Data Analytics : Building A Better Mouse Trap

1. Define data analytics in general and provide a brief overview of the evolution of utilizing data analytics in business.

The goals have been set and data analytics best practices need to be monitored. The experienced gained in this phase will shape the next course of action based on external and internal issues. As the data is formulated, it will identify the strengths and the weaknesses, threats and opportunities for improvements. Because the internal and external issues will continue

Data is an important component used by all technology and businesses to gain insights and refine their marketing techniques to improve customer experience. Through the use of data analysis companies can capture a large amount of data, and retrieve information regarding the company’s trends. In Richard’s case using data analytics allows him to gain better insights on customer behaviors, in which he was able to determine which factors influence a user to purchase the eReader early. These factors will allow the team to develop new business models and improve their marketing strategies to capture these individuals (“Big Data Analytics”, 2017). For example, if most of the early users are from California, it would be beneficial to promote the eReader

The latest technological development in data analytics and implementation helps organizations gain insights and how to deal with informed decisions.

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.

Data analytics is the science of examining raw data with the purpose of drawing conclusions about certain information that is drawn from the data. By gathering data, it must be captured and reviewed then it can be turned into information. There are different types of analytics such as descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics will describe, what happened during the process. Diagnostic analytics describes, why did it happen. Predictive analytics describes, what will happen. Prescriptive analytics describes, how can the process happen with a different approach. By applying these different types of analytics, it will answer several questions during the auditing process. Involving analytics to a process it requires

In today’s society businesses accumulate all the data they can gather into the data warehouse, from which they can do data mining. This means that when we go to the grocery store and use our “saver card”, to get the tiny percent off, that store automatically tracks what you purchased and then enters it into their data warehouse. This allows the businesses to later go through that information and look for particular trends, seeing what products are popular what time of year, and as well as try figure what would we most likely to purchase. The art of looking through the data warehouse is referred to as data mining.

Stage 4, Analytical Companies – Analytics have been applied at an enterprise-wide level and are being used to drive decision-making, performance, and innovation, but results may not yet have been realized

Today, the world’s trend in operating business focuses on data availability to enact the best suitable decision to improve, develop, and increase business revenues. Moreover, the availability of data helps to monitor and control the quality of provided products or services. However, the availability of data without proper analytics operations would have no meaning (1). Data analytics provide an important aid to an organization to figure out their position in the market in comparison with their competitors. Also, data analytics helps to identify what is the organization’s competitive ability in the market, what they should bet on, and what they should strive for. With that being said, many of today’s most successful organizations utilize

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.

In an uber globalized market of today, companies are faced with challenges in each and every step of their business. Our analytics and research services are geared towards giving those companies that extra edge over the competition. We process and analyze terabytes of data and break down all the fuzz and chatter around it to give our customers meaningful insights about their competition and the market they are engaged in.

The sections below go into greater detail about how organizations can use analytics as a competitive weapon to introduce new goods and services and support existing ones.

According to Competing on Analytics: The New Science of Winning, the critical value of analytics is important in today’s forward-looking enterprises, especially in a new data age. Every company and organization should strive to become an analytical competitor. Competing on Analytics reveals how companies think about their data and their exploitation of that data. Also, it highlights how companies such as the Boston Red Sox, Netflix, Amazon.com, CEMEX, Capital One, and Harrah’s Entertainment use analytics to build their competitive strategies and make better decisions in the severe competition. These companies and organizations use analytics to identify the most profitable customers, accelerate product innovation, optimize supply chains and pricing, and leverage the true drivers of financial performance.

Analytics has been used as isolated initiatives within my company. Different levels of applications of analytics is a reality. Historically, quality department has leading the way in data-driven decision process by using several statistical and quality

Google Analytics is a product or an analytical tool which helps marketers design strategy as per the response of the customers. As use to happen some years earlier, a marketer would design a strategy, implement it at the market place and then conduct surveys or customer feedbacks after a significant period has passed to know the effect of his strategy or whether he needs to bring changes. By the time the marketer implements a new strategy the market dynamics and needs of the customers have already changed and hence there is always a lag between the customer demands and the strategy response to that demand. The purpose of this study is to understand how Google Analytics as an analytical tool helps marketers react immediately to customer demands and interests. For this, we can consider the example of Slickdeals which is an e-commerce website selling products online.