Drei Studien zu Analyse und Management von Online-Konsumentenverhalten

Over the last two decades, the Internet has fundamentally changed the ways firms and consumers interact. The ongoing evolution of the Internet-enabled market environment entails new challenges for marketing research and practice, including the emergence of innovative business models, a proliferation of marketing channels, and an unknown wealth of data. This dissertation addresses these issues in three individual essays. Study 1 focuses on business models offering services for free, which have become increasingly prevalent in the online sector. Offering services for free raises new questions for service providers as well as marketing researchers: How do customers of free e-services contribute value without paying? What are the nature and dynamics of nonmonetary value contributions by nonpaying customers? Based on a literature review and depth interviews with senior executives of free e-service providers, Study 1 presents a comprehensive overview of nonmonetary value contributions in the free e-service sector, including not only word of mouth, co-production, and network effects but also attention and data as two new dimensions, which have been disregarded in marketing research. By putting their findings in the context of existing literature on customer value and customer engagement, the authors do not only shed light on the complex processes of value creation in the emerging e-service industry but also advance marketing and service research in general. Studies 2 and 3 investigate the analysis of online multichannel consumer behavior in times of big data. Firms can choose from a plethora of channels to reach consumers on the Internet, such that consumers often use a number of different channels along the customer journey. While the unprecedented availability of individual-level data enables new insights into multichannel consumer behavior, it also makes high demands on the efficiency and scalability of research approaches. Study 2 addresses the challenge of attributing credit to different channels along the customer journey. Because advertisers often do not know to what degree each channel actually contributes to their marketing success, this attribution challenge is of great managerial interest, yet academic approaches to it have not found wide application in practice. To increase practical acceptance, Study 2 introduces a graph-based framework to analyze multichannel online customer path data as first- and higher-order Markov walks. According to a comprehensive set of criteria for attribution models, embracing both scientific rigor and practical applicability, four model variations are evaluated on four, large, real-world data sets from different industries. Results indicate substantial differences to existing heuristics such as “last click wins” and demonstrate that insights into channel effectiveness cannot be generalized from single data sets. The proposed framework offers support to practitioners by facilitating objective budget allocation and improving team decisions and allows for future applications such as real-time bidding. Study 3 investigates how channel usage along the customer journey facilitates inferences on underlying purchase decision processes. To handle increasing complexity and sparse data in online multichannel environments, the author presents a new categorization of online channels and tests the approach on two large clickstream data sets using a proportional hazard model with time-varying covariates. By categorizing channels along the dimensions of contact origin and branded versus generic usage, Study 3 finds meaningful interaction effects between contacts across channel types, corresponding to the theory of choice sets. Including interactions based on the proposed categorization significantly improves model fit and outperforms alternative specifications. The results will help retailers gain a better understanding of customers’ decision-making progress in an online multichannel environment and help them develop individualized targeting approaches for real-time bidding. Using a variety of methods including qualitative interviews, Markov graphs, and survival models, this dissertation does not only advance knowledge on analyzing and managing online consumer behavior but also adds new perspectives to marketing and service research in general.Das Internet hat die Interaktion zwischen Unternehmen und Kunden grundlegend verändert. Die Etablierung eines interfähigen Marktumfelds bringt neuartige Herausforderungen für Marketingforschung und -praxis mit sich. Dazu zählt die Entstehung von innovativen Geschäftsmodellen ebenso wie eine Vervielfachung der verfügbaren Marketingkanäle und eine bislang unbekannte Fülle an Daten. Die vorliegende Dissertation untersucht diese Herausforderungen in drei unabhängigen Studien

CUSTOMER JOURNEYS ON ONLINE PURCHASE: SEARCH ENGINE, SOCIAL MEDIA, AND THIRD-PARTY ADVERTISING

As the technologies and better practices become broadly available, companies are moving more quickly from a single-click or search-only model toward greater sophisticated models of informing and influencing the customer online shopping journeys. This study scrutinizes the predictive relationship between three referral channels, search engine, social medial, and third-party advertising, and online consumer search and purchase. The results derived from vector autoregressive models suggest that the three channels have differential predictive relationship with sale measures. Such differential relationship is even more pronounced for the long-term, accumulative effects. The predictive power of the three channels is also considerably different in referring customers among competing online shopping websites. This study offers new insights for IT and marketing practitioners in respect to how different channels perform in order to optimize the media mix and overall performance

GENERATING CONSUMER INSIGHTS FROM BIG DATA CLICKSTREAM INFORMATION AND THE LINK WITH TRANSACTION-RELATED SHOPPING BEHAVIOR

E-Commerce firms collect enormous amounts of information in their databases. Yet, only a fraction is used to improve business processes and decision-making, while many useful sources often remain underexplored. Therefore, we propose a new and interdisciplinary method to identify goals of consumers and develop an online shopping typology. We use k-means clustering and non-parametric analysis of variance tests to categorize search patterns as Buying, Searching, Browsing or Bouncing. Adding to purchase decision-making theory we propose that the use of off-site clickstream data—the sequence of consumers’ advertising channel clicks to a firm’s website—can significantly enhance the understand-ing of shopping motivation and transaction-related behavior, even before entering the website. To run our consumer data analytics we use a unique and extensive dataset from a large European apparel company with over 80 million clicks covering 11 online advertising channels. Our results show that consumers with higher goal-direction have significantly higher purchase propensities, and against our expectations - consumers with higher levels of shopping involvement show higher return rates. Our conceptual approach and insights contribute to theory and practice alike such that it may help to improve real-time decision-making in marketing analytics to substantially enhance the customer experience online

Multi-Click Attribution in Sponsored Search Advertising: An Empirical Study in Hospitality Industry

Sponsored search advertising has become a dominant form of advertising for many firms in the hospitality vertical, with Priceline and Expedia each spending in excess of US$2 billion in online advertising in 2015. Given the competition in online advertising, it has become essential for advertisers to know how effectively to allocate financial resources to keywords. Central to budget allocation for keywords is an attribution of revenue (from converted ads) to the keywords generating consumer interest. Conventional wisdom suggests several ways to attribute revenues in the sponsored search advertising domain (e.g., last-click, first & last-click, or evenly distributed approach). We develop a multi-click attribution methodology using a unique multi-advertiser data set, which includes full advertiser and consumer-level click and purchase information. We add to the literature by developing a two-stage multi-click attribution methodology with a specific focus on sponsored search advertising in the hospitality industry with which we develop a parametric approach to calculate the value function from each stage of the estimation process. Given our multi-advertiser data set, we are able to illustrate the inefficiency of single-click attribution approaches, which undervalue assist clicks while overvaluing converted clicks

Internet-mainonnan tehokkuuden arviointi attribuutiomallinnuksen avulla

The importance for data-driven planning in online advertising has become a significant factor for marketers. Advancements in data collection technologies have provided marketers the prerequisites for thorough analyses of the impacts of online marketing activities and most often attribution models are used to evaluate the performance. An attribution model defines the contribution of advertising channels in inducing conversions among customers i.e. purchase decisions. This Thesis proposes a framework for online advertising performance analysis and budget optimization using such techniques. The empirical analysis is conducted with clickstream data collected across multiple websites using cookies. We use binary logistic regression model to classify customers to converters and to non-converters. To evaluate the cost performance of a channel, we present a metric that is based on the expected cost of conversions. The logistic regression model is estimated with and without bootstrap aggregation. The coefficients are averaged over 100 iterations and the posterior distribution of conversions is ensured in training samples. The results suggest that the probability of conversion is highest at the first banner impression. Moreover, the search engines are significantly more efficient in inducing conversions than banners and direct traffic, but banner impressions increase the traffic of other channels. Last, the joint effects of advertisements were found beneficial. While the research objectives of this Thesis were achieved, further research is required to improve the results of the proposed framework. Nevertheless, this study provides solid results for online marketing planners and means to optimize the online marketing activities in terms of budget allocation.Käyttäjätason Internet-käyttäytymistiedon merkitys on kasvanut Internet-mainonnan suunnittelussa. Kehittyneet tiedonkeruutekniikat mahdollistavat Internet-mainonnan vaikutusten yksilötason analysoinnin attribuutiomallinnuksella. Attribuutiomalli kuvaa, miten eri mainoskanavat ovat vaikuttaneet käyttäjän ostopäätökseen eli käyttäjän konversioon. Tässä tutkimuksessa esitetään attribuutiomallinnukseen perustuva viitekehys Internet-mainonnan tehokkuuden analysointia ja budjetin optimointia varten. Työn empiirinen tarkastelu tehdään käyttäjätason internetkäyttäytymistiedon perusteella. Analysoitu aineisto on kerätty Internet-sivuilta evästeiden avulla. Kuluttajien ostokäyttäytymistä mallinnetaan binäärisellä logistisella regressiomallilla. Mainoskanavien kustannustehokkuuden mittaamiseen työssä esitetään metriikka, joka kuvaa sitä odotusarvoista kustannusta, millä käyttäjä kussakin kanavassa konvertoituu. Tulosten perusteella käyttäjän todennäköisyys konvertoitua on suurimmillaan ensimmäisen bannerihavainnon jälkeen. Samoin näiden valossa hakukone on tehokas konvertoimaan käyttäjiä. Lisäksi havaittiin, että bannerimainokset vaikuttavat muiden kanavien kävijämääriin, ja useimmiten mainoskanavien yhteisvaikutukset lisäävät käyttäjän konvertoitumis-todennäköisyyttä. Tutkimukselle asetut tavoitteet saavutettiin. Tutkimuksessa havaittiin, että markkinointikanavien välisten suhteiden parempi ymmärtäminen vaatii lisätutkimusta. Tutkimuksessa saatujen tulosten avulla Internet-mainonnan suunnittelijat pystyvät tehostamaan markkinointitoimenpiteitä ja markkinointibudjetin käyttöä

FIRMS? MARKETING STRATEGIES AND CONSUMER RESPONSES IN PLATFORM-BASED E-COMMERCE MARKETS

Ph.DDOCTOR OF PHILOSOPH

Multi-touch attribution in the mobile gaming industry

User acquisition spend is a big investment for mobile gaming companies. Because of the large scale, even small improvements in how this spend is allocated can provide big returns. To allocate advertising spend well; it is important that the credit of a conversion be attributed as accurately as possible. The current attribution model - standard to the industry - is a last-touch attribution model, which attributes 100% of the credit to the last touch-point. However, before a user installs a game they might see ads from multiple channels that might all affect the user’s propensity to install. With the last-touch attribution model, the uplift of these ads is not observed which skews the returns on advertising spent for different channels. This study looks at how install probability develops as impressions per user increase, how long the effect of an ad lasts and attempts to find better attribution models that attribute credit better than the last-touch model. Three multi-touch attribution models are proposed; two based on the Shapley value and one based on the ad effect time decay of different channels. The data for this study comes from a mobile gaming company and consists of impressions seen by both installed and non-installed users as well as impression channels, impression time and install time. The data was collected during a 38-day period and has data from 44,719,217 users who were divided into a training set and a test set with a 70%/30% split. The test set is used to validate the proposed models against the last-touch attribution model by using the models trained on the training set to generate predictions on install probability for user paths in the test data set. The study finds that the ad effect of all channels declines very quickly after the first day and is almost zero at seven days after the impression. The study also attempts to find the correlation between install probability and the amount of impressions a user has seen. Regarding this objective, the study is inconclusive. This correlation behaves very differently between different channels and because the amount of impressions per users could not be controlled for, it is difficult to deduce causation. Out of the three proposed attribution models, only one is able to outperform the last-touch model when it comes to predicting install probabilities from the training set’s paths. The model that outperformed is a Shapley value based model that considers the times of impressions for each path when calculating credit attribution. Finally, the study finds that only 9.5% of observed installs had impressions from more than one channel during a seven-day attribution window. This combined with the difficulty of validating attribution models based on return on advertising spend means that developing a multi-touch attribution model probably is not a very low hanging fruit for performance marketers. What would be worth looking into would be to test optimizing the frequency of ads shown to users

Keyword Segmentation, Campaign Organization, and Budget Allocation in Sponsored Search Advertising

Sponsored search advertising, where search providers allow advertisers to participate in a real-time auction and compete for ad slots on search engine results pages (SERPs), is currently one of the most popular advertising channels by marketers. Some domains such as Amazon.com allocate in millions of dollars a month to their sponsored search campaigns. Considering the amount of money allocated to sponsored search as well as the dynamic nature of keyword advertising process, the campaign budget planning decision is a non-trivial task for advertisers. Budget constrained advertisers must consider a number of factors when deciding how to organize campaigns, how much budget to allocate to them, and which keywords to bid on. Specifically, they must decide how to spend budget across planning horizons, markets, campaigns, and ad groups. In this thesis, I develop a simulation model that integrates the issues of keyword segmentation, campaign organization, and budget allocation in order to characterize different budget allocation strategies and understand their implications on search advertising performance. Using the buying funnel model as the basis of keyword segmentation and campaign organization, I examine several budget allocation strategies (i.e., search Volume-based, Cost-based, and Clicks-based) and evaluate their performance implications for firms that may pursue different marketing objectives based on industry and or product/service offerings. I evaluate the simulation model using four fortune 500 companies as cases and their keyword advertising data obtained from Spyfu.com. The results and statistical analysis shows significant improvements in budget utilization using the above-mentioned allocation strategies over a Baseline strategy commonly used in practice. The study offers a unique insight into the budget allocation problem in sponsored search advertising by leveraging a theoretical framework for keyword segmentation, campaign management, and performance evaluation. It also provides insights for advertiser on operational issues such as keyword categorization and campaign organization and prioritization for improved performance. The proposed simulation model also contributes a valid experimental environment to test further decision scenarios, theoretical frameworks, and campaign allocation strategies in sponsored search advertising

Lookalike Targeting on Others\u27 Journeys: Brand Versus Performance Marketing

Lookalike targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we find that seeding on others’ journey stage can be effective in new customer acquisition; despite the cold start nature of customer acquisition using Lookalike audiences, third parties can indeed identify factors unobserved to the advertiser that move individuals along the journey and can be correlated with the lookalikes. Further, while journey-based seeding adds no incremental value for brand marketing (click-through), seeding on more downstream stages improves performance marketing (donation) outcomes. Second, we evaluate audience expansion strategies by lowering match ranks between the seed and lookalikes to increase acquisition reach. The drop in effectiveness with lower match rank range is much greater for performance marketing than for brand marketing. Performance marketers can alleviate the problem by making the ad targeting explicit, and thus increase perceived relevance; however, it has no incremental impact for higher match lookalikes. Increasing perceived targeting relevance makes acquisition cost comparable for both high and low match ranks