Discover the latest and most impactful research on leveraging data analytics to generate business insights here. All the research listed comes from the ARF or one of its subsidiaries: The Journal of Advertising Research (JAR), the Marketing Science Institute (MSI) or the Coalition for Innovative Media Measurement (CIMM). Feel free to bookmark this page, as it will be updated periodically.
Email advertising can increase spending in actual, physical retail stores, new statistical tools show. If this is not uplifting enough for those who deal with such advertising, analysis published in an MSI working paper found something else surprising. The effects are highest among consumers with fewer recent interactions with that particular retailer.
While Randomized Controlled Trials (RCTs) are ideal for advertising measurement, it is not always feasible to conduct one. Until advertising platforms are willing to provide more information about how they deliver targeted ads or implement auctions, alternative observational methods are unlikely to reliably estimate causal effects. That includes utilizing newer machine learning techniques.
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Random controlled experiments for A/B testing help improve things like a company's marketing or customer service. However, individually optimizing interventions may not always capture interactions across the entire purchase decision journey. To optimize interventions more holistically, use a Bayesian reinforcement learning model. It can integrate multiple historical experiments, which can improve both current impact as well as future learning.
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Do sports sponsorship announcements help or hurt the stock-market returns of sponsor companies and their rivals? A study comparing these effects in the U.S. and Japan provides insight into competitive advantages (or lack thereof) between sponsors and their rivals, depending on market reactions in each country, whether the sporting event is held in a home country and the market impact between rival firms.
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In December 2020, the Journal of Advertising Research published an independent seminal bibliographic study that identified key publishing trends since the academic journal’s inception in 1960. Now, a more granular overview expands on those findings to reveal the prominence of practitioner authors amid a wide range of themes, while encouraging future submissions.
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Do digital voice assistants make website navigation easier? Not necessarily, according to this MSI working paper. Researchers found that a sense of “social presence” created by such assistants can evoke social norms. And so, these devices can in fact predispose consumers to more prosocial behavior, such as donating to important causes and tipping.
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Which is more efficient when it comes to advantageous decision outcomes, marketing-mix metrics or financial metrics? Researchers in this award-winning MSI working paper investigated this question, basing their research on a behavioral framework and statistical model. On average, according to their sample, marketing-mix metrics are more effective than financial. Even so, managers seem more eager to use financial metrics, possibly, because they are easier to understand across the organization.
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Using mobile location data to improve targeting marketing is a good strategy, but the downside is that it can increase consumer concern over privacy. What’s a better way to do it? Instead of aggregating data by home location, doing so by the centroid of brand sites visited can result in good predictions, while still maintaining individual anonymity.
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It’s no surprise that new product launches often fail to meet their targets. The trick for managers is to improve their predictions for such products. They must balance the costs and benefits of many different data sources and analytic techniques in order to improve forecasting. To enhance the accuracy of predicting the market-level sales of new products, researchers Marton Varga, Anita Tusche, Paulo Albuquerque, Nadine Gier, Bernd Weber, and Hilke Plassmann, analyzed the added value of different data types. Their conclusions are illuminating.
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There are a number of different biases that can impact panel and census research and big data sets which utilize artificial intelligence (AI) or machine learning (ML). The attached Knowledge at Hand identifies over two dozen unique biases. It discusses how they might occur and what mitigation options researchers should consider to account for them.
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