Refine
Year of publication
Document Type
- Article (40)
- Part of Periodical (2)
- Book (1)
- Contribution to a Periodical (1)
Has Fulltext
- yes (44)
Is part of the Bibliography
- no (44)
Keywords
- Corporate Finance (2)
- Tax (2)
- pricing (2)
- Advertising (1)
- App Tracking Transparency Framework (1)
- Apple (1)
- Audience Segments (1)
- Automated Feedback (1)
- Brand focus (1)
- CCPA (1)
Institute
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.