Randolph E. Bucklin

Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site

The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations from existing members, outbound WOM can be precisely tracked. Along with traditional marketing, WOM can then be linked to the number of new members subsequently joining the site (sign-ups). Because of the endogeneity among WOM, new sign-ups, and traditional marketing activity, the authors employ a vector autoregressive (VAR) modeling approach. Estimates from the VAR model show that WOM referrals have substantially longer carryover effects than traditional marketing actions and produce substantially higher response elasticities. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper-bound estimate for the financial incentives the firm might offer to stimulate WOM.

From Generic to Branded: A Model of Spillover in Paid Search Advertising

In Internet paid search advertising, marketers pay for search engines to serve text advertisements in response to keyword searches that are generic (e.g., “hotels”) or branded (e.g., “Hilton Hotels”). Although standalone metrics usually show that generic keywords have higher apparent costs to the advertiser than branded keywords, generic search may create a spillover effect on subsequent branded search. Building on the Nerlove–Arrow advertising framework, the authors propose a dynamic linear model to capture the potential spillover from generic to branded paid search. In the model, generic search advertisements serve to expose users to information about the brands ability to meet their needs, raising awareness that the brand is relevant to the search. In turn, this can induce additional future search activity for keywords that include the brand name. Using a Bayesian estimation approach, the authors apply the model to data from a paid search campaign for a major lodging chain. The results show that generic search activity positively affects future branded search activity through awareness of relevance. However, branded search does not affect generic search, demonstrating that the spillover is asymmetric. The findings have implications for understanding search behavior on the Internet and the management of paid search advertising.

Choice and the Internet: From Clickstream to Research Stream

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.

The Role of Internal Reference Points in the Category Purchase Decision

The authors study the role that reference effects play in the category purchase decision for consumer nondurable products. Category purchase behavior is represented by a nested logit model that is estimated on purchase records of shoppers in two Universal Product Code (UPC) scanner panels. A series of hypotheses are developed, modeled, and tested regarding the effects that internal reference points for product category attractiveness are likely to have on the decision to buy in a product category on a store visit. The authors hypothesize that the difference between a shoppers reference point for category attractiveness and the current level of category attractiveness will affect the purchase decision. In particular, the extent of purchase postponement caused by a loss (i.e., a negative discrepancy) should exceed the acceleration caused by a gain (i.e., a positive discrepancy). Reference effects on the category purchase decision are also hypothesized to interact with the shoppers familiarity with the store visited on a given trip. In particular, the impact of losses is predicted to be higher in unfamiliar than in familiar stores. The authors present model estimates and test results from two product categories (saltine crackers and liquid laundry detergent) and find all hypotheses to be supported. Copyright 1999 by the University of Chicago.

A Latent Instrumental Variables Approach to Modeling Keyword Conversion in Paid Search Advertising

The authors present a modeling approach to assess the purchase conversion performance of individual keywords in paid search advertising. The model facilitates estimation of daily keyword conversion and click-through rates in a sparse data environment while accounting for the endogenous position of the text advertisement served in response to a search. Position endogeneity in paid search data can arise from both omitted variables and measurement error. The authors propose a latent instrumental variable approach to address this problem. They estimate their model on keyword-level paid search data containing daily information on impressions, clicks, and reservations for a major lodging chain. They find that higher positions increase both the click-through and conversion rates. When advertisements are served in higher positions, approximately one-third of new conversions is due to increased click-through while approximately two-thirds are due to increased conversion rates. The authors show that the keyword list generated on the basis of their estimated conversion rates outperforms the status quo list as well as lists generated by observed conversion and click-through rates.

Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?

Many online shoppers initially acquired through paid search advertising later return to the same website directly. These so-called “direct type-in” visits can be an important indirect effect of paid search. Because visitors come to sites via different keywords and can vary in their propensity to make return visits, traffic at the keyword level is likely to be heterogeneous with respect to how much direct type-in visitation is generated. Estimating this indirect effect, especially at the keyword level, is difficult. First, standard paid search data are aggregated across consumers. Second, there are typically far more keywords than available observations. Third, data across keywords may be highly correlated. To address these issues, the authors propose a hierarchical Bayesian elastic net model that allows the textual attributes of keywords to be incorporated. The authors apply the model to a keyword-level data set from a major commercial website in the automotive industry. The results show a significant indirect effect of paid search that clearly differs across keywords. The estimated indirect effect is large enough that it could recover a substantial part of the cost of the paid search advertising. Results from textual attribute analysis suggest that branded and broader search terms are associated with higher levels of subsequent direct type-in visitation.

Distribution Intensity and New Car Choice

The authors develop a new method to assess how changes in the intensity of mature distribution networks—specifically, those in the U.S. automotive industry—might affect consumer choice. They capture distribution intensity by car make (e.g., Honda, Toyota) at the disaggregate level using the exact geographic locations of individual buyers and new car dealers. They develop three buyer-centric measures for the intensity level of each competing make from this information: (1) dealer accessibility (the buyers distance to the nearest outlet for each make), (2) dealer concentration (the extent to which multiple dealers for each make are located near a given buyer), and (3) dealer spread (the dispersion of the multiple dealers for each make relative to the buyers location). The authors propose a logit choice model, estimated with Bayesian methods, to study the association of these measures with new car choices. They apply the model to buyer records in the midsize premium sedan category, drawn from an anonymous sample provided by the Power Information Network. All three buyer-centric measures of intensity were significantly related to new car choice. Buyers were more likely to select cars whose dealer networks had shorter distances to the closest outlet (accessibility), more dealers within a given radius from the buyer (concentration), and locations that skewed toward the buyer (spread). Based on the modeling results, the market share elasticity of distribution intensity averages approximately .6 across the new car models included in the study. The approach should help firms evaluate the potential effects of expanding or contracting distribution networks for mature products.

From Decision Support to Decision Automation: A 2020 Vision

The authors discuss the long-run future of decision support systems in marketing. They argue that a growing proportion of marketing decisions can not only be supported but may also be automated. From a standpoint of both efficiency (e.g., management productivity) and effectiveness (e.g., resource allocation decisions), such automation is highly desirable. The authors describe how model-based automated decision-making is likely to penetrate various marketing decision-making environments and how such models can incorporate competitive dynamics. For example, the authors foresee that close to full automation can ultimately take place for many decisions about existing products in stable markets. Partial automation could characterize decision making for new products in stable markets and existing products in unstable markets.

Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment

This study examines the effects of Internet display advertising using cookie-level data from a field experiment at a financial tools provider. The experiment randomized assignment of cookies to treatment (firm ads) and control conditions (charity ads), enabling the authors to handle different sources of selection bias, including targeting algorithms and browsing behavior. They analyze display ad effects for users at different stages of the companys purchase funnel (i.e., nonvisitor, visitor, authenticated user, and converted customer) and find that display advertising positively affects visitation to the firms website for users in most stages of the purchase funnel, but not for those who previously visited the site without creating an account. Using a binary logit model, the authors calculate marginal effects and elasticities by funnel stage and analyze the potential value of reallocating display ad impressions across users at different stages. Expected visits increase almost 10% when display ad impressions are partially reallocated from nonvisitors and visitors to authenticated users. The authors also show that results from the controlled experiment data differ significantly from those computed using standard correlational approaches.

Identifying multiple preference segments from own- and cross-price elasticities

The authors develop an approach to decompose a market-level matrix of own- and cross-price elasticities to reveal potentially overlapping preference segments. The approach is grounded on the premise that markets may be represented by a parsimonious number of relatively homogeneous segments. Market-level elasticities are expressed as functions of segment weights and within-segment market shares. These relationships permit segment weights and within-segment market shares to be estimated from the market-level elasticity matrix and patterns of brand substitutability to be analyzed. The approach is illustrated with data on the grocery coffee category.