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The Handbook of Marketing Analytics showcases analytical marketing methods and their high-impact real-life applications in marketing management, public policy, and litigation support. Fourteen chapters present an overview of specific marketing analytic methods in technical detail, while 22 case studies present thorough examples of the use of each method. The contributors featured are recognized authorities in their fields. Multidisciplinary in scope, this Handbook covers experimental methods, non-experimental methods, and their digital-era extensions. It explores topics such as classical and Bayesian econometrics, causality, machine learning, optimization, and recent advancements in conjoint analysis. This standout collection of analytical methods and application will be useful and educational for all readers, whether they are academics or practitioners in marketing, public policy, or litigation

There is general agreement that a firm's scarce marketing resources should be managed for the purpose of long-term profitable growth. Putting that premise into practice is difficult, as only the short-term impact of marketing actions can be readily observed. Persistence modeling has become a well-accepted tool for long-run impact detection. Its consistent use across a broad range of settings has resulted in novel empirical generalizations on the long-run effectiveness of several marketing instruments and has contributed unique insights on, among others, (i) the marketing-finance interface, (ii) the role of new media, and (iii) the mediating role of a broad set of mindset metrics. Moreover, the recent addition of a more normative focus has added considerably to the actionability of these insights.

Abstract
If company revenues fluctuate, the resulting volatility makes it more difficult to project the company's future revenues and earnings and ensure steady cash-flow. This lessens investor confidence and, as such, can harm the financial health of a brand. So, effective marketing can have undesired financial side effects.
The optimal marketing behaviors derived with and without volatility calculations will be quite different. Analytically savvy companies will be able to gain competitive advantage from this realization.

Abstract
The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships.
Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data.