MICHAEL TRUSOV. Education. Dissertation. UCLA Anderson School of Management 110 Westwood Plaza, Suite B401 Los Angeles, CA

Transcription

1 MICHAEL TRUSOV of Management, Suite B (phone) (fax) web: Education Ph.D. (Marketing), of Management (expected June 2007) MBA, 2000, California State University Northridge M.A., Computer Science, 1995, Riga Technical University B.A., Computer Science, 1993, Riga Technical University Dissertation Three Essays on Internet Marketing Essay 1. Your Members Are Also Your Customers: Marketing for Internet Social Networks Perhaps the fastest growing arena in the World Wide Web is the space of social networking sites (e.g., Friendster, Facebook, MySpace). The success of these sites directly depends on the number and activity level of their users. What attracts users to the site is a continually changing digital content (e.g. messages, pictures, photos, music, videos, blogs, etc) generated by other users. In contrast, e-commerce sites (e.g. Amazon) or portals (e.g. Yahoo!) can support their well being by manipulating content (adding products, adjusting prices, updating news, etc). I propose a model which allows social networking site managers to identify community members whose network influence makes them important for the business. I link individual user activity to the online behavior of other users in the network. Using Bayesian methods and a variables selection approach, I infer which users in the community are influential in affecting the site usage of other members. Substantively, I show that in a usergenerated content environment, network effects are critical in determining a customer s value to the firm. I find significant heterogeneity among users on two dimensions: susceptibility to influence from other users and the extent of influence on others in the network. Essay 2. Estimating the Dynamic Effects of Online Word-Of-Mouth on Member Growth of Internet Social Networks In this essay I study Word-Of-Mouth (WOM) marketing in an online community. Specifically, I look at the elasticity of electronic referrals. I use daily data from a major social networking site. I observe the number of new users joining the community, as well as the number of electronic referrals sent by current community members to bring in new members. After controlling for standard marketing activities, such as promotion events and media appearances, I estimate the dynamic effect of referral activities on new customer acquisition. My results show that the elasticity of WOM referral marketing is about three times greater than traditional advertising elasticity. I use vector autoregression to control for possible endogeneity among WOM, new customer acquisitions and traditional marketing. To my knowledge it is the first study to quantify online WOM referrals in marketing. October 2006 Michael Trusov Page 1 of 7

2 Essay 3. Should We Hope You Shopped Around? Effects of Prior Internet Search on E- Commerce Site Activity In my third essay I study online shopping behavior on automotive web sites with a dataset provided by ComScore Media Metrix. My modeling objective is to predict/explain customers choices made on a selected transactional (e-commerce) web site. What distinguishes this study from previous work on online shopping is that it accounts for a user s behavior on other (possibly competing) sites prior to the first visit to the focal site. I find that a user s progress through the session on the focal site reveals much about purchase intentions. In contrast, prior activities on other sites are not as helpful in explaining user choices. While user-level clickstream data collected across multiple sites (which is typically not available to online businesses) improves the predictive power of the behavioral model, it is not dramatically important. Another key point is that, assuming cross site data is available, it is important to account for heterogeneity in different web sites of the same category as they might have different effects in explaining user behavior on the focal site. [abstracts and manuscripts for the above essays are available at Research Interests Internet and E-commerce (social networks on the Internet, clickstream analysis, electronic word-of-mouth marketing, ecrm, online recommendation systems, customer lifetime value assessment); Discrete Choice Modeling; Data Mining. Teaching Interests Marketing Management, Channels, Marketing Models, Marketing Research, Internet Marketing and E-Commerce, Customer Relationship Management and Direct Marketing. Papers in Press and Under Review Michael Trusov, Anand Bodapati and Lee Cooper (2006), Retailer Promotion Planning: Improving Forecast Accuracy and Interpretability, (forthcoming), Journal of Interactive Marketing. Michael Trusov and Randolph E. Bucklin (2006), Should We Hope You Shopped Around? Effects of Prior Internet Search on E-Commerce Site Activity, being revised for second review at Marketing Science. Working Papers Michael Trusov, Randolph E. Bucklin and Koen Pauwels (2006), Estimating the Elasticity of Word-of-Mouth Marketing, to be submitted to the Journal of Marketing. Zainab Jamal, Randolph E. Bucklin and Michael Trusov (2006), Customer Fit and Customer Retention at an Internet Recommendation Site, to be submitted to the Journal of Marketing Research. Pradeep Bhardwaj, Randolph E. Bucklin and Michael Trusov (2006), Compensating a Multi- Product Salesforce with Selling Effort Spillover: Theory and Evidence from the Automobile Industry, to be submitted to the Marketing Science. October 2006 Michael Trusov Page 2 of 7

3 Work in Progress In Search of Anti-Lock (in): Information Search on the Web, with Randolph E. Bucklin and Catarina Sismeiro Investigating the Sticker Shock Effect in Online Car Shopping, with Randolph E. Bucklin Modeling Usage Dynamics in Online Social Networking Site, with Anand Bodapati Conference Presentations Michael Trusov and Randolph E. Bucklin, Should We Hope You Shopped Around? Effects of Prior Internet Search on E-Commerce Site Activity, INFORMS Marketing Science Conference, Emory University, June 2005 Michael Trusov, Anand Bodapati and Lee Cooper, Retailer Promotion Planning: Improving Forecast Accuracy and Interpretability, International Workshop on Customer Relationship Management: Data Mining Meets Marketing, NYU, November 2005 Michael Trusov, Anand Bodapati and Randolph E. Bucklin, Predicting User Involvement in Internet Social Networks, INFORMS Marketing Science Conference, University of Pittsburgh, June 2006 Awards and Fellowships 2006: Fellow, Sheth Foundation Doctoral Consortium : Ph.D. Degree Fellowship, Graduate Division, UCLA : Ph.D. Summer Fellowship, of Management Employment : IT Development Manager, Automatic Response Technologies, San Clemente, CA Internet/Intranet applications, e-commerce, marketing processes automation, data management, digital communications : Principal, Trusoft, Northridge, CA Internet/Intranet applications, e-commerce, data warehousing, reporting, mining, and visualization : Programmer/Analyst, Information Products Inc., Sherman Oaks, CA Contact management software, direct marketing applications, Internet, database development : Network Administrator, UniBanka (now SEB Unibanka), Riga, Latvia Design, deployment and support of the enterprise network October 2006 Michael Trusov Page 3 of 7

4 Publications Outside Marketing Michael Trusov (1999), Developing for the Internet: Bringing Unsupported File Formats to the Web, Imaging and Document Solutions, January, Vol.8 No.1. Michael Trusov (1997), Conflicts in Double-click and Drag Operations, Delphi Developer's Journal, August, Vol.3 No.8. Michael Trusov (1997), Creating Cursors Dynamically, Delphi Developer's Journal, August, Vol.3 No.8. Cantu, Marco and Michael Trusov (1997), Accessing Protected Properties, Delphi Developer's Journal, February, Vol.3 No.2. Dissertation Committee Randolph Bucklin (chair) Andrew Ainslie Anand Bodapati Steven Scott (USC Marshall School of Business) Ph.D. Courses Marketing Seminars Marketing Management Bayesian Methods Discrete Choice Models Analytical Marketing Models Behavioral Decision Theory Consumer Behavior Econometrics/Statistics Probability and Statistics for Econometrics Single Equation Econometric Models System Models in Econometrics Topics in Econometrics Multivariate Statistics Bayesian Statistics (USC) Introduction to Bayesian Statistics Time Series Analysis Statistical Analysis with Latent Variables Event History Analysis Discrete Event Simulation Models and Systems Other Courses Data Mining Research Methodologies Mike Hanssens Andrew Ainslie Randolph Bucklin Pradeep Bhardwaj Sanjay Sood Shi Zhang Jinyong Hahn Ekaterini Kyriazidou Moshe Buchinsky Jinyong Hahn Don Morrison Steven Scott Chiara Sabatti Rick Schoenberg Bengt Muthen William Mason Scott Carr Stott Parker Barbara Lawrence October 2006 Michael Trusov Page 4 of 7

5 References Randolph Bucklin Professor of Marketing Office: (310) Andrew Ainslie Associate Professor of Marketing Office: (310) Anand Bodapati Assistant Professor of Marketing Office: (310) Lee G. Cooper Professor Emeritus Recalled Office: (310) Steven Scott Assistant Professor of Statistics University of Southern California 3670 Trousdale Pkwy. Bridge Hall 401-H Los Angeles, CA Office: (213) October 2006 Michael Trusov Page 5 of 7

6 Abstracts of other papers (not included in the dissertation) Customer Fit and Customer Retention at an Internet Recommendation Site, with Zainab Jamal and Randolph E. Bucklin We study the effects of customer fit, switching costs and customer interactions on customer retention at an Internet recommendation site. We define customer fit as the accuracy with which the site s recommendation engine reproduces a customer s stated preferences. We capture switching costs using the number of ratings given by a customer during a visit. We also capture customer interactions with the site using the average time spent giving a rating and the number of categories within the site visited by the customer. We use a stratified conditional Cox proportional hazard model for multiple events where each return visit to the site is defined as an event. The stratification allows a different baseline hazard function for the first versus subsequent return visits, permitting customer retention probabilities to vary across first return versus subsequent return visits. We estimate our model using clickstream data from the movies category of an Internet recommendation site. We find that the quality of the site s recommendation engine, as captured by idiosyncratic customer fit, significantly affects the probability of a customer s return visit. We also find a significant positive impact of switching costs on customer retention and that initial interactions during the first visit significantly influence customer retention. Each of these factors had the most impact following the customer s first visit, with effect sizes declining or becoming insignificant for subsequent visits. Retailer Promotion Planning: Improving Forecast Accuracy and Interpretability, with Anand Bodapati and Lee Cooper This paper considers the supermarket manager s problem of forecasting demand for a product as a function of the product s attributes and of market control variables. The typical supermarket offers hundreds of thousands of products. To forecast sales on SKU level a good model should account for product attributes, historical sales levels, and store specifics, and to control for marketing mix. One of the challenges here is that many variables which describe product, store or promotion conditions are categorical with hundreds or thousands of levels in a single attribute. Traditional tactical forecasting models would have to deal with thousands of dummy variables to denote different manufacturers, the various merchandise divisions in a store, the store-by-store specifics, the possible interactions between these sets of indicators, and/or the possible interactions with the many other variables in the model. Also, only a small number of these variables will typically be useful in predicting product demand. Furthermore, different product categories will be described on different attributes. Identifying the right product attributes and incorporating them correctly into a prediction model is a very difficult statistical problem. We propose an analytical engine that combines techniques from statistical market response modeling, datamining and combinatorial optimization to produce a small, efficient rule set that predicts sales volume levels in a manner that is optimal in several respects. In prediction tests on a holdout sample, the analytical engine s performance is very impressive. Specifically, we show that by allowing for set-valued features in the rule syntax (Cohen, 1996) the quality of predictions (in terms of correctly applied forecast adjustments) improves by as much as 50% (the original datamining algorithm [Cooper and Giufridda 2000], produced 11.84% improvement over the traditional PromoCast market-response model [Cooper et al. 1999]). Using this new PromoCast data-mining algorithm as an example, we also show that by applying simple optimization techniques to the set of generated rules, the size of the rules October 2006 Michael Trusov Page 6 of 7

7 space can be significantly reduced without loss of predictive ability. In our sample we are able to reduce the number of generated rules from 156 to just 21 while preserving the overall accuracy of the forecast. The optimized rule set is much more manageable and transparent to a marketing practitioner. Compensating a Multi Product Salesforce with Selling Effort Spillover: Theory and Evidence from Automobile Industry, with Pradeep Bhardwaj and Randolph E. Bucklin In many business situations a sales force is responsible for selling multiple products. Specifically, we consider the business situation in which selling effort expended on a focal product creates positive spillover effects for the sale of complementary or secondary products. For example, automobile dealers sell cars and then also attempt to sell add-on products such as financing, anti-theft devices, and extended warranties. Similarly, a medical devices sales force sells imaging systems and catheters to the hospitals. In general, having sold the imaging device makes it somewhat easier to sell the catheters to the same hospital. One common feature of these situations is that greater effort by the sales person to sell the focal product can make the selling the secondary product easier. This could be due to better knowledge of the customer s needs and/or a better rapport having been established between the sales person and the customer. In this paper we examine whether incentive pay should be the same for the focal versus the secondary product. If different, should incentive pay be higher for the focal or secondary product? Using the agency theoretic approach, we develop an analytical model, derive optimal compensation plans, compare them with benchmarks, and examine comparative statics. Using a proprietary data set from the automotive industry, we then conduct an empirical assessment of the implications of the model. October 2006 Michael Trusov Page 7 of 7