PREDICTIVE BUSINESS ANALYTICS REPORT
Predictive analytics is a field of study that is concerned with the extraction of information from a given data set so that it can be used in predicting the past or future trends and behavior of a specific line of investment (Siegel 2013). There are several areas in which this field of study is applied in the real life scenarios. For instance, manufacturers are eager to know how their products will be received in specific market segments. On the same note, politicians depend on predictive polls to establish the worthiness of their campaign strategies. All these scenarios require that some sort of data be collected and then analyzed via special techniques with the goal of deducing a possible pattern.
Accordingly, the statistical data will be used to predict specific trends related to Cain Spring, a fictitious company that deals with both alcoholic and non-alcoholic beverages. The company has operations in different parts of the world where it strives to establish a balance in the sale of its products. However, due to some reasons, the peak demand of some products is only high during specific months of the year. According to the data provided, it is also evident that the demand of the drinks differs from one region to another. On the same note, the personality of the sales representatives as well as the price of the drinks significantly influences the demand of the products.
Data Analysis and Key Decisions
According to the data, Infinity is the most popular brand followed by Legend and Kodiak. In this case, the statistical sales for Infinity, Legend, and Kodiak are 32, 9 and 4 respectively. This is represented in figure 1 below and suggests that Cain Springs should focus more in marketing the brands of the less popular drinks (Cleland 2013, p. 182). It is highly likely that the sale of Infinity will not be affected in the short-run if the company decides to focus its marketing resources to the other products.
Figure 1: The popularity of specific brands in percentage
According to the raw data, it is also evident that Infinity generates the most revenue for the company at $ 98990 followed by Legend and Kodiak each at $ 8193.8 and $ 8123 respectively. The percentage of this contribution is summarized in figure 2 below. Due to increased popularity of the Infinity brand the company should investigate on the possible reasons for its success in the market (Cleland 2013, p. 182). The results should then be used to help improve the sales of the other products which are performing poorly in the market.
Figure 2: The value of revenue generated by each brand
The quantity by units of brands sold is also indicative of the generated revenue. In this case, 375 units of Infinity were sold worldwide followed by 66 and 60 units for Legends and Kodiak respectively. This is summarized in figure 3 below. It is evident that the units of sales as well as the revenues generated by both the Legend and Kodiak are almost equal. There is a possibility that the two are similar in terms of tastes and packaging and hence the consumers do not see a significant factor to form a tastes and preferences. The company should explore on the manner in which the two could be differentiated in the market (Cleland 2013, p. 182). On the same note, this could suggest that the company should improve while at the same time adopting a similar marketing strategy for the two products.
Figure 3: Demand of the products by units worldwide
To sum up on the above findings, predictive analytics can help a company such as the Cain Springs to analyze the trends and behaviours in regard to the sale of its products worldwide. So far, only one product appears to be dominating in the sales while the others are performing at the same level. There is a need to establish a balance in the three products.
List of references
Cleland, K. N., 2013. Improving profit : using contribution metrics to boost the bottom line. Apress.
Siegel, E.,2013. Predictive analytics : the power to predict who will click, buy, lie, or die. Hoboken, New Jersey : John Wiley & Sons, Inc.