Greybar Case Summary

As a group, team A collectively provided input and decisions on our MSRP in order to increase sales and attempt to gain market shares. Thus we wanted to understand the price sensitivity of the different target markets to set the appropriate price (Winer & Dhar, 2016, p. 248). We carefully reviewed the MSRP performance summary provided, starting with the first period, comparing it to the average retail price by way of the channel report (ARPCR). The original price for Allround was $5.29. At that time we were $.40 higher in price than our competition however the team felt that with the $.50 coupon discount we were offering, we would fall in line with other over-the-counter Besthelp who was our immediate

The company is profitable. However, management has become concerned about the profitability of the customers in its three customer-size categories—large, medium and small. Different customers demand different levels of support. Management has no basis for identifying customers that generate high profits or to drop those that do not generate enough revenues to cover the expenses to support them. Under the previous accounting system, it wasn't possible to determine the costs of supporting individual customers.

In this article How Does Target Know so Much about Its Customers? Utilizing Customer Analytics to Make Marketing Decisions, authors describe data mining program, types of data needed to identify changes in consumer behavior, privacy issues that arise with data mining, and customer analytics support marketing decisions. Target customer behavior is tracked, analyzed, and possibly shared with other business, whenever shoppers make a purchase or browse on its Web site. In fact, all three authors enlightened, “Target Corporation is a leader in analyzing vast amounts of data to identify buying patterns, improve customer satisfaction, predict future trends, select promotional strategies, and increase revenue ”(Corrigan, Craciun, Powell 159). Target

Positioning is complex concept that does not involve creating something original, but to make your alter your consumer idea of your brand. WIth that being said, a major part of positioning is targeting a narrow audience. For universities like Lindenwood, who have a huge athletic program, that means targeting football players and manipulating them into thinking they have a great football program because they have had players drafted in the past. If Lindenwood can successfully do that, that would create a higher interest for football players, which would lead to more recruites, a better football program, and a larger profit for the school.

Companies recognize that acquiring a new customer can cost thousands of dollars. Therefore, to optimize their marketing spend, businesses are searching for answers within analytics to drive their customer acquisition strategies.

My organization Penske Truck Leasing has been experiencing a sharp decline in the revenue generated from its full service leasing. Results of initial sales analysis concluded that the inflow of the new customers for full service leasing has been growing steadily; thus to increase revenue, the focus needs to be on the renewals for the existing customers. The company realizes that a lack of validated customer loyalty drivers prevents effective focus on customer retention and thus directly affects profitability. Challenges with data availability and business metrics have made attempts to measure and diagnose our service delivery and customer experience, both, difficult and inconclusive. In order to address this challenge, the organization has launched an initiative to build a customer analytics system that can achieve a single view of customer experience and make decisions about how best to acquire and retain customers, recognize high-value customers and proactively interact with them. I have been identified as the lead architect for the BI and analytics team that is going to implement this project.

Customer data is almost as valuable as the currency exchanged for goods and services in business and many experts believe that customer data is the currency of choice for many business segments (Bridgwater, 2013). Having context, or gaining business insight as it is described by Bridgwater, around what the customer data means is when this data is most valuable and that is where some of the challenges line-up for managing this data as valuable currency.

According to the Case context, we can find out many ideas about leadership. First, from the great Chinese ancient philosopher-Confucius that we can learn that a phenomenal leader needs to love people and use virtue to rule a society. The ultimate goal for a leader is to fulfill everyone’s demand. In order to achieve this goal; a leader must be a sage or at least a very wise man to set up a very extraordinary example for others to learn. However, this opinion has a very obvious flaw that people cannot be as good as he think and most people are selfish that this kind of leadership is not going to be successful. Second, From the Machiavelli, we can acquire the idea which

There are many ways that organizations are able to leverage the power of predictive analytics. One of the more popular ways is through their current SAP software. SAP has been one of the premier enterprise software tools and now has made its way into the predictive analytics side. The ability of the software to work with the current data environment is able to save both time and money. The current software has the capability to perform five forms of analysis including: Time series analysis, classification analysis, cluster analysis, association analysis, and outlier analysis. In the time series, analysis of historical patterns are used in the known data to make predictions about future values (MacGregor). Classification analysis tries to predict a variable using the data of other variables that you believe affect the values of the variable that we are trying to predict. Cluster Analysis groups the data into clusters or segments that have similar attributes which is often useful when trying to understand subsets of larger data. Association analysis looks at the association of different things, with an example being if a customer purchases this product what other product are they likely to buy. Finally an

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.

SAP is known for their ability to innovate and release new software at a fast rate. SAP encountered some early losses when they would not be able to provide consulting services to their new clients. This focus on developing its R&D market led to problems with their ability to market and sell in global markets. It wasn’t until SAP went public on the Frankfurt stock exchange in 1988 that they were able to raise the cash to fund the global operations. SAP still faced problems with clients having to use external consultants to install and modify the software.

To compete effectively in an era in which advantages are ephemeral, companies need to move beyond historical, rear-view understandings of business performance and customer behavior and become more proactive(tableau). Predictive Analytics is the use of data science for audience profiling. Generic audience profiling involves determining specific characteristics of your target audience and creating specific personas to represent each type of person within your target audience. Predictive analytics is essentially the same process, but from a data perspective (koozai). Predictive analytics can be used in wide areas in the industry, it’s importance is not constrained to a particular domain and ranges from marketing, telecommunication, retail, banking, etc. For example, the telecommunication industry has noticed a high customer churn since the switching costs are slim to none. So telecommunication companies operating in this industry are looking for new ways to differentiate themselves from competitors in order to retain customers. By using predictive analytics as a solution to this problem, they would be able to understand the customer needs, requirements and retain them also allowing them to acquire new ones more effectively. With predictive analytics, companies can predict trends, understand customers, improve business, drive strategic decision making and predict behavior. A company named Cox Communications, the third largest cable entertainment and broadband

Business analytics plays a major in sourcing new customers in emerging markets. Target has over the years, employed business analyst that mine customer data for shopping habits and use the data observed to design advertising and marketing schemes that can

"Mantas Scenarios are already coded, tested, and ready to use, enabling customers to realize benefits much sooner than with other, more time-consuming alternatives. There is no learning stage, or fuzzy-adaptive-black box scheme to run. We code it in right from the start because we understand the business and the business user. Mantas gives financial institutions and communications service providers the ability to monitor and analyze customer, performance and transaction information across the entire organization for a complete and accurate picture of behaviors of interest. As a result, it 's now possible to detect suspect behavior early enough to mitigate risk, report and prioritize findings without manual intervention, comply with changing regulations, increase revenue by improving

Enhancing customer satisfaction and retention using data tools provides many organizations steps in creating a service environment that knows who its customers really are and how it plans to retain those customers. Many service organizations has implemented customer relationship management tools. These tools are used to keep track of customer needs, access multiple sources of customer information, for example; credit reports, past contacts, and voice and e-mail messages to record service provided. Using these data tools could be a crucial element for providing