For a professional in any field, it is essential to understand and apply statistics in practice, going beyond just mathematical calculations. In different spheres of activity, statistics perform certain functions, positively influencing an enterprise’s work quality. Professionals work in three ways: data analysis, product quality assessment, and the exchange of information between people, and marketing statistics are vital for evaluation.
First of all, statisticians need to draw conclusions from the analyzed data. This is necessary for making informed decisions and assessing patterns and trends in the market (Yang & Kim, 2020). Statistical methods such as regression analysis are especially needed among professionals, as they allow for more accurate forecasting. The second way to use statistics is to assess the quality of services and goods, as in the case of medicine, when evaluating the effectiveness of patient treatment. Third, statistics make it possible to speed up the exchange of information by making data available to the public in an accessible way.
For a more detailed analysis of the application of statistics will be taken an example of the field of marketing, where it is necessary to assess the effectiveness of the advertising campaign. For example, a manager can investigate his own campaign and how it was perceived by potential buyers, which will allow for drawing qualitative conclusions. Surveys can be conducted to see if people’s perception of the brand has changed for the better, and all this will create an overall assessment of the actions.
In conclusion, statistics can be used in various ways and provide both qualitative communication and detailed data analysis. The example of marketing shows how effective statistics are for evaluating advertising campaigns. Thus, three methods of using statistics, such as information exchange, performance evaluation, and data analysis, allow managers to do their jobs correctly and improve the results of enterprises.
Reference
Yang, S., & Kim, J. K. (2020). Statistical data integration in survey sampling: a review. Japanese Journal of Statistics and Data Science, 3(2), 625–650. Web.