![]() Definition of Marketing Analytics, models, and metrics Marketing analytics is a process to gather data, analyze, and report for the purpose of marketing. Or to be defined more clearly, marketing analytics is the state of techniques and tools that provide actionable insight in business. Techniques means model such as spreadsheet that gives a decision tool and metrics performance indicators that monitor the growth of business. Metrics helps monitor the situation and diagnose problem while model represents relevant information to helps decide on the course of actions. Nowadays, the trend is the adoption of marketing analytics in an organization. Firstly, a CEO wants the marketing department to improve the productivity and reduce costs. Second, data-driven presentations are used to back up proposed new plans. Fourth, data that are stored online provide easy access. Fifth, the customer information storage provides some following benefits: First of all, it helps drive revenue, positioning the marketing department as a profit center instead of cost centre. Next, marketing analysts predict the outcomes of their campaigns. Third, analytics persuades the executive with numbers. Fourth, the executive appreciates revenue, and the analytics delivers revenue when used effectively. Last but not least, analytics helps marketers test experiments before proceeding. With this approach, they can make some mistakes on paper with a little loss.
A model is a representation of reality to solve a problem. A graph shows the increase of ad spending leading to sales revenue. This effectiveness begins to level off when the ad spending comes to a certain point that the firm loses the revenue. The model helps evaluate the impact of input variables. Models come in different styles. There are three ways. First, it is expressed verbally: ad spending influences sales. Second, the model is expressed in a chart or graph. Third, a model can be expressed mathematically. A model can come in forms. First, a descriptive form states the marking phenomenon. Second, a predictive form helps determine the likely outcome given certain inputs. Last, a normative form maximizes the outcome given limits on the input variables. The independent variable is seen as advertising or sales, it is classified into controllable and non-controllable variables. For instance, advertising level or features of products are a controllable variable. Conversely, non-controllable variables include customer age, economic condition and so forth. The dependent variable is referred to marketing objectives such as output. In for-profit companies, the dependent variable are seen as sales revenue, units sold, profit and so forth. In for-non-profit companies, it is donation generated, pledge made, and so forth. Y = a + bx In a linear response model, y equals a plus b multiplied by x. Where y is a dependent variable representing sales revenue, a is perimeter called the y intercept, b is coefficient to x which interprets the slope of the line. X is an independent variable which represents ad. When x equals 0, y intercept is level of y. Metrics is a business-oriented key performance indicator such as sales per distribution channel, cost per sale, and sale for profit. Metrics helps monitor marketing effectiveness that the firm decides corrective approaches Comments are closed.
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AuthorRebellious head, crazy about challenges, changes, passionate about writing. That's all about the author. Archives
July 2018
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