Business analysis
Ii
Some of the critical information that can be deducted from the report include the level of innovation. This information would help the organization in future decision making in that it will be able to identify the field that needs more investment and invest in it. It will also help in predicting the next trend in the market. Another information on this insight is the report of customers’ feedback on the issues concerning online marketing. This helps in determining individuals’ satisfaction and how the company can improve its activities to ensure there are a large number of customers buying with it in the future.
The other information presented in this report includes the ability of growth after investment. This information will help ensure that the company has made a sound decision when determining the amount of money to be invested in certain fields and possible returns. In this report, information on performance after invention on a certain commodity has been indicated. This will help in ensuring that decision on which product to invest has been well made.
Iii
Online shopping has become an activity that every individual who can use the internet and search for his or her preference tend to involve in. Customers are visiting marketing cites to research commodities that companies have in the market. Besides, business individuals are using the online market to market their products. This has helped in creating an extensive network and enlarging markets up to the global level. The insight report has given more detail on innovation performance. In this, the analysis of return after the innovation has been done.
Furthermore, the insight has summarized the dynamics of innovation, where a change in the commodity is related to change in return. Through this insight, the potential of realizing profits after innovation on a commodity has also be summarized. From this insight, the ability to grow through the use of online marketing is well elaborated.
Iv
Some suggestions would lead to an improvement in the report. One of the suggestions is an indication of how customer satisfaction would be determined after any commodity is released. Another idea would be giving recommendations on areas that are likely to grow evenly from individuals being innovative. The other suggestion would be the use of real information during the analysis and compare the outcome of any investment. On doing these activities, there is a higher chance that the insight would be more applicable and useful.
Q 2
I,
regression analysis can be effectively used to forecast future demand for the company product. It can also be used in determining the strength of an independent variable. This is where a commodity identification of a result of any change occurred after changing a commodity is done. It can also be used in the prediction of results after any investment. This will be following early reports and analysis of trend changing.
3
Classification mainly deals with the categorization of commodities, while prediction gives the possibility of the next element in a set. In taxonomy, there is a grouping of data following similarities stated. In many instances, the prediction is applied to foretell future possibilities.
Ii
Different classification methods are used in grouping information. They include linear classification, quadratic classification, and neuro networks. Other are decision trees, learning vectors, and support vector machines. These methods are highly used.
Iii
Neural networks are used in classification in that they summarize the similarities of commodities as they relate with each other. In neural network classification, there are different layers that receive inputs from a separate layer. The data are computed, and their class is determined. From this level, the results are sent to another layer where further classification is performed. The first step classifications are as per the main differences that exist, and as the classification continues, there is a more specification of identity as they approach the end of classification.
Iv
Clustering is used in breaking down huge data into small groups of data. In business, analytics clustering can be used in the determination of how beneficial a commodity is to the business. Another way through which clustering can be used is to predict the future fate of a product in business. This follows the outline of the advantage and disadvantages associated with an entity. It can be used to explore patterns followed in business.
One of the business causes that can be addressed using clustering is the issue of losses in the business. Clustering will ease the identification of where the losses are from and help in identifying measures to take as per identified cause. Another cause that can be addressed through the use of the technique is how to deal with competition. The technique will ease the identification of the source of competition and work to reduce their effects.