Reference no: EM13840023

An explanatory variable, also known as a predictor variable, is a variable that can influence the outcome of a response (dependent) variable (Taylor, 2015). In our hypothetical situation, the dependent variable is the level of demand for the new, signature t-shirts, and the explanatory variables are the components on which the level of demand is dependent. As the individual chosen to forecast the demand for this new product, I would initially include the following three explanatory variables in my research: the price strategies of competing goods, the income level of the target demographic, and the effectiveness of advertising campaigns; each of these variables has the ability to impact the overall level of demand for the new product, both positively and negatively, which is why they would be evaluated.

First, the pricing of competing goods is an explanatory variable, which requires an in-depth analysis, because the existence of suitable substitutes causes consumer demand to be more elastic in nature. As we learned last week, when the demand for a product is elastic, overall demand will decrease if there is an increase in the price of the product (Moffatt, 2015). If the company sets their prices too high, demand may decrease because viable substitutes exist; if the price is set too low, demand may decline due to consumers relating lower pricing to sub-par quality. Analyzing competitor's prices, as an explanatory variable, allows the organization to understand how their pricing strategy compares to others within the same market, and how the price of their new, signature t-shirt line could impact the overall level of demand within the market.

Second, the income level of the targeted demographic is another explanatory variable which could have a significant impact on the overall level of demand for the new signature t-shirts. Much like price elasticity, the overall income level of a target demographic can impact the overall demand for the product in that, if a product is targeted at a demographic which does not have the financial means to purchase the merchandise at the price offered, demand will be quite low (Mhurchu, et al., 2015). Unlike elastic demand due to competing products and prices, elastic demand, as a result of income, is directly related to the targeted demographic. If the organization is able to properly test and analyze their targeted demographic, they can either adjust their pricing, or they can target a new demographic that is responsive to their current pricing; both alternatives utilize the data received in order to help increase consumer demand for the signature t-shirts.

Lastly, a third explanatory variable that can have an impact on the level of demand for the company's new product is the overall

promotional strategy for the company. When attempting to forecast sales, using that AIDA model for advertising can provide some key insight as to how the company's promotional strategy will impact demand. The components of the AIDA model are attention, interest, desire, and action; each component is directly dependent on the successful completion of the preceding component. By assessing the company's promotional tactics with this model, one can learn whether or attention is being given to a new product, if that attention grows into consumer interest, if that interest manifests itself within the consumer in order to create desire, and if the desire is strong enough to push the consumer to take action (Ferrell & Hartline, 2014). In the end, the level of demand for the new product is dependent on the advertising tactics pushing consumers to take action; anything short of the marketing strategy meeting all of the steps in the AIDA process would result in a lesser level of demand.

If the task required the forecasting of international sales, the explanatory variables of the price strategies of competing goods, the income level of the target demographic, and the effectiveness of advertising campaigns would still be evaluated. However, there would also need to be an additional explanatory variable added to the analysis, and that is the generally accepted principles of the geographic location. When attempting to forecast international sales, the geographic features that must be considered are the cultural norms, language differences, delivery methods of the product, local and national laws, and available marketing channels (Morello, 2015). All of these variables impact the level of demand for a product within the geographic location; proper analysis of these variables can allow the organization to break down the various target markets into sub-groups, in order to pinpoint the desired target market. Doing so can help increase the level of demand for the t-shirts, whereas failing to understand the geographical impact can lead to lower demand levels.