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Technology enables a firm to produce a granular record of every touchpoint consumers make in their online purchase journey before they convert at the firm's website. However, firms still depend on aggregate measures to guide their marketing investments in multiple online channels (e.g., display, paid search, referral, e-mail). This article introduces a methodology to attribute the incremental value of each marketing channel in an online environment using individual-level data of customers' touches. The authors propose a measurement model to analyze customers' (1) consideration of online channels, (2) visits through these channels over time, and (3) subsequent purchases at the website to estimate the carryover and spillover effects of prior touches at both the visit and purchase stages. The authors use the estimated carryover and spillover effects to attribute the conversion credit to different channels and find that these channels' relative contributions are significantly different from those found by other currently used metrics. A field study validates the proposed model's ability to estimate the incremental impact of a channel on conversions. In targeting customers with different patterns of touches in their purchase funnel, these estimates help identify cases in which retargeting strategies may actually decrease conversion probabilities.

Firms use different attribution strategies such as last-click or first-click attribution to assign conversion credits to search keywords that appear in their consumers’ paths to purchase. These attributed credits impact a firm’s future bidding and budget allocations among keywords and, in turn, determine the overall return-on-investment of search campaigns. In this paper, we model the relationship among the advertiser’s bidding decision for keywords, the search engine’s ranking decision for these keywords, and the consumer’s click-through rate and conversion rate on each keyword, and analyze the impact of the attribution strategy on the overall return-on-investment of paid search advertising. We estimate our simultaneous equations model using a six-month panel data of several hundred keywords from an online jewelry retailer. The data comprises a quasi-experiment as the firm changed attribution strategy from last-click to first-click attribution halfway through the data window. Our results show that returns for keyword investments vary significantly under the different attribution strategies. For the focal firm, first-click attribution leads to lower revenue returns and a more pronounced decrease for more specific keywords. Our policy simulation exercise shows how the firm can increase its overall returns by better attributing the real contribution of keywords. We discuss how an appropriate attribution strategy can help firms to better target customers and lower acquisition costs in the context of paid search advertising. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0987 .

Marketers of digital content such as books, news, video, music, and mobile games often provide free samples of the content for consumers to try out before buying the product or signing up for subscription. Similarly, firms selling software (such as software as a service), and cloud-based services may provide free limited-version products or a free-trial period for the service. In this article, the authors focus on how firms should design such free samples to maximize their revenue. They examine in an analytical setting how quality and other design parameters of the sample affect profit generated by the product or service. They then test the normative implications in the application context of a book publisher that provides free samples for the books it sells online. Using a field experiment, the authors vary the design parameters of the sample and, based on the demand estimates, provide recommendations for the firm on the optimal design of the sample. They find that, rather than being substitutes, free samples of the entire content can be very effective in increasing revenues. Furthermore, they find that higher-quality samples have a greater impact on the sales of popular content. This has important implications for freemium and free-trial business models.

Industry 4.0 technologies have been regarded as powerful means to enhance a firm's competitiveness in the Internet of Things environment. However, implementing Industry 4.0 technologies calls for considerable capital expenditure and might interrupt normal production in the short term. This study conducts an empirical analysis of the impact of investing in Industry 4.0 technologies based on a sample of 563 investment announcements of publicly listed firms on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2013 to 2018. Using the event study method, we find empirical evidence that these investment announcements lead to positive stock market reactions and improved financial performance. In particular, we empirically evaluate firms’ short‐ and long‐term stock prices and financial measures by considering the type of investments (i.e., digital or physical investments), whether the investment is product‐oriented or manufacturing process‐oriented, and whether the technologies are applied within a firm or across the supply chain. Our empirical findings hold true when a firm's strategic decision is accounted for and remain robust through various tests. Furthermore, we propose a decision framework for firms to balance the tradeoff between short‐term disruption and long‐term benefits resulting from an investment in Industry 4.0. Specifically, we develop a two‐period model and investigate when and to what extent a firm should invest in Industry 4.0 technologies. Empirical and modeling analyses provide managerial insights for firms that grapple with the net benefit of investment in Industry 4.0.