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Analyzing Website Choice Using Clickstream Data

This paper uses clickstream data from Plurimus Corp. (formerly Foveon Corp.) to analyze user choice of Internet portals. It will show that commonly used econometric models for examining grocery scanner data can be applied to clickstream data on advertising-based online markets. Developing a framework to study consumer choices of free websites is an essential step to better understanding user behavior on the Internet. The main data for this study is a clickstream data set consisting of every website visited by 2654 users from December 27 1999 to March 31 2000. Using this data, I construct several variables including search success, time spent searching, and whether a website is an individual's starting page. I also have advertising and media mentions data. This study helps to increase understanding of user behavior on the Internet. It explores some key determinants of website choice and simulates market responses to changes in the online environment. By better understanding website choice, we can better evaluate the implications of policy decisions.


Introduction
The growth of the Internet has provided economists, marketers, and statisticians with a potentially rich and informative data source. Since everything on the Internet is necessarily digital, all activity can be easily recorded and stored in a database for future examination. This data has found disparate uses, from advertisement targeting to law enforcement. One prevalent but relatively under used example of such data is clickstream data. This data consists of each website visited by a panel of users and the order in which they arrive at the sites. It is often accompanied by the time of arrival at and departure from the site as well as the degree of activity at the site and the demographic characteristics of the users.
Examples of companies that collect clickstream data based on broad panels are Netratings Inc., MediaMetrix Inc., and Plurimus Corp. This paper uses data from Plurimus Corp. to analyze user choice of Internet portals. It will show that commonly used econometric models for examining grocery scanner data can be applied to clickstream data on advertising-based online markets.
A portal is a launching pad to the Internet. Portals, such as Yahoo, Lycos, and MSN, are sometimes referred to as search engines. Adar and Huberman (1999 p. 2) describe them as "a refinement of the web search engine service". Portals have search engine capabilities, but they also have other features. These may include email, news, and a link-based directory to the web separate from the search service. There are few, if any, pure search engines remaining. In this paper, I am interested in the portal as a starting point and not as a destination. Therefore I look at the use of portal main pages, directory pages, and search pages, but not at email, news, and shopping pages.
The methodology used here closely mimics that of Guadagni and Little's (1983) paper that estimates a multinomial logit model with scanner data to examine consumer coffee purchases. It shows that the model has reasonably good out-of-sample predictive ability.
Furthermore, informative simulations can be conducted on the effects on market share of changing a variable. For example, it can derive an estimate of the impact on number of visits of increasing advertising by one dollar. The results, however, have to be interpreted with caution. The data does not satisfy the Independent of Irrelevant Alternatives assumption made in the model. This assumption implies that there is no correlation between the alternatives outside of the observed variables. When this assumption fails, estimating switching behavior becomes difficult. The coefficients and the simulation results will therefore have some bias. Future work will apply the techniques of more recent developments in the econometric analysis of panel data to the Internet portal market. This will alleviate the above problem.
Guadagni and Little take advantage of the richness of their data set by treating each purchase as a separate observation. Those few studies that have used clickstream data thus far (such as Goldfarb (2000a), Moe and Fader (2000), Sandvig (2000)), have aggregated the data to a market share level. While this has provided interesting insights into specific problems, it is not the best approach to understanding website choice and the causes of website shares within a given market. Aggregating the data deletes considerable relevant information. Important determinants of website choice include an individual's past experience at a site and the site that the individual went to the previous time. Unlike most other marketing studies using choice-specific data, there is no monetary price here. Goettler and Shachar (1999) also examine a consumer panel that faces no price consisting of individual choices of television shows.
Developing a framework to study consumer choices of free (advertising-supported) websites is an essential step to better understanding user behavior on the Internet. According to the data set used in this study, more than two-thirds of all consumer Internet traffic is at advertising-supported sites. With the exceptions of Amazon and EBay, the top twenty sites in terms of unique visitors are all advertising-supported. The literature on this important aspect of the Internet is sparse. Three studies that focus on advertising-supported websites are Adar and Huberman (1999), Gandal (2001), and Goldfarb (2000a). Adar and Huberman (1999) show that portals can discriminate between users as those looking for certain topics are willing to spend more time. This means that search engines can capture more consumer surplus (in the form of advertising revenue), but forcing consumers that are willing to spend more time to view more pages and advertisements. Gandal (2001) examines market share at an aggregate level to try to examine the portal market. He finds that early entrants have an advantage and that certain features matter more than others. Goldfarb (2000a) examines concentration levels in advertising-supported Internet markets. Lynch and Ariely (2000) is one of few Internet studies that looks at choice-specific data.
They construct a simulated environment for the purchase of wine and examine purchase choice. Like Lynch and Ariely's study, this paper takes advantage of the choice-specific data. Unlike their study, I look at the choice of free web sites using actual user clickstreams.
The main data for this study was supplied by Plurimus Corporation. It is a clickstream data set consisting of every website visited by 2654 users from December 27 1999 to March 31 2000. It also contains data on the time of arrival at and departure from each site. In total, the data set contains 3,228,595 website visits, of which 859,587 (2622 people) are to Internet portals. Using this data, I construct measures of past search success, past time spent searching, whether a site is an individual's starting page, whether an individual has an email account at the site, and the number of pages viewed at each site. A considerable section of this paper is dedicated to explaining the construction of these variables from the raw data. Many of the decisions were based on a questionnaire conducted in June 2000 (see Goldfarb 2000b for further details). I link the Plurimus data to monthly advertising spending data from J. Walter Thompson Company and media mentions data found through the Lexis-Nexis Academic Universe.
The next section of the paper will describe the application of the methodology used by Guadagni and Little to the present problem. Section three will describe the data set, the questionnaire used to inform data construction, the actual process of data construction, and summary statistics. Section four will present the results, test the model's predictive ability, and examine market response to changes in the control variables. The paper will conclude by summarizing the key results and proposing several potential areas for future research.

Using the Multinomial Logit With Clickstream Data
Internet users choose which website to visit just as they make several other economic choices: given the alternatives available and the information they have about those alternatives, they choose the alternative that will give them the highest utility. In terms of grocery products such as coffee (studied by Guadagni and Little), this means that households buy the product that has the best attributes for the lowest price. In terms of portals, this means that households will use the portal that will allow them to maximize the probability of finding what they seek and minimizing the time spent. Conceptually, I assume households are exogenously given a "goal" when they go online. I explore this assumption in the questionnaire part of this paper and in Goldfarb (2000b). They go to the portal that they expect will help them achieve that goal in the least time with the most accuracy.
In the multinomial logit model, the expected utility of the portal is based on past history, several website characteristics (that may vary over time), outside influences such as advertising and media mentions, and an idiosyncratic error term. Formally, household i visits website j at choice occasion t when for all k = j. Here u ijt is defined by X ijt may include variables that change over any or all of i, j, and t. β may vary over i, j, or t, implying household heterogeneity, brand heterogeneity, time (choice occasion) heterogeneity or any combination of the three. In this study, X ijt will never vary over just t, just i, just t and i, or just t and j. It will vary over just j in the form of portal-specific dummy variables. β will be assumed constant. Future work will look at heterogeneity across households in β. There are I households, J websites, T i choice occasions for each household, and I i=1 T i total choice occasions. It is expected utility to the user, not to the observer, that is of interest. It is assumed that the user knows ε ijt . The expectation is taken over relevant variables that the user may not know the value of before visiting the website. For example, the user does not know how long she will spend on the website. She does, however, have an expectation of how long it will take based on her past experience at that website.
In order to get the multinomial logit form, the ε ijt are assumed to be independently distributed random variables with a type II extreme value distribution. Given the above assumptions, the probability of household i choosing brand j at choice occasion t can be expressed as: The model, as expressed above is a combination of Theil's (1969) multinomial logit and McFadden's (1974) conditional logit. It is commonly referred to as a mixed logit or as a multinomial logit. Since this paper assumes β is fixed, the model here is a conditional logit.
The log likelihood function is as follows: where d ijt is equal to one if alternative j is chosen by individual i at time t, and is equal to zero otherwise.
A significant potential problem with this framework is that it implies an assumption of independence of irrelevant alternatives (IIA). If a household is offered a new alternative that is almost identical to one of the current alternatives, say k, then this new alternative should be expected to only draw buyers from k; however, under IIA, the new alternative draws buyers from all the other alternatives. I test for and reject the assumption of IIA in section 4. This is a significant problem that will be addressed in future work by allowing for household heterogeneity.
In this model, the researcher observes the choice by each household on each choice occasion. Let y ijt = 1 if household i chooses website j on choice occasion t and let y ijt = 0 otherwise. The researcher also observes the characteristics of each website at that choice occasion for that household X ijt .

Data
A. Raw data sources and description The fourth limitation is that the data is collected at the household level rather than at the individual level. If two people in a given household have considerably different habits this will show up as one person with widely varying habits. While this makes it difficult to assess the extent of learning over time, it is a standard problem in consumer panels.
Fifth, it does not contain information on households from the first time they go online.
Therefore initial conditions are potentially a problem. Although the observations may not be independently and identically distributed, this problem may be partially alleviated by the law of large numbers due to the number of observations per household in the data set.
More than 79% of the households in the final data set make 30 or more choices. The mean household makes 259 portal choices and the median household makes 120 portal choices.
Together, these five data limitations mean that results should be extended to different geographic distributions, AOL users, and at work users with caution. Furthermore, the fourth and fifth limitations mean that understanding learning behavior is not possible.
I join this clickstream data set with two other data sets. The first is an advertising data set provided by J. Walter Thompson Company. This data set consists of all advertising spending by each of the portals used in this study on a monthly basis. The spending is determined by a thorough sampling of television, radio, newspaper, magazine, outdoor, and Internet advertising by each of the portals. The number of advertisements is then multiplied by the average cost of advertising in each medium (at the program level in television and the issue level in magazines). Since this data is not individual-specific, it will likely underestimate the impact of advertising. The methodology used in this paper, however, can easily be adapted to individual-specific advertising data.
I also constructed a data set of 'media mentions' for each of the relevant companies. If a company is mentioned on network television news (ABC, CBS, or NBC), in the Wall Street Journal, in the New York Times, or in USA Today on a given day or the day before then the media mentions variable is equal to one. Otherwise it is equal to zero. Unfortunately, I do not know which individuals were actually watching or reading which media. It is likely, however, that mentions in these media are highly correlated with mentions in other media such as local newspapers.
In the data set several dozen portals are observed to be chosen. For computational feasibility, I limit the number of portals to the eight with the most visits (in order): Yahoo, Microsoft Network (MSN), Netscape, Excite, AOL, Altavista, Iwon, and Lycos. These eight make up eighty percent of all visits and all sites with more than 2.5% of total visits. There was a natural break after Lycos because the ninth most visited portal, MyWay, is a site that is the default of several Internet Service Providers and is rarely chosen as anything but a start-up page. Go.com is not included because, although it is commonly ranked in the top five portals, a large percentage of those visits are to destination websites such as ESPN.com, Disney.com, and MrShowbiz.com. The Go.com portal page itself ranks tenth in total visits and ninth in unique visits. Qualitative results, however, do not change with the addition of more portals. Future work will explore methodologies that allow for the inclusion of a larger number of portals.

B. Questionnaire
There were several issues related to analyzing a clickstream data set that did not have obvious answers. I conducted an email-based survey of Internet search habits to help resolve these issues. Using surveys to inform data interpretation is relatively rare in economics. Helper (2000) asserts that economists should use more surveys and field research in order to better understand data. She emphasizes that this type of research "allows exploration of areas with little preexisting data or theory" (p. 228). Analysis of clickstream data certainly qualifies as one such area. Manski (2000) recommends questionnaires to elicit agents' preferences and expectations directly. Jaffe, Trajtenberg, and Fogarty (2000) use surveys to determine whether patent citations are a good proxy variable for communication. In other words they use a survey to determine how to interpret a data set. In this paper, I use a survey to determine how derive variables such as search success from raw clickstream data. Further details on the questionnaire are in Goldfarb (2000b) 1. Questionnaire methodology The survey was sent to each participant as an email attachment in Microsoft Word template format. In the accompanying email, I explained that I was a doctoral student in economics studying Internet habits. Respondents came from two groups. The first group, henceforth referred to as the 'spammed' group, consists of the 34 respondents to unsolicited email. The second group of respondents consisted of 23 'friends of friends'. After receiving a response rate of roughly three percent, I decided to augment my numbers by asking several friends and family members to forward the survey to their mailboxes. When there is sufficient data, I present results in this paper for the 34 'spammed' respondents and for the 57 in the total sample.
Clearly, this is a biased sample and cannot be used to conduct classical statistics. It can, however, be used to inform myths, suggest ideas, and suggest stories. The survey results are quite informative about individual surfing habits. By observing a biased sample of people, I can follow the search process more closely than I can with a broader sample. It is common practice in psychology and in experimental economics to draw candidates from undergraduate classes, and then to use this information to inform theory.
The survey itself asks respondents to search for driving directions, medical information, an MP3, and something of their own choosing. Respondents then answered several questions about the searches (see the Appendix for a copy of the full questionnaire). The search tasks were chosen to be diverse and to reflect common search activities. The survey also asks several questions about user Internet habits.

Questionnaire Results
Two of the issues addressed in the questionnaire are particularly important to this paper.
The first is determining which variables are relevant to an analysis of search engine competition (and hence which variables to construct and include in the study). The second is how to determine whether a given search fails. Other issues addressed include whether faster search is more desirable, and whether habits differ at the second search engine in a given search from those at the first.
There are many potential relevant variables for analysis. The survey asked which pages individuals bookmarked and what was each individual's starting page. Individuals rarely bookmarked portals, and those that were bookmarked were rarely used in the actual search part of the questionnaire. On the other hand, start pages were found to play an important role in site choice. The survey also asked respondents to give reasons why they preferred their favorite portal. The only specific portal feature mentioned was email. Other features such as shopping, Internet radio, games, and an online community were not mentioned. As a result of these findings, I include whether a portal is an individual's start page and whether that person has an email account at that site. I do not include other features or bookmarks.
Another important variable that the survey suggests should be included is the goal of search. Most respondents claimed to use more than one search engine because "Different search engines are better suited to different tasks". Links to relevant pages were also said to be important. I could derive data on whether a portal is linked to the next page visited.
I did not include goal of search in the final analysis because including it did not satisfy the Akaike information criterion or the Bayesian information criterion for goodness of fit.
Whether a search fails is an important factor for an individual's experience with a data set. Ideally each person would only conduct one task during each online session. Therefore if the researcher observes the individual visit a search engine followed by a visit to a destination website without searching again, then it would be reasonable to assume the search was successful. In this scenario, if the researcher observes the individual search again after going to the site then the search would appear to have been a failure. More than 45% of the respondents claim to either perform several tasks or have no specific task in mind when they go online, considerably complicating the definition of a failed search.
The group with no specific task in mind makes up only five percent of respondents (6% of spammed). Defining how they search and the reasons for it are beyond the scope of this survey. Much more important is controlling for the more than forty percent of respondents (also roughly 40% of spammed) who do several tasks when they go online. One way to do this is to compare the goals of searches that occur during a given session. If the goals are the same, it is more likely that they are part of the same search task. Also, the elapsed time between searches may be relevant as would the number of sites seen between the visits to search engines.
Thus, if people search twice for the same thing in a short period of time, it seems reasonable to assume that the first search was a failure and the second a success. This relies on one further assumption: that people do not go to the destination site from the portal by typing in the name of the site. They only use links on the search page. They may type in a destination site, but not from the portal. Only 5.8% of 155 searches (4.7% of 85 for spammed) were followed by the use of a non-portal site that was not the final destination. This means that using the above method, over ninety-four percent of searches labelled as successful would in fact have been successful. While this is not perfect, it seems to be a reasonable measure. Also, if a person goes directly from one search engine to another then the visit to the first site is likely a failure. Using the above criteria, I constructed a variable for whether each search failed.
The survey also showed that more experienced users search faster. This suggests that faster search is probably more desirable. Furthermore, the survey suggests that habits are different at the second search engine visited during a given search than at the first. Again, while the above information comes from a statistically biased sample, it does inform the researcher about analysis of clickstream data.

C. Data set Construction
I used the above information to construct several variables from the raw clickstream data. Instead of listing whether a portal has features, this variable proxies whether people actually use these features. In other words, if people use a link, it means they are using a feature at that site, rather than the search capabilities.
Search failure was constructed largely as described in section 3.2. If a household visited two portal sites in a row, and there was less than five minutes between visits, then the first search is considered a failure. Furthermore, if the household conducts a search and then searches again for the same goal (at the same site or at a different one) within five minutes of the first search then the search is considered a failure. While five minutes is an arbitrary number, extending it to ten minutes or shortening it to three minutes did not change the number of failures much. As with time spent, it is whether previous searches at a site failed that matters. Also as with time spent, more complicated functions of past failure do not yield qualitatively different results. For robustness, I also calculated a failed search variable that included searches that were not followed by other searches.
If a portal was the first visited in the search process, then f irsttry ijt = 1. If an individual has already searched and failed, then f irsttry ijt = 0.
This paper mimics Guadagni and Little's methodology for constructing their 'loyalty' variable almost exactly. In their paper, loyalty is considered to be a weighted average of past purchases of the brand, treated as dummy variables. Let portsame ijt = 1 if household i bought brand j as its previous purchase and zero otherwise.
Rather than estimate α by maximum likelihood which would significantly complicate the computational problem they calibrate α based on dummies for lags of length one to ten. In the present study, the value for alpha that minimizes the sum of the difference between the actual dummy coefficients and the loyalty function above was 0.7782. I also use portsame alone as a loyalty variable in the study. Note that this loyalty variable can be a result of either individual preferences for a given portal or from some kind of lock-in. In future work, I plan to separate out these effects of heterogeneity and state dependence. In a recent study, Abramson, Andrews, Currim, and Jones (2000) find this to be the best loyalty measure they tried.
In this study, I define the portsame ijt variable to depend on the previous portal visited of any kind, not just the previous of the eight portals used in this study. Therefore, if a household visits Yahoo then About.com and then Yahoo again, portsame ijt on the second visit to Yahoo is equal to zero, even though only two observations are included in the data set. This means that a household is not considered brand loyal if it went to a rival portal's website, even if that rival portal is not in the sample. If I only included the sample, the coefficient on the loyalty variable increases slightly but its significance falls slightly. Note that the initial conditions problem frequently encountered in this literature does not apply here due to the large number of observations per household.
How much time a household's previous visit to a portal took and whether that search failed are only observed when the household has visited that portal previously in the data set. Since not every household visits every portal, these variables are missing for a large number of observations. I therefore created a dummy variable for missing data. I also interact one minus the missing data variable with the view length of previous search and the failure of previous search variables. This overcomes the significant potential bias of assuming a value for the missing data or of ignoring it entirely. The missing data dummy has no economic interpretation.
Tables 3 and 4 contain descriptive statistics of the final data set. Table 5 presents the main results of the paper. Model (1)  with choice probability. This is consistent with the assumption that pages viewed proxy depth of search. In this regression, last view length is significant at the 99% confidence level.

A. Coefficients
Thus, controlling for depth, households prefer to spend less time at a portal. F irst try reveals that Netscape and MSN are preferred as first pages in a search than as later pages.
This makes sense as they are the pages that appear when using the search function in the Netscape Navigator and Microsoft Internet Explorer browsers. They are also often default start pages, but the results do not change in models (4) through (6) which control for the start page.
Model (4) adds the start page variable to model (2). The coefficient on this variable is very large compared to the other dummy variables and the likelihood improves more for this variable than for any others; however, the coefficient is not significantly different from zero as it has an extremely high standard error.
Model (5) is the same as model (4) except that is adds the interaction variable of media mentions and loyalty. Of particular interest here is the increase in the significance of media mentions. This suggests that being mentioned in the media has a larger effect for households that are less loyal to the brand.
Model (6) is the 'kitchen sink' regression in that it includes all of the variables in the study. The coefficients and their significance are similar to models (1) through (5).
Another interesting aspect of all of the models is that there is a clear brand preference for Yahoo over the others. Models (1) through (3) have negative coefficients for all brand dummies (Yahoo is the base). Models (4) through (6) also have negative dummies for Yahoo but others are often preferred on the first try. Adding the coefficients together, however, leaves a negative number meaning that Yahoo is prefered even on the first try.
The Akaike information criterion revealed that last view length squared, last number of pages squared, and media mentions * loyalty should be included. Other variables such as advertising squared and advertising * loyalty did not satisfy the Akaike information criterion. Note that including start page increases the likelihood a great deal, even though the effect is statistically insignificant. Any variables included in this study that satisfy the Akaike information criterion also satisfy the Bayesian information criterion.

B. Robustness of coefficients to small changes
Three different models are estimated in table 6. The first is the same as model (2) (8)) uses dummy variables for whether the portal is the same as that used the previous period and that used two periods before by that household. The third (model (9)) uses only the one period lag. The coefficients are still significantly positive in all cases. Note, however, that the explanatory power of these two methods is considerably less than that of Guadagni and Little's loyalty variable. In both of these models, the last view length variable is not significantly different from zero. In model (9), the coefficient becomes positive. If last number of pages is included then last view length does become significantly negative. Table 7 shows the results of conducting the above analysis with any seven of the eight portals. Note that, with a few exceptions, the coefficients change little. When either of the two largest advertisers are dropped (AOL or Yahoo), advertising becomes insignificantly negative. When Iwon, the site with the highest view length, is dropped, past view length becomes insignificantly positive. This is not because people are playing games at Iwon, since games are considered a destination and not part of the portal page.
Although there is little change in the coefficients, the χ 2 statistics at the bottom of table 7 show that the independence of irrelevant alternatives (IIA) assumption does not hold in this model. IIA implies that there is no correlation between the alternatives outside of the effects of known features. It is likely that Netscape and MSN are highly negatively correlated since they are based on different browsers. This method wrongly assumes that they are uncorrelated, bringing potential bias to some of the coefficients and weakening the assertions that can be made from policy analysis. It is essential that future work control for IIA.
These χ 2 statistics were calculated using a Hausman test following Hausman and McFadden (1984). The coefficients on the brand dummies were neither included in the Hausman test nor presented in table 7 although they were estimated for each model. While the coefficients themselves change little when a portal is dropped out of the estimation, the large sample size and corresponding low variances of the coefficients lead to a rejection of IIA. This is a considerable, though frequently encountered, problem in this type of analysis. Hausman and Wise (1978) and, more recently, Berry (1994) describe how accounting for heterogeneity alleviates this problem. This will be the subject of future work. Guadagni and Little (1983 p. 221), however, argue that "a more important test of the model will be its performance on a holdout sample of customers." This is conducted in the next section.
These robustness checks suggest that the effects of advertising and last view length on probability of choice may not be significantly different from zero. The coefficient on start page is also not significantly different from zero. The effect of media mentions is robust, but the impact is still not large. The other variables are all very important, particularly loyalty. The cause of the importance of the loyalty variable, however, is unknown; it could be due to either state dependence or unobserved household heterogeneity or both.

C. Predictive Ability
This section explores out of sample predictive power. Figure 1 shows the predicted and actual shares of MSN over the fourteen weeks from December 27 1999 to March 31 2000 for an outside sample of roughly 600 households. The predictions are done using both model (1) and model (6). In this case, both models match the actual shares rather closely. Figures   2 through 8 show the predicted and actual shares for the other portals. The fits are far from perfect. Both models under-predict Yahoo's share, both over-predict AOL, Altavista, Excite, Iwon, and Lycos, and both fit MSN and Netscape fairly well. With the exception of Iwon, model (6) fits better than model (1). For each of the brands, however, both models matched the general trends in the actual shares. Week-to-week changes in actual shares are captured by the predicted models.
Accounting for differences among households should help improve this predictive ability.
Preliminary work in accounting for household tastes has shown, for example, that some people have a substantial taste preference for Iwon, while others have a substantial dislike.
This bimodal distribution of tastes is averaged out in the model used here. Thus, actual preferences for Iwon are not well represented. The preliminary work suggests that the brands with a unimodal and narrow distribution of tastes across households are predicted better than are other firms in the model presented in this paper.
While not perfect, this model has significant predictive power and could be used to explore how policies in one market would work in another.

D. Market Response to Variable Changes
Tables 8 and 9 explore the market responses to variable changes in model (2) assuming no competitive response. Table 8 presents the elasticity of the model to slight changes in the variables at the variable means. five cents (about the revenue received from the five advertisements seen over typical two page views at a typical search engine), then it would be worth it for MSN to implement this change as long as it cost less than one hundred and fifty thousand dollars.
The advertising results are perhaps the most interesting. An increase in advertising by one dollar would bring six more visits to Altavista but twenty-six more to Yahoo. Therefore,

Conclusion
This study has provided a preliminary look at estimating demand for advertising-supported Internet websites based on clickstream data. The methodology provides a reasonable fit to the actual patterns in the data. It has reasonable predictive power and is informative about the potential impact of various policy changes.
This methodology has several weaknesses. The first, and most important, is that it does not take into account individual heterogeneity. This leads to a rejection of the Independence of Irrelevant Alternatives hypothesis as well as poor predictive ability for Iwon and Yahoo in particular. In future work with the data, I will estimate a model that accounts for this heterogeneity.
Another weakness in this methodology is that it does not allow for the market to grow. It predicts changes in share of a given population. It therefore ignores the impact of new users in a rapidly growing market and the effect of promotion on market size. The assumption that new users will have similar tastes to the current ones has some supporting evidence in that fact that market leaders change little over time in advertising-based Internet industries (Goldfarb 2000b), but this methodology is much better at exploring the demand of existing users rather than that of potential new users.
With respect policy implications, the study provides a framework for understanding policy effects. The simulations in section 4.3 show the impact of potential policy changes on market shares. While they do not take into account supply side reactions or individual heterogeneity, they do give better estimates of policy effects than currently exist. More detailed policy analysis can also be explored in this framework. For example, a portal could simulate a link to a commonly used site, say americangreetings.com. It could then determine the effect of this link on market share. The actual increase in share resulting from this change would be no more than the simulated level. It may be less because it may be that people who go to a given portal are also the kind of people who like the links it has. Thus the effects of the new link may be less than predicted. Because it does not account for individual heterogeneity, this model does not provide an effective framework for examining the effects of major industry changes such as bankruptcies, nor does it provide a way to look at the welfare impact of improved technology. In future work, I will match the heterogeneous demand model to a supply side model and estimate the effects of industry changes on demand and welfare.
The main purpose of this study was to show that demand for free online services can be estimated using methodologies that are common in both the economics and the marketing         -442,856 -425,651 -421,531 -386,956 -386,659 -386,581 *** significant at a 1% level in a two-tailed test ** significant at a 5% level in a two-tailed test * significant at a 10% level in a two-tailed test significant at a 10% level in a one-tailed test  Log likelihood -427,055 -456,840 -504,324 *** significant at a 1% level in a two-tailed test ** significant at a 5% level in a two-tailed test * significant at a 10% level in a two-tailed test