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Formulating Interesting Research Questions
Formulating Interesting Research Questions

... incremental innovation takes the form of replication studies that also may extend the examination to include additional peripheral constructs. Incremental innovation has its place in academic research, but it is inherently less interesting than more innovative research and offers relatively little c ...
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... The best companies within these pillars drive innovation and excellence in products and services according to their field of specialty. The best companies are also able to engage prospects, customers, industry influencers and content communities through their marketing strategies within the context ...
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... IDENTIFY AND CONNECT WITH YOUR CUSTOMERS AND PROSPECTS The industry’s first machine learning predictive analytics solution for dealers that automatically harnesses data to better identify, connect with, and market smarter to a dealer’s entire customer database. SIMPLER AND MORE EFFECTIVE Dealers rep ...
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Marketing mix modeling

Marketing mix modeling (MMM) is a term of art for the use of statistical analysis such as multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics (marketing mix) on sales and then forecast the impact of future sets of tactics. It is often used to optimize advertising mix and promotional tactics with respect to sales revenue or profit.The techniques were developed by econometricians and were first applied to consumer packaged goods, since manufacturers of those goods had access to good data on sales and marketing support. The first companies dedicated to the commercial development of MMM were MMA (then Media Marketing Assessment) started in 1990 and the Hudson River Group founded in 1989. Other early pioneer-users of econometric modeling were the ATG group at the advertising agency JWT in the 1990s and later incorporated into MindShare ATG, BrandScience at Omnicom, and the specialist modeling agency OHAL since the late 1980s. These agencies took MMM from being a little-used and academic discipline to being a widespread and common marketing tool. Improved availability of data, massively greater computing power, and the pressure to measure and optimize marketing spend has driven the explosion in popularity as a marketing tool. In the recent times MMM has found acceptance as a trustworthy marketing tool among the major consumer marketing companies. Often in the digital media context, MMM is referred to as attribution modeling.
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