3. Data Mining and Predictive Analytics as Marketing Strategy Tool
After understanding the possible outcomes and usages of Big Data Mining and Analytics, the study of the process is necessary to identify the real possibilities behind this techniques and how this can improve a business performance. To do this; we should comprehend the basics about data mining and the process that leads from pure data to insights.
At this point, is important to note that Big data itself does not represent more large data set of structured and unstructured data; nowadays bigger than ever and in continuous expansion that can be defined as the "problem of big data" (Cox M. & Ellsworth D., 1997). The ability to organize this "problem" given certain parameters and to be able to build a model or representation of a reality taking care of the existing patterns and relationships to find the true value that lies hidden in data is what can be defined as Data mining (DM) (Kadiyala, S. S., & Srivastava, A., 2011).
According to Edelstein H., (1998). There are three levels of classification in the DM process to consider: Discovery, Predictive and Forensic. Each one can be used in different stages and purposes to add value in a Marketing strategy.
Figure XX DM process schema.
In a nutshell; these processes can be defined according to Rygielski (et. al 2002) as:
Discovery: Analyzing the data in a exploratory way in search for patterns and affiliations where no apparent relationship was before.
Predictive Modeling: Utilization of the patterns discovered in the discovery step to forecast possible future conducts or behavior.
Forensic Analysis: Use of the identify relationships to look for outliers or unusual elements in the data.
Other classifications ...
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... (such as linear regression and logistic regression) or forecasting techniques (such as neural networks and survival analysis) can be used to determine a given result, such as demand forecasting or customer attraction through direct marketing and evaluate their responses.
Other alternatives to predictive modeling in businesses are churn prediction and customer retention. Where the ability to anticipate their decisions; with for example, loyalty programs, ono-to-one marketing (personalized solution) or complaints management. Will allow companies to increase their profitability in industries such as Telecommunications where retention costs are lower as compared to new customers acquisition (Ngai et al., 2009).
Other association techniques can be also used to determine cross selling opportunities, increasing customer lifetime value and profitability over time.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
Target’s data mining is so advanced and precise that they have been able to predict when a woman is pregnant before they know they are pregnant (Hill, 2012). Target collects information about everything that their customers have purchased not only from their stores, but from other sources too (Hill, 2012). This information is then stored and linked to a “Guest ID number” which is linked “…to their credit card, name, or email address…” (Hill, 2012). Target compiled this information along with information from their data bases about women that have created baby registries in the past and found patterns in this data (Hill, 2012). They were then able to analyze their customers to see if their purchasing habits were similar to those of pregnant women, which they were (Hill, 2012). Therefore, they are able to send out coupons to women for products such as scent-free soap, supplements such as calcium, hand sanitizers, etc. (Hill, 2012). Target’s predictions are so accurate, that they are able to predict a pregnant woman’s due date within a small margin as a result of their data mining abilities (Hill, 2012). This allows them to send the pregnant woman specialized coupons for items that she might need during different stages of her pregnancy (Hill,
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data.
...rs since the reward is tangible. Since 80 percent of profit comes from a small percentage of customers, programs should be developed to retain them. Companies will use resources that aren’t available to the entire customer base to ensure they are retaining their most valuable customers and offering incentives to encourage others to move up.
The creativity and ingenious of human beings has enabled the development of technologies that have overall, benefited all of mankind. Arguably one of the most if not the most pivotal man made technological achievement is that of the internet. The internet has allowed for the seeming less transfer of data and information in a matter of seconds. With this innovation has come an increase in communication, enhancement of understanding other cultures, and a mass gathering of data. The amount of data now in existence due to the internet has created the need for big data. Big data has developed as a solution to the traditional computer infrastructure that has become obsolete due to its inability to handle the massive amounts of data now in existence. The benefits of big data are ever expanding and attractive as it can improve the efficiency of companies, research, health sciences yet, the consequences of using big data are just as intensifying and are causing some backlash in many communities. The current issues surrounding big data and the increasingly dependent nature of the world’s people on big data will undoubtedly impact the use of big data in the future.
I found little on this area except for how it can be used for marketing purposes. I then analysed my secondary research and identified gaps, which lead me to develop my primary research methodologies.
Abstract— Customer churn is a business term used to describe the loss of customer. It describes those customers or clients who leave or switch to competitors. In the telecommunication industry, customers have multiple choices of services and they frequently switch from one service to another. In this competitive market, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. So that’s why there is very higher rate of customer churn in telecommunications industry experiences an average of 30-35% annual churn rate. The purpose of this paper is to propose an efficient Customer Churn Prediction Model based on classification techniques, which will help the telecommunication company to predict the customer churn rate to know about which customers are loyal to them.
Attracting focus from firms in all industries, Big Data offers many benefits to those companies with the ability to harness its full potential. Firms using small data derive all of the data’s worth from its primary use, the purpose for which the data was initially collected. With Big Data, “data’s value shifts from its primary use towards its potential future uses” (Mayer-Schonberger & Cukier, 2013, p.99) thus leading to considerable increases in business efficiency. Employing Big Data analytics allows firms to increase their innovative capacity, and realize substantial cost reductions and time reductions. Moreover, Big Data techniques can be applied to support internal business decisions by identifying complex relationships within data. However, it is also important to recognize that much of Big Data’s value is “largely predicated on the public’s continued willingness to give data about themselves freely” (Brough, n.d., para. 11). As previously discussed, much of the content of Big Data is unstructured data from social media sites etc., and so if such data were to no longer be publically available due to regulation etc. the value of Big Data would be significantly diminished.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Companies have transformed technology from a supporting tool into a strategic weapon.”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions."(Davenport, 2006) Data is compiled to enhance business practices. When samples are taken, they are used to examine research and understand how to solve problems or why situations are as they are. Furthermore, in this article, Thomas Davenport discusses analytics from a business standpoint. He refers to organizations that have been successful in their usage of data and statistical analysis. In addition, he also discusses how data and statistics can be vital in the efforts to improve the operations of businesses.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Data mining is the computer-assisted process that digs through and analyzes massive sets of data, and then extracts the meaning of the figures. Data mining has empowered companies to find new opportunities for growth, decisions and the ability to identify customers, trends and purchasing decisions (Rygielski, Wang, and Yen 2002). The data mining results allows companies to save customers prior to departure to a competitor. Second indicator is that the Target’s company strategy includes the phrase “predictive analytics”, which is defined as “Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.” (http://www.predictiveanalyticstoday.com). Target utilizes three primary sets of guest expectations, understanding their needs, deliver relevant messages and offers and contact guest with the right vehicle (Pole 2010). Target purchases the demographics, spending habits and neighborhood data just to start the process of towards those three sets of guest
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.