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

How Social Media Advertising and Repetitive Marketing Messages Affect the Online Purhasing Behavior?

In the past decade social media advertising has disrupted the marketing and advertising totally. As social media advertising platforms such as Facebook offer easy, effective and relatively cheap services, they have enabled and encouraged the rise of new kinds of companies operating solely online by tapping into the potential of easily reaching the audience and attracting them to their webstores. This has made it possible for up and coming companies with less well-known brands to attract customers and build up their brands. Naturally as the marketing field has been disrupted by the social media advertising, the traditional rules and guidelines of marketing need to be re-evaluated requiring academic research to understand how social media marketing and people’s behavior online differs from more traditional channels. Additionally, the ability to effectively personalize the marketing messages for different audience groups for example based on the previous engagement or other online behavior brings up another layer to the phenomenon. For the purpose of this study, the audience visiting the webstore of the case company is divided based on their previous brand engagement to three groups; fresh audience with no previous engagement, retargeted audience with some engagement for example on social media platforms or website visits and returning audience with previous webstore visits. Fresh and retargeted audience groups ended up to the webstore via Facebook advertisements while returning audience returned to the site without the need of extra marketing activities. With t-tests and ANOVA it was possible to establish differences in behavior between these groups and based on that, regression models were created to further understand the drivers affecting conversion rate and revenue per user. What comes to the reactions to the Facebook advertisements, people with previous brand engagement, i.e. retargeted audience was much more likely to enter the webstore by clicking the advertisement than fresh audience. Additionally, retargeted audience has higher conversion rate and higher revenue per user values as well. As previous research has also found, previous engagement with the brand is indeed the strongest indicator for purchase intention. In addition to that, returning audience i.e. the people who return to the website on their own have the highest conversion rates and revenue per user values out of the audience groups studied. It is likely that this can be explained with the stronger firm-consumer relationship, making this group the most loyal and profitable customers. For the fresh and retargeted audience groups, time spent on the website has positive affect on both conversion rate and revenue per user. So, it seems that when previous engagement with the brand is lower, clicking the Facebook advertisement and spending more time on the website builds up the firm-customer relationship and improves purchase intention

Growth hacking, digital marketing and startup metrics - a data driven approach

The main goal of the work project is to discuss the importance of analytics and data driven decisions in startup companies, since they are frequently presented as neglected components within startups. In order to understand their benefits for startups several tools were used: literature review, questionnaires, interviews and a Startup Company as a case study. This Startup allowed this work project to focus on analytics and metrics, more precisely on those related with marketing area. Therefore, this work was conducted to propose several suggestions that will allow startups to grow in terms of revenues and users without jeopardizing sustainable growth

Digital Marketing in the Business Environment

Promotion of products has become an increasingly important component in the new digital age, mostly thanks to digital marketing. The traditional form of marketing is lagging behind digital marketing, which offers users new opportunities like personalized messages or answers to a search query. There are several ways to advertise on the internet, and in this paper, ways and tools will be presented that allow digital advertising as well as their advantages and disadvantages. Specifically, search engine optimization, search engine marketing, display advertising, social networking marketing and e-mail marketing will be discussed. Also, the goal of the paper is to enable more efficient creation and implementation of similar contents in new business environments through an insight into internet advertising, social and business networks

Customer habits in a B2B context : impacts on cash flow level and volatility

Hábitos estão presentes em uma grande parte do dia-a-dia das pessoas. À medida que são repetidas ações com resultados satisfatórios em contextos estáveis, as respostas para ações futuras começam a ser ativadas automaticamente na memória de um indivíduo. Com o tempo, as decisões tornam-se menos impulsionadas por objetivos e intenções e, desta forma, um hábito é formado. Medidas empíricas de hábitos baseadas em dados de transações de clientes foram desenvolvidas pela área de marketing e vincularam comportamentos habituais de pessoas na hora da compra e o impacto financeiro nas empresas. Esta dissertação tem como objetivo analisar o impacto de comportamentos habituais no contexto B2B de transações entre fabricantes e varejistas. O responsável por efetuar uma compra em uma empresa pode comparar especificações, preços e avaliar os concorrentes antes de fazer um pedido. No entanto, é praticamente impossível avaliar todos os produtos sempre que for necessária uma compra para reabastecer estoques ou para solicitar um item vendido no catálogo por um vendedor dentro da loja. Portanto, espera-se que com o tempo, uma parte das transações que são realizadas começam a ser conduzidas por comportamentos habituais de alguém envolvido no processo de compra. Esta dissertação propõe medir os hábitos de compra e promoção em um banco de dados de transações e aplicar análises quantitativas para avaliar como os hábitos impactam os níveis de fluxo de caixa e a volatilidade dos mesmos. Uma análise posterior é proposta para comparar como os clientes habituais se relacionam com os clientes mais valiosos da empresa e uma simulação é proposta para analisar o impacto de uma eventual aquisição de clientes. Os resultados mostram que os hábitos mais fortes de compra aumentam os níveis de fluxo de caixa, mas também afetam positivamente a volatilidade do fluxo de caixa. Em contrapartida, os hábitos de promoção, com o passar do tempo, tendem a gerar fluxos de caixa menos voláteis que os hábitos de compra, mas com a desvantagem de diminuir os níveis dos mesmos.Habits are widespread in most of life. As people repeat actions with satisfactory outcomes in stable contexts, responses start to become automatically retrieved in memory. Over time decisions become less driven by goals and intentions, and therefore, a habitual behavior is formed. Empirical measures of habits based on customer transactions data were developed by marketing scholars and have linked habitual behaviors of people when purchasing and their impact on firms’ performance. This dissertation aims to analyze the impact of habitual behaviors in the context of business-to-business transactions with manufacturers and retailers. The responsible for buying in a firm may compare specifications, prices and assess competitors before making a purchase. However, it is unfeasible to evaluate all products every time it is required a purchase to replenish stocks or to order a sold item in a catalog by a sales employee. Therefore, it is expected that over time, a portion of repeat transactions start to be driven by habitual behaviors of someone involved in the process of buying. This dissertation proposes to measure the Purchase and Promotion Habits in a database of transactions and to apply quantitative analyzes to evaluate how habits affect cash flow levels and their volatility. A later analysis is proposed to compare how regular customers relate to the company's most valuable customers and a simulation is proposed to analyze the impact of eventual customer acquisition. The results show that stronger Purchase Habits increase cash flow levels, but also positively affect cash flow volatility. On the other hand, Promotion Habits, over time, tend to generate less volatile cash flows than Purchase Habits, but with the disadvantage of reducing their levels

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objectiveNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Applications of Multi-Touch Attribution Modelling

The Digital landscape has evolved vastly since the early 2000s in terms of analytical tools and tracking software. With the Rise of 4G to 5G, smartphones have become the norm when surfing through the web. New problems arise in terms of measuring business performance like Cross-Channel and Multi-Channel Attribution. Companies are selling more products and services on their Websites and marketplaces than ever before. Brands must become digital natives and translate all of their offline business into the internet. When Brands invest in multiple marketing channels and those channels mix up in the Customer Journey, new measurement problems arise. Based on the current standard methodology on web analytics, companies track their conversions (signups, subscriptions, orders) and assign each channel’s attribution using simple heuristics. In other words, simple decision models. It has been vastly studied that single-touch attribution does not perform well under complex business scenarios like those observed nowadays. Attribution modeling has been a hot topic in the last decade due to the rise of Machine Learning and data mining. Nowadays, there are two current trends. The problem can be analyzed from a Machine Learning standpoint, understanding that it looks like a Classification problem with a Binary Outcome (0/1). On the other hand, Shapley Values and Game theory also adapt efficiently to the question, where every player gets credit for contributing to conversions. Given that there are different state-of-the-art models which perform better than others and that multiple papers are trying to improve robustness, predictive accuracy, interpretability, this thesis will focus primarily on applications and interpretability of the model. Most of today’s Marketing Managers and teams find it extremely hard to use and apply these types of models due to the complexity of the topic and black-box models, which have little to no interpretability. The idea is to encourage more companies into the MTA landscape to test their models and optimize them specifically for their industry in this work. Additionally, to my knowledge, there is no research on Markov Chains applied to Subscription Business Models that are substantially different from E-Commerce Customer Journeys.Por motivos relacionados con los derechos de autor este documento solo puede ser consultado en la Biblioteca Di Tella. Para reservar una cita podés ponerte en contacto con [email protected]. Si sos el autor de esta tesis y querés autorizar su publicación en este repositorio, podés ponerte en contacto con [email protected]

Suljettujen online-mainosalustojen strategiat — tapaukset Google ja Facebook

This thesis studies closed ad platforms in the modern online advertising industry. The research in the field is still nascent and the concept of a closed ad platform doesn’t exist. The objective of the research was to discover the main factors determining the revenue of online advertising platforms and to understand why some publishers choose to establish their own closed ad platforms instead of selling their inventory for third-party ad platforms. The concept of a closed ad platform is defined leveraging the existing online advertising literature and the platform governance structure theory. Using the case study method, Google and Facebook were chosen as the cases as they have driven most of the innovation in the field and quickly gained significant market share. In total, 47 people were interviewed for this study, most of them working for advanced online advertisers. Based on the interviews, a microeconomic mathematic formula is created for modeling an ad platform’s net advertising revenue. The formula is used to identify the five main drivers of an ad platform’s revenue an each of them are studied in depth. The results suggest that the most important revenue drivers the ad platforms can affect are access to an active user base, the efficiency of ad serving and the comprehensiveness of measurement. Setting up a closed ad platform requires significant investments from a publisher and should be only done if it can improve the advertisers’ results. After it’s been established, a closed platform can leverage its position to collect user data and structured business data to optimize its performance further. The results provide a structured understanding of the main dynamics in the industry that can be used in decision-making and a basis for future research on closed ad platforms.Tämä diplomityö tutkii suljettuja mainosalustoja nykyaikaisella online-mainonta-alalla. Alan tutkimus on vielä aluillaan ja suljetun mainosalustan konseptia ei ole olemassa. Tämän tutkimuksen tavoitteena oli löytää online-mainosalustojen liikevaihdon määrittävät tekijät ja ymmärtää miksi jotkut julkaisijat valitsevat omien suljettujen mainosalustojen perustamisen mainospaikkojen kolmansien osapuolien mainosalustoille myymisen sijaan. Suljetun mainosalustan konsepti määritellään olemassaolevaa online- mainontakirjallisuutta ja alustojen hallintarakenneteoriaa hyödyntäen. Tapaustutkimusmenetelmää käyttäen, Google ja Facebook valittiin tapauksiksi, sillä ne ovat ajaneet eniten innovaatioita alalla ja nopeasti saavuttaneet merkittävän markkinaosuuden. Yhteensä 47 henkilöä haastateltiin tätä tutkimusta varten, useimmat heistä edistyneiden online-mainostajien työntekijöitä. Haastattelujen perusteella luodaan mikrotaloudellinen matemaattinen kaava mainosalustan nettoliikevaihdon mallintamiseksi. Kaavaa käytetään tunnistamaan mainosalustan liikevaihdon viisi pääkomponenttia, ja kuhunkin niistä perehdytään syvällisemmin. Tulokset viittaavat, että tärkeimmät liikevaihdon ajurit, joihin mainosalustat voivat vaikuttaa ovat pääsy aktiiviseen käyttäjäkantaan, mainosten näyttämisen tehokkuus ja mittaamisen kattavuus. Suljetun mainosalustan perustaminen vaatii merkittäviä investointeja julkaisijalta ja tulisi tehdä ainoastaan, jos sillä voidaan parantaa mainostajien tuloksia. Suljetun alustan perustamisen jälkeen sen positiota voidaan hyödyntää käyttäjädatan ja strukturoidun liiketoimintadatan keräämiseksi suorituskyvyn edelleen optimoimiseksi. Tulokset tarjoavat toimialan päädynamiikkojen ymmärryksen, jota voidaan käyttää päätöksenteossa sekä pohjana suljettujen mainosalustojen edelleen tutkimiseksi tulevaisuudessa

Two-sided user generated content platforms on the Internet : optimal strategies for growth

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2007.Includes bibliographical references (leaf 20).Two-sided platforms aggregating user generated content have become increasingly common on the Internet, as highlighted by the recent emergence of two knowledge markets connecting user submitted questions with user generated answers. An experiment with marketing messages was run on Google to determine which side of a knowledge market offered stronger benefits. Advertising performed poorly on both sides, but led to an unexpected finding: site content, once indexed by search engines, resulted in an order of magnitude more traffic and more user conversions than search engine advertising. This finding suggested that optimal growth strategies for user generated content properties on the Internet focus on maximizing content reach, increasing production of content on the site, and acquiring new content.James Kelm.M.B.A