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THE TERM WEB 2.0, COINED FOR A VARIETY OF RECENT WEB APPLICATIONS, RESOUNDS THROUGHOUT THE LAND AND FIRES ONLINE MARKETERS’ IMAGINATION IN MANY INDUSTRIES. WE EXAMINE EMPIRICALLY HOW FAR THOSE APPLICATIONS ARE USED BY RETAIL BANKING CUSTOMERS AND WHICH ROLE THEY PLAY IN THE RETAIL CUSTOMERS’ PURCHASE PROCESS.
Marketers increasingly use word of mouth to promote products or acquire new customers. But is such companystimulated WOM effective? Are customers who are referred by other customers really worth the effort? A recent study clearly says “yes”. In a study of almost 10,000 accounts at a German bank, the referred customers turned out to be 25 % more profi table than customers acquired by other means. Over a 33-month period, they generated higher profi t margins, were more loyal and showed a higher customer lifetime value. The difference in lifetime value between referred and non-referred customers was most pronounced among younger people and among retail (as opposed to private banking) customers. The reward of € 25 per acquired customer clearly paid off. Given the average difference in customer lifetime value of € 40, this amount implied a return on investment (ROI) of roughly 60 % over a six-year period. The encouraging results of this study, however, do not imply that “viral-for-hire” works in each and every case. Referral programs would be most beneficial for products and services that customers might not appreciate immediately. Products and services that imply some kind of risk would also benefit to a more than average degree from referrals because prospects are likely to feel more confi dent when a trusted person has positive experiences. Companies should consider carefully which prospects to target with referral programs and how large a referral fee to provide.
FINANCIAL SERVICE PROVIDERS FACE SERIOUS PROBLEMS IF MANY OF THEIR CUSTOMERS LEAVE QUICKLY BECAUSE SUCH CUSTOMERS HAVE LITTLE LONG-TERM VALUE. STILL, CURRENT REPORTING PRIMARILY FOCUSES ON CURRENT PROFITABILITY THAT REPRESENTS THE SHORT-TERM VALUE OF THE CUSTOMERS. THE LONG-TERM VALUE TYPICALLY RECEIVES LITTLE ATTENTION. CUSTOMER EQUITY REPORTING PRESENTS A MEANS TO FOCUS ON THE LONG-TERM VALUE OF THE COMPANY'S CUSTOMERS. IT AVOIDS THE RISK THAT SHORT-TERM PROFITS ARE INCREASED AT THE EXPENSE OF LONG-TERM VALUE CREATION AND ITS CENTRAL METRIC, CUSTOMER EQUITY, SERVES AS AN EARLY WARNING INDICATOR FOR RISK MANAGEMENT SYSTEMS THAT FOCUS ON CUSTOMER LOSS.
EMITTERS OF MUTUAL FUNDS AND OTHER FINANCIAL PRODUCTS LACK INFORMATION ABOUT THEIR CUSTOMERS. THEY MOSTLY OPERATE WITH A PRODUCT-CENTRIC MARKE TING CONCEPT. WITH INFORMATION ABOUT CUSTOMERS, THEY COULD SHIFT TOWARDS A MORE CUSTOMER-CENTRIC STRATEGY. HOWEVER, SUCH A STRATEGY DEMANDS INFOR MATION THAT IS HARDLY AVAILABLE. VIRTUAL PORTFOLIOS CAN BRIDGE THIS GAP AND PROVIDE EMITTERS OF FINANCIAL PRODUCTS WITH KNOWLEDGE ABOUT THEIR CUSTOMERS AND THEIR COMPETITORS. THIS ARTICLE ILLUSTRATES THE INSIGHTS THAT VIRTUAL PORTFOLIOS CAN PROVIDE TO EMITTERS OF A MUTUAL FUND.
ON THE INTERNET, SEARCH ENGINES INFLUENCE THE BEHAVIOR OF AN INCREASING PART OF CUSTOMERS. BANKS MAKE USE OF SEARCH ENGINES TO PROMOTE PRODUCTS BY USING KEYWORD AUCTIONS TO PURCHASE A PLACE OF THEIR ADVERTISEMENTS IN THE SPONSORED SEARCH LISTINGS. WE DESCRIBE HOW TO BID IN KEYWORD AUCTIONS AND HOW TO MEASURE THE SUCCESS OF SEARCHENGINE MARKETING.
IN TWO-SIDED MARKETS SUCH AS EXCHANGES, AN INTERMEDIARY BRINGS TOGETHER TWO DISTINCT CUSTOMER POPULATIONS, E.G., BUYERS AND SELLERS. THESE CUSTOMER POPULATIONS INTERACT VIA A PLATFORM PROVIDED BY THE INTERMEDIARY, AND TYPICALLY NETWORK EFFECTS ARE OBSERVABLE IN THESE MARKETS; IF THE NUMBER OF BUYERS IS HIGH, MORE SELLERS ARE ATTRACTED TO THE PLATFORM, AND VICE VERSA. IN SUCH MARKETS IT IS DIFFICULT TO MEASURE THE ECONOMIC SUCCESS OF IT INVESTMENTS. THIS ARTICLE PROPOSES A SOLUTION.
UNDER LAISSEZ-FAIRE REGULATION, REGULATORS CHOOSE NOT TO INTERFERE BECAUSE THEY SEEK TO STIMULATE INNOVATION AND PROTECT ENTERPRISES FROM THE COSTS IMPOSED BY REGULATORY COMPLIANCE. YET, EMPIRICAL EVIDENCE REGARDING THE ABILITY OF LAISSEZ-FAIRE REGULATION TO ENSURE CONSUMER PROTECTION IS LACKING. THIS ARTICLE TESTS EMPIRICALLY WHETHER THE CURRENT LAISSEZ-FAIRE REGULATION OF PRICE ADVERTISING CLAIMS ON THE MOST POPULAR REWARD-BASED CROWDFUNDING PLATFORM, KICKSTARTER, IS SUFFICIENT TO PROTECT CONSUMERS.
THE PROLIFERATION OF THE INTERNET HAS ENABLED PLATFORM INTERMEDIARIES TO CREATE TWO-SIDED MARKETS IN MANY INDUSTRIES. IN SUCH MARKETS, NETWORK EFFECTS OFTEN OCCUR WHICH CAN DIFFER FOR NEW AND EXISTING CUSTOMERS. THE AUTHORS DEVELOP AN INFLUX-OUTFLOW MODEL TO INVESTIGATE THE CONDITIONS UNDER WHICH THE ESTIMATION OF SAME-SIDE AND CROSS-SIDE NETWORK EFFECTS SHOULD DISTINGUISH BETWEEN ITS IMPACT ON THE NUMBER OF NEW CUSTOMERS (I.E., ACQUISITION) AND EXISTING CUSTOMERS (I.E., THEIR ACTIVITY).
IN THE PAST YEARS THE CUSTOMER FEEDBACK METRIC RECOMMENDATION INTENTION HAS GAINED IMPORTANCE, ESPECIALLY DUE TO THE WIDESPREAD CONCEPT NET PROMOTER SCORE (NPS). THE NPS CONCEPT IMPLIES A POSITIVE, NON-LINEAR RELATIONSHIP BETWEEN RECOMMENDATION INTENTION AND CUSTOMER VALUE. THIS ARTICLE INVESTIGATES THE RELATIONSHIP BETWEEN RECOMMENDATION INTENTION OF INDIVIDUAL CUSTOMERS AND THEIR VALUE FOR THE FIRM. THE RESULTS SHOW THAT RECOMMENDATION INTENTION SIGNIFICANTLY INCREASES CONTRIBUTION MARGIN BUT NEITHER RETENTION NOR CUSTOMER VALUE. THE METRIC SATISFACTION HAS A SIGNIFICANT, POSITIVE IMPACT ON CUSTOMER VALUE AND CAN THUS BE USED AS A LEADING INDICATOR. THEREFORE, THE RESULTS DO NOT CONFIRM THE SUPERIORITY OF THE NPS CONCEPT FOR CUSTOMER MANAGEMENT.
Customer equity reporting
(2014)
WHARTON SCHOOL OF BUSINESS AT UNIVERSITY OF PHILADELPHIA HAS JUSTLAUNCHED AN 8-WEEK ONLINE PROGRAM “STRATEGIC VALUE OF CUSTOMER RELATIONSHIPS – ONLINE” TAUGHT BY MARKETING PROFESSOR AND AUTHOR PETER FADER. HE INVITED PROFESSOR SKIERA, DIRECTOR OF THE E-FINANCE LAB, TO PHILADELPHIA TO LEARN ABOUT HIS THOUGHTS ON “CUSTOMER EQUITY REPORTING”. THIS ARTICLE SUMMARIZES SOME OF PROFESSOR FADER’S QUESTIONS AND PROFESSOR SKIERA’S REPLIES.
Economic value of data
(2020)
FIRMS COLLECT A LARGE AMOUNT OF DATA BY ENGAGING HEAVILY IN THE COLLECTION AND STORAGE OF ONLINE USER ACTIVITY VIA VARIOUS USER TRACKING TECHNOLOGIES. RECENT POLICY INITIATIVES AIM AT RESTRICTING THIS PRACTICE TO PROTECT CONSUMER PRIVACY. WE STUDY EMPIRICALLY THE CONSEQUENCES OF SUCH RESTRICTIONS FOR ONLINE PUBLISHERS, SUCH AS NEWS WEBSITES, BECAUSE THEY STRONGLY RELY ON REVENUES THAT ARE GENERATED BASED ON USER DATA. WE FIND A PRICE DECREASE OF CA. 30% FOR ONLINE ADS WHEN NO DATA FROM USER TRACKING IS AVAILABLE. THE POTENTIAL REVENUE LOSS COULD BE MORE THAN EUR 14 BILLION IN THE EU AND MORE THAN USD 27 BILLION IN THE US.
Data is considered the new oil of the economy, but privacy concerns limit their use, leading to a widespread sense that data analytics and privacy are contradictory. Yet such a view is too narrow, because firms can implement a wide range of methods that satisfy different degrees of privacy and still enable them to leverage varied data analytics methods. Therefore, the current study specifies different functions related to data analytics and privacy (i.e., data collection, storage, verification, analytics, and dissemination of insights), compares how these functions might be performed at different levels (consumer, intermediary, and firm), outlines how well different analytics methods address consumer privacy, and draws several conclusions, along with future research directions.
THE PRICE-TO-EARNINGS (P/E) RATIO IS ONE OF THE MOST IMPORTANT METRICS FOR VALUING FIRMS. UNFORTUNATELY, INTERPRETATIONS OF HIGH-GROWTH FIRMS’ P/E RATIOS CAN BE CHALLENGING, BECAUSE THEY FREQUENTLY EXHIBIT EITHER EXTREMELY HIGH OR NEGATIVE VALUES. WE SHOW THAT THE USE OF CUSTOMER METRICS ALLOWS FOR BETTER INTERPRETING THESE P/E RATIOS, THAT IMPROVEMENTS IN CUSTOMER METRICS HAVE NON-INTUITIVE AND SURPRISING EFFECTS ON THE P/E RATIO, AND THAT OUR NEW MODEL BETTER PREDICTS FUTURE P/E RATIOS THAN EXISTING MODELS.
DESPITE AMPLE EVIDENCE THAT CUSTOMERS EXHIBIT HIGHER DISCOUNT RATES THAN FIRMS, IT IS NOT CLEAR HOW DIFFERENCES IN DISCOUNT RATES AFFECT OPTIMAL PRICES, PROFITS, AND WELFARE OF COMPLEMENTARY PRODUCTS (WHICH COULD BE GOODS OR SERVICES). WE SHOW FOR COMPLEMENTARY PROUCTS THAT HIGHER DISCOUNT RATES OF CUSTOMERS DO NOT INCREASE PROFIT OR CONSUMER SURPLUS. FIRMS, INCLUDING BANKS, WOULD BE ADVISED TO SEEK TO REDUCE EXCESSIVE DISCOUNT RATES AMONG CONSUMERS.
UNDERSTANDING THE COMPETITIVE ENVIRONMENT FOR DIGITAL CONSUMER ATTENTION IS CRUCIAL FOR BANKS’ STRATEGIC ACTIONS. THEREFORE, BANKS NEED TO DETERMINE THE MARKET THEY COMPETE FOR, THEIR SUCCESS ON THIS MARKET, AND WHO THEY COMPETE WITH FOR CONSUMER ATTENTION. USING ORGANIC SEARCH ENGINE DATA, WE PROPOSE A NEW APPROACH TO (I) DEFINE THE DIGITAL MARKET, (II) IDENTIFY THE PLAYERS IN THE MARKET, (III) ESTIMATE THE DISTRIBUTION OF DIGITAL CONSUMER ATTENTION ACROSS BANKS, AND (IV) UNCOVER THE COMPETITIVE MARKET STRUCTURE FOR THE ONLINE RETAIL BANKING MARKET IN GERMANY.
STUDIES HAVE FOCUSED ON INNOVATIONS IN VARIOUS CONTEXTS BUT LARGELY EXCLUDED FINANCIAL INNOVATIONS, DESPITE THEIR NOTABLE IMPORTANCE. THIS STUDY ANALYZES THE TYPES OF FINANCIAL INNOVATIONS BY MAJOR BANKS AND THEIR PAYOFFS. THE RESULTS INDICATE THAT SECURITY AND CREDIT INSTRUMENTS CONSTITUTE THE MOST COMMON FINANCIAL INNOVATIONS. THE AVERAGE RETURNS TO A FINANCIAL INNOVATION ARE $146 MILLION. IN ADDITION, RADICALNESS AND FINANCIAL RISKINESS INCREASE THE RETURNS, WHEREAS COMPLEXITY DECREASES THEM.
Prior studies indicate the protective role of Ultraviolet-B (UVB) radiation in human health, mediated by vitamin D synthesis. In this observational study, we empirically outline a negative association of UVB radiation as measured by ultraviolet index (UVI) with the number of COVID-19 deaths. We apply a fixed-effect log-linear regression model to a panel dataset of 152 countries over 108 days (n = 6524). We use the cumulative number of COVID-19 deaths and case-fatality rate (CFR) as the main dependent variables and isolate the UVI effect from potential confounding factors. After controlling for time-constant and time-varying factors, we find that a permanent unit increase in UVI is associated with a 1.2 percentage points decline in daily growth rates of cumulative COVID-19 deaths [p < 0.01] and a 1.0 percentage points decline in the CFR daily growth rate [p < 0.05]. These results represent a significant percentage reduction in terms of daily growth rates of cumulative COVID-19 deaths (− 12%) and CFR (− 38%). We find a significant negative association between UVI and COVID-19 deaths, indicating evidence of the protective role of UVB in mitigating COVID-19 deaths. If confirmed via clinical studies, then the possibility of mitigating COVID-19 deaths via sensible sunlight exposure or vitamin D intervention would be very attractive.
MANY PEOPLE CLAIM THAT FIRMS NEED TO EMBRACE DIGITAL TECHNOLOGIES. YET, WE KNOW LITTLE ABOUT DIGITAL EMBRACEMENT, ITS ANTECEDENTS, AND ECONOMIC CONSEQUENCES. THIS ARTICLE PROPOSES A TEXTUAL APPROACH TO MEASURE DIGITAL EMBRACEMENT AND APPLIES IT IN AN EMPIRICAL STUDY COVERING 2,278 PUBLICLY LISTED U.S. FIRMS OVER 17 YEARS. THE RESULTS OUTLINE A VAST HETEROGENEITY IN FIRMS’ DIGITAL EMBRACEMENT IN AND ACROSS INDUSTRIES. REMARKABLY, A HIGHER DIGITAL EMBRACEMENT PREDICTS HIGHER FINANCIAL PERFORMANCE.
AS WE INCREASINGLY RELY ON SEARCH ENGINES AS AN IMPORTANT SOURCE OF INFORMATION TO SUPPORT OUR DECISIONS, SEARCH ENGINES BECAME AN IMPORTANT VENUE FOR FIRMS TO ATTRACT ATTENTION AND SECURE THE LONGEVITY OF THEIR OPERATIONS. THIS ARTICLE DISCUSSES THE RESULTS OF OUR EMPIRICAL STUDIES ON HOW TO CAPTURE A FIRM’S VISIBILITY IN ORGANIC SEARCH AND HOW IT AFFECTS ITS SHORT- AND LONG-TERM FINANCIAL PERFORMANCE.
Nowadays, firms lack information to derive the share of wallet, a vital metric that identifies how much additional spending a firm could capture from each customer. However, decoding Blockchain data enables observing all transactions of each wallet, respectively customer, on the Ethereum NFT market. To shed light on the share of wallet, we analyzed 22.7 million transactions from over 1.3 million customers across eight competing firms on the Ethereum NFT market.
Telecommunications companies traditionally offer several tariffs from which their customers can choose the tariff that best suits their preferences. Yet, customers sometimes make choices that are not optimal for them because they do not minimize their bill for a certain usage amount. We show in this paper that companies should be very concerned about choices in which customers pick tariffs that are too small for them because they lead to a significant increase in customers churn. In contrast, this is not the case if customers choose tariffs that are too big for them. The reason is that in particular flat-rates provide customers with the additional benefit that they guarantee a constant bill amount that consumption can be enjoyed more freely because all costs are already accounted for.
Customer loyalty is a critical measure for success, showing if a firm's product is received well by its customers. To understand its development over time, two fundamental questions must be answered: (I) How will current customers' loyalty develop, and (II) will new customers' loyalty differ from current customers' loyalty? The authors empirically answer these questions based on a data set including ~500 B2B web technologies with jointly ~325 million customers spanning over 24 years. They show that loyalty hardly develops and, if so, it rather decreases than increases. The loyalty of current customers rarely changes and, if so, rather increases than decreases. New customers are most likely less loyal than current customers. These results show that by failing to account for these underlying developments, stakeholders, in most cases, draw the wrong conclusions about product value measured via customer lifetime value.
In recent years, European regulators have debated restricting the time an online tracker can track a user to protect consumer privacy better. Despite the significance of these debates, there has been a noticeable absence of any comprehensive cost-benefit analysis. This article fills this gap on the cost side by suggesting an approach to estimate the economic consequences of lifetime restrictions on cookies for publishers. The empirical study on cookies of 54,127 users who received ∼128 million ad impressions over ∼2.5 years yields an average cookie lifetime of 279 days, with an average value of €2.52 per cookie. Only ∼13 % of all cookies increase their daily value over time, but their average value is about four times larger than the average value of all cookies. Restricting cookies’ lifetime to one year (two years) could potentially decrease their lifetime value by ∼25 % (∼19 %), which represents a potential decrease in the value of all cookies of ∼9 % (∼5%). Most cookies, however, would not be affected by lifetime restrictions of 12 or 24 months as 72 % (85 %) of the users delete their cookies within 12 (24) months. In light of the €10.60 billion cookie-based display ad revenue in Europe, such restrictions would endanger €904 million (€576 million) annually, equivalent to €2.08 (€1.33) per EU internet user. The article discusses these results' marketing strategy challenges and opportunities for advertisers and publishers.
Even as online advertising continues to grow, a central question remains: Who to target? Yet, advertisers know little about how to select from the hundreds of audience segments for targeting (and combinations thereof) for a profitable online advertising campaign. Utilizing insights from a field experiment on Facebook (Study 1), we develop a model that helps advertisers solve the cold-start problem of selecting audience segments for targeting. Our model enables advertisers to calculate the break-even performance of an audience segment to make a targeted ad campaign at least as profitable as an untargeted one. Advertisers can use this novel model to decide whether to test specific audience segments in their campaigns (e.g., in randomized controlled trials). We apply our model to data from the Spotify ad platform to study the profitability of different audience segments (Study 2). Approximately half of those audience segments require the click-through rate to double compared to an untargeted campaign, which is unrealistically high for most ad campaigns. Our model also shows that narrow segments require a lift that is likely not attainable, specifically when the data quality of these segments is poor. We confirm this theoretical finding in an empirical study (Study 3): A decrease in data quality due to Apple’s introduction of the App Tracking Transparency (ATT) framework more negatively affects the click-through rate of narrow (versus broad) audience segments.
Most event studies rely on cumulative abnormal returns, measured as percentage changes in stock prices, as their dependent variable. Stock price reflects the value of the operating business plus non-operating assets minus debt. Yet, many events, in particular in marketing, only influence the value of the operating business, but not non-operating assets and debt. For these cases, the authors argue that the cumulative abnormal return on the operating business, defined as the ratio between the cumulative abnormal return on stock price and the firm-specific leverage effect, is a more appropriate dependent variable. Ignoring the differences in firm-specific leverage effects inflates the impact of observations pertaining to firms with large debt and deflates those pertaining to firms with large non-operating assets. Observations of firms with high debt receive several times the weight attributed to firms with low debt. A simulation study and the reanalysis of three previously published marketing event studies shows that ignoring the firm-specific leverage effects influences an event study's results in unpredictable ways.
This research examines the impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value. Using proprietary data on annualized advertising expenditures for 1651 firms spanning seven years, we document that both display advertising and paid search advertising exhibit positive effects on firm performance (measured by sales) and firm value (measured by Tobin's q). Paid search advertising has a more positive effect on sales than offline advertising, consistent with paid search being closest to the actual purchase decision and having enhanced targeting abilities. Display advertising exhibits a relatively more positive effect on Tobin's q than offline advertising, consistent with its long-term effects. The findings suggest heterogeneous economic benefits across different types of advertising, with direct implications for managers in analyzing advertising effectiveness and external stakeholders in assessing firm performance.
This article uses information from two data sources, Compustat and Nexis Uni, and textual analysis to measure and validate the brand focus and customer focus of 109 U.S. listed retailers. The results from an analysis of their 853 earnings calls in 2010 and 2018 outline that on average, both foci increased over time. Although both foci vary substantially, brand focus varies more widely across retailers than their customer focus. Both foci are independent of each other. Specialty retailers have the highest brand focus, and internet & direct marketing retailers have the highest customer focus. A positive correlation exists between a retailer’s customer focus and its profitability, but not between a retailer’s brand focus and its profitability. The authors use the results to generate a research agenda that can direct future research in further systematically exploring firms’ brand and customer focus.
Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.
If service providers can identify reasons users are in favor of or against a service, they have insightful information that can help them understand user behavior and what they need to do to change such behavior. This article argues that the novel text-mining technique referred to as information-seeking argument mining (IS-AM) can identify these reasons. The empirical study applies IS-AM to news articles and reviews about electric scooter-sharing systems (i.e., a service enabling the short-term rentals of electric motorized scooters). Its results point to IS-AM as a promising technique to improve service; the data enable the authors to identify 40 reasons to use or not use electric scooter-sharing systems, as well as their importance to users. Furthermore, the results show that news articles are better data sources than reviews because they are longer and contain more arguments and, thus, reasons.
Ad blockers allow users to browse websites without viewing ads. Online news publishers that rely on advertising income tend to perceive users’ adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads—which users presumably dislike—may affect users’ online news consumption behavior in positive ways. Using 3.1 million visits from 79,856 registered users on a news website, this research finds that ad blocker adoption has robust positive effects on the quantity and variety of articles users consume. Specifically, ad blocker adoption increases the number of articles that users read by 21.0%–43.2%, and it increases the number of content categories that users consume by 13.4%–29.1%. These effects are stronger for less-experienced users of the website. The increase in news consumption stems from increases in repeat visits to the news website, rather than in the number of page impressions per visit. These postadoption visits tend to start from direct navigation to the news website, rather than from referral sources. The authors discuss how news publishers could benefit from these findings, including exploring revenue models that consider users’ desire to avoid ads.
A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to change. Embedded in such changes are firms’ trajectories, that is, the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes to market structure analysis by providing a forward-looking perspective on competition, revealing firms’ (re)positioning strategies and indicating strategy effectiveness. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap via extensive simulations and apply it empirically to study the trajectories of more than 1,000 publicly listed firms over 20 years. We find substantial changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
For many services, consumers can choose among a range of optional tariffs that differ in their access and usage prices. Recent studies indicate that tariff-specific preferences may lead consumers to choose a tariff that does not minimize their expected billing rate. This study analyzes how tariff-specific preferences influence the responsiveness of consumers’ usage and tariff choice to changes in price. We show that consumer heterogeneity in tariff-specific preferences leads to heterogeneity in their sensitivity to price changes. Specifically, consumers with tariff-specific preferences are less sensitive to price increases of their preferred tariff than other consumers. Our results provide an additional reason why firms should offer multiple tariffs rather than a uniform nonlinear pricing plan to extract maximum consumer surplus.
Digitale Technologien begünstigen den Einsatz einer dynamischen Preisgestaltung, also von Preisen, die für ein prinzipiell gleiches Produkt unangekündigt variieren. Dabei werden in der öffentlichen Diskussion unterschiedliche Ausgestaltungsformen dynamischer Preise oftmals vermischt, was eine sinnvolle Analyse der Vor- und Nachteile der dynamischen Preisgestaltung erschwert. Das Ziel des Beitrags ist die Darstellung der ökonomischen Grundlagen und die Diskussion sowie Klassifikation der Ausgestaltungsmöglichkeiten der dynamischen Preisgestaltung. Darüber hinaus erfolgt eine Bewertung der Vor- und Nachteile der dynamischen Preisgestaltung aus Käufer- und Verkäufersicht. Abschließend werden Implikationen für die betriebswirtschaftliche Forschung diskutiert.
Detailed feedback on exercises helps learners become proficient but is time-consuming for educators and, thus, hardly scalable. This manuscript evaluates how well Generative Artificial Intelligence (AI) provides automated feedback on complex multimodal exercises requiring coding, statistics, and economic reasoning. Besides providing this technology through an easily accessible web application, this article evaluates the technology’s performance by comparing the quantitative feedback (i.e., points achieved) from Generative AI models with human expert feedback for 4,349 solutions to marketing analytics exercises. The results show that automated feedback produced by Generative AI (GPT-4) provides almost unbiased evaluations while correlating highly with (r = 0.94) and deviating only 6 % from human evaluations. GPT-4 performs best among seven Generative AI models, albeit at the highest cost. Comparing the models’ performance with costs shows that GPT-4, Mistral Large, Claude 3 Opus, and Gemini 1.0 Pro dominate three other Generative AI models (Claude 3 Sonnet, GPT-3.5, and Gemini 1.5 Pro). Expert assessment of the qualitative feedback (i.e., the AI’s textual response) indicates that it is mostly correct, sufficient, and appropriate for learners. A survey of marketing analytics learners shows that they highly recommend the app and its Generative AI feedback. An advantage of the app is its subject-agnosticism—it does not require any subject- or exercise-specific training. Thus, it is immediately usable for new exercises in marketing analytics and other subjects.