Predictive Analytics And E Commerce And Internet Based Services Industry

Decision making is one of the most important aspects in life and work because of its strong link to success and effectiveness. Actually, successful people achieve their goals in life and work through effective and efficient decision making. The decision making process is usually guided by an individual’s beliefs, values, and attitudes as well concepts. While a person can use various concepts in making decisions, they should be very careful to select a concept that is effective and contributes to huge success. Nonetheless, these concepts exist to help an individual become a better decision maker in the world around him/her.

The data analytic process is one in which a large amount of information is collected using software specifically geared towards collecting, identifying and storing information for use by the company. The information is gleaned from different forums, with social media being the most rich and useful. The information is then quickly sorted and organized for use by the collecting agency (Turban, Volonino, Wood, & Sipior, 2002, p. 6). The use of data analytics really took flight in 2010 when different companies offered software that enabled a company to implement their own data analytics. This led to better marketing campaigns, improved customer relations and it gave companies using the software a bigger advantage over their competitors (Savitz, 2012).

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

Decision making is the defined as the thought process of selecting a logical choice from the available options. When trying to make a good decision, a person must weigh the positive and negatives of each option, and consider all possible alternatives. For effective decision making, a person must be able to forecast the outcome of each option as well.

Decision making process is the act of choosing between alternatives. For example, when we see a manager planning out goals, gathers information and puts in order, and is in control of the project, it is called exercising the decision making process.

The standard definition of decision making is; the process of mapping the likely consequences of decisions, working out the importance of individual factors and choosing the best course of action to take ("Definition of decision,"). In this case study effective decision making tools will be used to choose the best course of action to take in the scenario.

In this article we have tried to define and make good and comprehensive explanations of decision making process in different situation. because of decision making importance in business we have tried to take a deep look different subjects that are direct and indirectly related to decision making so as we know decision making is the process of selecting a logical choice from among the available options to do that we need to evaluate, analyze and determine which alternative will be suited for our case of actions.

With “big data” and analytics being a hot topic as of late, drawing headlines like “The Sexiest Job of the 21st Century: Data Analyst” (Morris, 2013), it’s clear that this isn’t just a fad passing through. Data analytics has been around for a while now, but it is finally getting the recognition in deserves by many businesses. More businesses are seeing the value in using their data to make better business decisions, and one method of doing that is through using predictive analytics. According to Teresa Meek, “Predictive analytics can improve retailers’ pricing, inventory control, customer service, and ultimately, their bottom line” (2015). This paper is going to focus on looking at the different types of predictive analytics in relation to the retail industry. After highlighting the techniques and different software available to do predictive analytics, different retail businesses will be examined on how they are currently using predictive analytics as well.

Decision making is the study of identifying and choosing alternatives based on the values and preferences of the decision maker. It is also an important part of doing a better business Moreover, using appropriate IT software and spreadsheets for data analysis and the

Decision-making is the act of choosing one alternative from among the set of alternatives. The decision-making process is recognising and defining the nature of decision situation, identifying alternatives, choosing the best alternative, and putting it into practice. (Griffin R, 2012). Decision making process also helps businesses allocate scarce resources by individuals or groups to achieve goals under conditions of uncertainty and risk. Sometimes, groups choose better decisions than individuals. It is important to realise that effectiveness of decision is different than ease of decision. Once goal has been established, managers could adopt the classical model or the rational decision making model. There is also a risk of uncertainty which states that the manager does not know all the possible alternatives and their possible consequences. In this issue, rational decision making is used.

Detail research of the option available to take the decision and the outcomes of that decision is the seven-step decision-making model. After selecting the information that is analyze, decision makers will team up by presenting personal data about the information. Any decision making model used will start with analyzing the problem that makes a

This case study reviews the strategy followed by Union Investment Group (UIG) to analyze customer behavior patterns using predictive analytics (Union Investment GmbH, 2015). With the business objective of boosting profitability and staying ahead of the competition while facing a customer base that demanded individualized product offerings, UIG teamed with IBM to determine the problem at hand. After describing the problem, this case study next considers the stakeholders involved, other expert opinions, recommendations, and caveats.

When people argue with decision analysis and its achievements they usually mention the story of DuPont, an American chemical company. It managed to successfully integrate risk and decision analysis functions to both operational and strategic levels. The company claimed that Desciison and Risk Analysis not only brought tangible benefits to the company, but also intangible, such as increased attention and commitment to actions (Krumm et Roll, 1992).

Michael J. Shaw a,b,c,) , Chandrasekar Subramaniam a , Gek Woo Tan a , Michael E. Welge b

Detailed understanding of the identified problems and collaborative determination of the solution is decision-making. Successful accomplishment of this task requires applying multiple steps which assist individuals to make exceptional decisions. One’s decision-making model might be different than others because there are four known decision-making styles: rational bounded rationality, intuitive, and creative.