. Purchases can be made by following a link to a participating online shop (“click- out”). SSCs are experiencing high growth rates in consumer popularity (e.g., Polyvore attracts more than 6 million unique visitors per month). Thus, this business model has received considerable venture capital in recent years. By analyzing clickstream data, we investigate which factors, especially social shopping features, are significant for predicting purchasing behavior within SSCs. Our logit model includes about 2.73 million visiting sessions and shows that social shopping features exert a significant impact, both positive and negative. Tags and high ratings have a positive impact on a click-out. In contrast, the more lists and styles used, the less likely the user is to make a click-out. Yet, lists and styles seem to enhance site stickiness and browsing. Moreover, the more direct shopping features that are used, the less likely the user is to conduct a click-out. Increasing trans- action costs and information overload could be potential reasons. We also found that community members are more likely to make a click-out than ordinary users. This implies that community members are more profitable.
KEY WORDS AND PHRASES: Clickstream data, online consumer purchasing behavior, social shopping, user-generated content, virtual community.