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Marketing mix modeling (MMM) is an analytical approach that uses historic information to quantify impact of marketing activities on sales. Example information that can be used are syndicated point-of-sale data (aggregated collection of product retail sales activity across a chosen set of parameters, like category of product or geographic market) and companies’ internal data.
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A MKDSS is used to support the software vendors’ planning strategy for marketing products. It can help to identify advantageous levels of pricing, advertising spending, and advertising copy for the firm’s products. [4] This helps determine the firm's marketing mix for product software.
The prospect of expanding or modifying the marketing mix for services was a core discussion topic at the inaugural AMA Conference dedicated to Services Marketing in the early 1980s, and built on earlier theoretical works pointing to many important problems and limitations of the 4 Ps model. [20]
The business model canvas is a strategic management template that is used for developing new business models and documenting existing ones. [2] [3] It offers a visual chart with elements describing a firm's or product's value proposition, [4] infrastructure, customers, and finances, [1] assisting businesses to align their activities by illustrating potential trade-offs.
GE multifactorial analysis is a technique used in brand marketing and product management to help a company decide what products to add to its portfolio and which opportunities in the market they should continue to invest in. It is conceptually similar to BCG analysis, but more complex with nine cells
SOSTAC contains a general marketing strategy which can be applied in various commercial situations. [ 7 ] [ 8 ] It includes an in-depth SWOT analysis , which helps businesses get ready for marketing campaigns; the main difference is that SOSTAC also focuses on the implementation stages of the process, [ 9 ] on marketing communications [ 10 ...
In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. [1] [2] [3] They also inherit from generalized linear models the idea of extending linear mixed models to non-normal data.