Discuss About The Important For Companies Since Management?
Data analytic is very important for companies since it helps the management of various businesses to draw conclusions from a set of collected data. Data analytic is the process that involves examining sets of data so as to obtain meaningful information that can be used to draw conclusions. These conclusions can then be used as the basis of making decisions by an organization. This tool is very important since it gives statistical data that is measurable and reliable. There are a variety of data analytics technologies and techniques that companies` use to analyze information in their organizations. The information can be about market trends, customer data and even data on competitors. It is important since it helps managers to make very informed decisions (Bartlett , 2013). This report uses Coca Cola Company as the case study for this assessment. The report seeks to identify how the company uses data analytics to help manage churn. The business problem that is facing the company is the increased competition in the market which threatens its market share. The company seeks to obtain data from customers regarding their preferences and how their needs can be met more effectively. The report also examines Coca Cola method of operation and potential for increasing efficiency in the company. The data analysis methods used by the company as well as communication strategies and the significance of the data to stakeholder is also discussed.
The purpose of this case study is to examine how data analytics can be used by a company to reduce customer attrition. This study focuses on Coca cola to help us identify how the company has been able to successfully use data to help to maintain its customer base and prevent customers from changing allegiance. The study will use real examples of how Coca Cola has used data analytics to generate important information that can be used for decision making regarding customers (Surma,Go?rniakowska & Gee,2011)
In the recent years, Coca Cola Company has noticed a decrease in its sales especially in the US. This decrease in sales can be attributed to many factors in the market. One of the factors that contributed to the decline in the sales of coca cola was due to increased health consciousness among consumers (Marr, 2015). In the year 2016, the sales for the companies carbonated drinks reduced by 0.8%. The decline for Diet coke alone was recorded at 4.3%. This decrease in sales can also be attributed to customer attrition because of increased competition in the market. This paper therefore seeks to establish how Coca Cola Company uses data analytics to prevent or manage customer attrition.
Coca cola is a global company but it operates in local units in all the areas it does business. The company has a system where it vets and appoints bottling companies which act as their partners. The coca cola company has more than 250 bottlers globally (Albright, Winston & zappe 2011).The company operates through a decentralized system. Coca cola manufactures concentrates, beverage bases and syrup and sells them to bottling companies. The company owns the brand of the products made from its concentrates and syrup. The company is also in charge of marketing initiatives to help reach the final consumer and influence sales. The bottling partners of Coca cola are responsible for manufacturing, packaging, merchandising and distributing coca cola products. The retailer is the final point in the distribution cycle of the company. Coca cola partners also met in the year 2007 and developed a set of performance indicators. The company faces a challenge in consolidating economic data and therefore, most of the corporate partners prepare their individual corporate responsibility reports. However, the company is focused on ensuring that their standards are met by the bottler partners to ensure that the brand name of coca cola remains strong.
Increased efficiency can be achieved in the Coca cola model of operation through consolidation of its market globally. The company’s operations are so fragmented to the point of the company losing control of how the company performs in the region. The consolidation of operations will help the company to reduce operational costs of coordinating all the markets its operating in. Consolidation will also help the company to focus on understanding the needs of consumers better. This will help the company to meet the needs of the consumers and to gain competitive advantage (Showers, 2015). Efficiency can also be increased by coca cola by sourcing out the marketing and advertising role to third parties. This should be mainly a marketing company to help draw strategies to target different market segments. This will also help coca cola to cut on costs of marketing which constitutes the biggest expenditure by the company.
Coca cola depends on data from various sources to help it in understanding market trends and also competition in the industry. This data is very important since it helps coca cola in fulfilling the needs of the customers by understanding their needs better. The data is also collected on the industry competitiveness, new players entering and leaving the market. Coca cola`s data on the sales volumes achieved in the various regions it operates in Is also available (Davenport, Harris& morison,2010). The model of operation of coca cola makes it somehow difficult to collect, analyze and compare data since each market has different characteristics and needs. The customers are also different in each of the markets that the company operates. In addition to this, coca cola can also access data on its competitors. It is very important for any company to have optimum knowledge about the competitor since it helps in designing strategies. Data on the sales of competitors, the new products being launched as well as loss or gain in customer base is also available.
The data is from varying diverse sources which provides an opportunity for the company to compare the data. The data comes from journals such as Euromonitor International, Unesda Statista (Foreman,2013). Magazines such as the Financial Times are also used as important source of data. There are also various organizations that conduct data on industry trends and consumption behavior in the soft drinks industry which are able to track data on the industry in a timely manner. There are also a variety of other companies who conduct yearly research and they provide important data which is good for reference. Coca cola keeps financial records which help in making decisions by the management. The company prepares yearly report and presents them to stakeholders to help evaluate the financial position of the company. The data on the performance of the company is also analyzed to help to examine the factors which impact on the performance of the company. The data collected from various sources as stated above is used by coca cola to reduce customer attrition. This data is scientifically analyzed to help understand the customer better and therefore fulfill their needs. Some of the strategies that can be undertaken to prevent customer attrition include improving product quality, diversifying product, increased advertisement to counter competitors and ensuring fair pricing of the company’s products (Provost &Fawcett, 2013). The strategy to be employed will depend on the conclusions drawn from the data collected.
Data for this case study is measured using different methods depending on the aspects under consideration. The financial data can be measured using balance sheets, income statements and cash flow statements. This helps to identify the financial performance of the company in the markets in which it operates. If the performance is seen to have gone down, then it means that the company is losing customers and corrective action needs to be taken. Financial data is compared with previous year’s performance and the predetermined goals (Delen, 2015). The other key performance metric is the comparison of sales with those of closets competitors. The trend determined here can help the company to know whether or not it is experiencing customer attrition.
The data analysis methods used by the company depend on the type of data collected. Quantitative data is analyzed using methods such as predictive focusing by designing an hypothesis which is compared with the outcome of the data analyzed. The exploratory data analysis technique is also used to analyze data. In this case, analysts apply exploratory techniques to help understand the messages in the data. Descriptive statistics are also integrated in this model. Data visualization is used to examine the patterns by drawing table and graphs. This helps the researcher to interpret the data in a clearer manner. The relationship between variables can be determined using the correlation, regression and t-test. The tools that can be used to analyze the data include SPSS and Excel spreadsheet. The following is sample of data collected on the customer’s preference of either coke diet or other types of coca cola products;
|
Name of product/category |
No of loyal customers |
|
Coke diet |
239 |
|
Mineral water |
105 |
|
Other coca cola drinks |
156 |
From the sample data presented, an histogram graph can be generated that can be able to indicate the customers preference for each of the products listed. Conclusions can be drawn from the graph which can help in decision making.
After the analysis of data collected from various secondary and primary sources it is important that the data is communicated to various stakeholders. Communication of the results of the data analysis is important in order to help in drawing conclusions and decision making. This information is communicated in various formats such as graphs and tables. These data visualization techniques help to communicate clearly and efficiently. The visualization technique will help users to get an overview of the outcomes without necessarily having to read the report itself.
There are various stakeholders in coca cola Company who may be interested in the report on the data collected. One of the major stakeholders interested in these analytics is the management team of the company. The management needs information to be able to make decisions and design strategies on behalf of the company. This information helps in preventing customer attrition. The other stakeholders include the shareholders of the company, future investors, employees of the company as well as other cooperate partners. The other who may be interested in this data are the general public since the company is listed.
The insights of the data analytics should be presented to stakeholders in the simplest way possible. The presentation should be clear and simple to understand. The data is presented by the use of graphs and tables and other visual features which may be necessary. The insights of from the data can also be presented using frequency distribution. Graphical presentation may be bar graphs, histogram, pie chart or line graph. The collected data needs to be copied in a data analysis tool such as SPSS or Microsoft Excel. From there, the graphs can be easily developed to help give insight into the data entered into the system.
There are many benefits of using data analytics. The major benefit is that it helps in decision making process. Numerical and up to date data can be used by the management of the company to make decisions that help to reduce attrition among customers. The management can improve products quality or improve advertising effort. The data helps the company in understanding customer needs and doing everything necessary to meet consumer needs. The data can also be used by the company to understand their competitors better and to gain insight into the market trends and therefore help the company to design strategies that will give the company competitive advantage. The data can be used by investors to make investment decisions. This depends on how the company is performing and whether or not the company is making enough profits. Data analytics are helping coca cola launch new products that are popular with customers and hence helping coca cola to maintain its customers and market share. The company is also focusing on developing products that are health conscious and hence reducing customer attrition.
The challenges that may be faced in implementing of the recommendation include the data collected may be inaccurate and therefore mislead the management when making the decision. This can result to wrong decisions by arrived at by the management. Another challenge is that the problem detected may not be controlled by the organization since it is an external management issue. An example of this are government legislation`s which may affect business operations.
Conclusion
Data analytics is very important for various companies. Data analytics helps in analyzing and interpreting data in such a way that it generates useful information. This information can be used by the management of the company in making informed decisions. The information obtained from data analytics can be used by various other shareholders to help them in decision making. This report analyzes the sources of data for coca cola and the tools and methods used to analyze this data. The presentation methods are also discussed as well as the benefits and consequences of the data obtained by the company.
References
Albright, s. C., winston, w. L., & zappe, c. J. (2011). Data analysis and decision making. Mason, ohio, south-western/cengage learning.
Bartlett, r. (2013). A practitioner’s guide to business analytics: Using data analysis tools to improve your organization’s decision making and strategy. New york, mcgraw-hill professional.
Black, k. (2010). Business statistics: For contemporary decision making. Hoboken, nj, wiley.
Davenport, t. H., harris, j. G., & morison, r. (2010). Analytics at work: Smarter decisions, better results. Http://www.books24x7.com/marc.asp?Bookid=36278.
Delen, d. (2015). Real-world data mining: Applied business analytics and decision making. Upper saddle river, new jersey, pearson education ltd. Http://proquest.safaribooksonline.com/9780133551150.
Icdsst (conference), & delibas?ic?, b. (2015). Decision support systems v — big data analytics for decision making: First international conference, icdsst 2015, belgrade, serbia, may 27-29, 2015, proceedings. Http://dx.doi.org/10.1007/978-3-319-18533-0.
Foreman, j. W. (2013). Data smart using data science to transform information into insight. Hoboken, wiley.
Kossovsky, n., & miller, t. A. (2010). Managing risk and reputation to create enterprise value. Indianapolis, trafford publishing.
Marr, b. (2015). Big data: Using smart big data, analytics and metrics to make better decisions and improve performance.
Provost, f., & fawcett, t. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, ca, o’reilly media. Http://public.eblib.com/choice/publicfullrecord.aspx?P=1323973.
Rainer, r. K., cegielski, c. G., splettstoesser-hogeterp, i., & sanchez-rodriguez, c. (2013). Introduction to information systems: Supporting and transforming business.
.. Library analytics and metrics. Using data to drive decisions and services. London, facet publishing.
Surma, j., go?rniakowska, m., & gee, p. (2011). Business intelligence: Making decisions through data analytics. New york, n.y., business expert press
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