FG ABWL, insbesondere Marketing
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Social media has become an integral part of Internet usage, with billions of users worldwide. Therefore, it is increasingly important for marketers to understand how to optimize one of the most important variables for success: Engagement. This study examines the relationship between user attention and engagement on social media, considering the effect of caption text length and emojis. Using an eye-tracking experiment, we measure user attention and engagement (likes, clicks, comments) for 161 social media posts. Our results confirm that attention affects engagement. Furthermore, we show that text length and emojis moderate this relationship. This study makes an important contribution to understanding one of the most important mechanisms for driving engagement.
In Zeiten von Inflation, stagnierenden Umsätzen und intensivem globalen Wettbewerb müssen Unternehmen sich von ihren Konkurrenten abheben, um Wettbewerbsvorteile zu erzielen . Ein interessanter Ansatz hierbei ist der neu entstandene Pick Your Price (PYP) Mechanismus, der ähnlich wie das bekannte Pay What You Want (PWYW) als partizipativer Preisbildungsmechanismus gilt und den Käufern die Möglichkeit g ibt den Preis massiv zu beeinflussen Käufer bevorzugen eine aktive Beteiligung am Preisbildungsprozess anstelle des passiven Akzeptierens fester Preise. Eine Teilnahme der Käufer an diesem Prozess erhöht zudem ihre Wahrnehmung von Fairness und Zufriedenheit. Allerdings stellt sich die Frage, welcher Preismechanismus Festpreis, PWYP oder PYP von Unternehmen genutzt werden sollte, um die Kaufabsicht und erwarteten Zahlungen zu optimieren. Um diese Frage zu beantworten, wurde ein Online Experiment durchgeführt und mittels ANOVA und Mediationsanalyse ausgewertet. Die se Studie vergl eicht d ie genannten Strategien anhand von Produkten mit unterschiedlichen Preisniveaus (Deodorant, Smartphone, Auto). Ziel war es, die Auswirkungen der Preismechanismen auf die Kaufabsicht, die erwarteten Zahlungen und die Wahrnehmung der Käufer in Bezug auf Kontrolle , A ufwand und Fairness, während des Kaufprozesses zu bewerten. Die Ergebnisse zeigen, dass PWYW die höchsten Werte für Preiskontrolle, kognitiven Aufwand und Kaufabsicht liefert. Die höchste Zahlungserwartung wurde hingegen für PYP ermittelt. Darüber hinaus zeigt diese Stud ie, dass die wahrgenommene Kontrolle die Kaufabsicht und die erwartete Zahlung am stärksten beeinflusst , gefolgt von
Aufwand und Fairness. Weiterhin werden Erkenntnisse für Forscher und Praktiker diskutiert.
Online influencer marketing has matured, and many managers now seek better performance with nano-influencers in seeding campaigns. Compared to macro-influencers, nano-influencers create more private (direct online or offline) brand messages that supposedly deliver more “persuasive punch”. But such private messages lack the usual online tracking per message, forcing managers to ask nano-influencers for self-reports, or to estimate sales at a campaign level if they want to assess their own decisions. Therefore, little is known about how to set up effective nano-influencer campaigns: does it pay to increase the public message share in nano-influencer activity, in order to benefit from private and public influencer activity? Or does it defeat its purpose, diminishing nano-influencers’ persuasiveness and total campaign results? This study analyses a unique campaign-level data set of various seeded marketing campaigns comprising the activity of 700,000 nano-influencers. We model the impact of nano-influencers’ activity on incremental sales and return-on-investment (ROI) at the campaign level. To answer our questions, we include effects of public message share and optimal public message share and their interaction with activity; instrumenting both for identification, and controlling for campaign, marketing, and brand context. We find conditional optimal public message shares for both sales and ROI. The average campaign benefits most when increasing influencer activity (through design or incentives) at public message shares between 4% and 5%. This shows the importance of striking the right balance between nano-influencers’ online and offline activity, and suggests a similar balance for combining nano- and macro-influencers in future campaign formats.
Social media is the staple of Internet consumption. There are currently 4.8 billion social media users worldwide. This means that almost every Internet user (5.18 billion) also consumes social media (Statista1, 2023). The enormous importance for the advertising market is underlined by the considerable advertising expenditure, which is expected to reach $ 207 billion in 2023 (Statista2, 2023). By comparison, traditional TV advertising is expected to reach $140 billion in 2023 (Statista3, 2023). However, social media is not only the ideal channel for marketers to advertise, but also to engage with their customers and strengthen their relationship with them (Lim & Rasul, 2022), as it has a positive impact on firm performance and word of mouth (de Oliveira Santini et al., 2020; Yost, Zhang, and Qi, 2021). As a result, companies want to know how to make their content, whether advertising or general, highly engaging. This dissertation aims to contribute to the following research question: “How can content engagement be increased, and what are the drivers of content engagement?"
Thus, the first objective of this work is to provide an overview of the basic decision-making process of user engagement in social media. Part I of this dissertation shows that previous research has shed light on the tools used to create engaging images and videos, referred to as main content (Berger & Milkman, 2012; Reichstein & Brusch, 2019). However, less light has been shed on an important aspect that also strongly influences engagement: the factors surrounding the content, referred to in this paper as framework conditions. One example of framework conditions is the text that accompanies a post, known as the caption. The second objective of this dissertation, in Part II and Part III, is therefore to examine the effects of framework conditions, such as the text length of a caption, on engagement. Table 0.1 gives an overview of the three manuscripts used in this dissertation.
Part I of this paper builds on the publication by Reichstein and Brusch (2019). This part provides a literature review on viral marketing, specifically an overview of the factors that influence content engagement. Viral marketing is a buzzword that represents the ideal of creating content that spreads like a virus. The section provides a definition of viral marketing and classifies the literature to date based on a hypothetical model. The model shows the decision-making process of individuals in social media, from receiving a content to making a decision to engage.
7
Part II builds on the published work of Reichstein, Brusch, and zu Ummeln (2021). An empirical study examines the effect of four framework factors (persons interested, title length, image, relationship information) on engagement and interest in Facebook events. The study is based on a survey that is analyzed using a statistical comparison of means. Additionally, the effect of emotions on engagement and interest is tested using correlation analysis. The results provide initial evidence that framework conditions influence engagement and interest. Furthermore, the strong effect of emotions on engagement, known from the literature (Berger & Milkman, 2012), is confirmed.
Part III is based on the unpublished manuscript by Reichstein, Brusch, Dost, and Brusch (2023). This part examines the effect of the caption, which can be considered as the framework of a social media post, on engagement. The focal variable studied is the text length of the caption. In addition, the effect of emojis and hashtags on engagement is examined. Two experiments and field data from an onminchannel retailer and a travel influencer are used. The results show that the relationship between text length and engagement follows an inverted U-shape, and thus the optimal text length maximizes engagement. The effect is surprisingly strong. Estimates for the given datasets show a potential increase in likes of up to 25% per post on average by optimizing text length.
The structure of this cumulative dissertation is as follows. The main part consists of the three papers (Part I, Part II, Part III). Each of them starts with a motivational part to clarify the contribution and the classification for the dissertation. It should be noted that the previously published papers (Part I and Part II) have been partially adjusted in language to unify the dissertation. At the end, there is a conclusion section that summarizes the findings of the paper.
Online influencer marketing has matured, and many managers now seek better performance with nano-influencers in seeding campaigns. Compared to macro-influencers, nano-influencers create more private (direct online or offline) brand messages that supposedly deliver more “persuasive punch”. But such private messages lack the usual online tracking per message, forcing managers to ask nano-influencers for self-reports, or to estimate sales at a campaign level if they want to assess their own decisions. Therefore, little is known about how to set up effective nano-influencer campaigns: does it pay to increase the public message share in nano-influencer activity, in order to benefit from private and public influencer activity? Or does it defeat its purpose, diminishing nano-influencers’ persuasiveness and total campaign results? This study analyses a unique campaign-level data set of various seeded marketing campaigns comprising the activity of 700,000 nano-influencers. We model the impact of nano-influencers’ activity on incremental
sales and return-on-investment (ROI) at the campaign
level. To answer our questions, we include effects of public
message share and optimal public message share and their
interaction with activity; instrumenting both for identification,
and controlling for campaign, marketing, and brand context.
We find conditional optimal public message shares for both
sales and ROI. The average campaign benefits most when
increasing influencer activity (through design or incentives)
at public message shares between 4% and 5%. This shows
the importance of striking the right balance between nanoinfluencers’ online and offline activity, and suggests a similar
balance for combining nano- and macro-influencers in
future campaign formats
“A picture is worth a thousand words”: Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images’ diversity either a SVM or a CNN provides the best basis for preference prediction.
Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). We introduce empirical dynamic models (EDM) to management science and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying relationships between variables (i.e. in the Granger sense), predicting variable evolution (i.e. in the Machine Learning sense), and attributing marginal effects from one variable on another (i.e. in the Neyman-Rubin sense). We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, we confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels.
Social media managers are constantly seeking ways to maximize engagement with their content. While prior research has shed light on strategies for creating engaging content, the impact of captions on engagement has been under-explored. Our study focuses specifically on the relationship between caption length and engagement in social media posts. Contrary to previous literature, we find that the relationship between caption length and engagement follows an inverted U-shape. Using field data from an omnichannel retailer (Facebook) and a travel influencer (Instagram), we determine the optimal caption length for maximizing engagement and present surprising results. Our results are robust for different model and identification specifications, indicating that captions are often too short, rather than too long, and that optimizing caption length in our case can increase engagement by up to 25%. These findings have important implications for social media managers and influencers seeking to enhance the impact of their posts.
Location is an impactful but irrevocable driver of retail store performance. Unless retailers rely on their gut feelings for finding high potential locations, they have to invest in extensive location research, calibrating performance models on expensive rich data (e.g., income or education of households in each prospective trading area). To prevent “the death of the high street”, also public administrators care for location potentials. This research proposes a parsimonious new model for location potentials, drawing from emerging urban scaling literature outside of marketing. We show that a measure of the local urban scale explains stores’ sales, local competitive intensity, and defining aspects of store lifecycles (managers’ location choice, sales ramp-up to a steady state after opening, store closure). We demonstrate these capabilities of the scaling approach using six datasets, including data from two retail chains (grocery and variety stores), public data, map data, and an experiment with retail managers. Our parsimonious model compares well to more complex multivariate benchmarks and remains more robust across modeling choices. We put forth a scale measure that can be cheaply obtained from map data, offering accessible applications for retail and public policy managers (e.g., “heat maps” across all potential locations in a city) and to marketing research in general (e.g., as input or control variable for geo or mobile marketing).
Empirical Dynamic Modelling for Exploring Complex Time Series in Management and Marketing Research
(2022)
Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). The authors introduce empirical dynamic models (EDM) to management and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying, predicting and attributing relationships between variables. We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, the authors confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels.