Data Mining Businesses Can Receive Many Benefits

Data Mining

Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many companies believe they can use it in order to decide which products will sell most often to which customers (Nisbet, Elder, & Miner, 2009). Association discovery is another type of data mining, and is more involved with the products that are sold and how they match up to specific types of customers, as opposed to specific customers by name or other determination (Nisbet, Elder, & Miner, 2009). In other words, predictive analytics look at what customer A will buy again, but association discovery looks at what customer A will buy based on his belonging to a particular group of customers who also buy a particular product.

Web mining is used to discover information about customers on the web (Hastie, Tibshirani, & Friedman, 2001). Club cards that are used to access a company website, for example, can help a customer be tracked and his or her spending and buying habits can be discovered. Customers may not always realize that this is going to take place, but it is the only way that companies can provide "for you" deals for specific customers who are part of their rewards structure. They have to know what the customers are routinely buying in order to know what to suggest to them. Clustering is yet another way of data mining, and it looks for customers who are related in their buying habits (Nisbet, Elder, & Miner, 2009). If a particular kind of customer buys certain things very often, then it stands to reason that other customers who also appear to be the same kind would buy those same things. Of course, like any kind of data mining it is not an exact science. There are many things that can affect the buying habits of a particular customer.

Some data mining algorithms are more reliable than others (Cabena, et al., 1997). While some can be trusted, others make frequent errors in the way they calculate customer information and what they predict based on that collected information (Cabena, et al., 1997). It is possible, with some study, to predict the kinds of errors these algorithms will probably produce. For each algorithm there are different errors, but they all come down to one thing: correlation is not causation. In other words, it is really not possible to predict why a customer will buy something in every instance. Without understanding the why of the issue, the purchasing of anything becomes a guessing game (Cabena, et al., 1997). A consumer may buy something only once, or because it was on sale, or because a family member was in town and likes that particular product. That does not mean that the consumer will buy that product ever again, and does not tell the company why that product was purchased.

Most of the errors that are made in data mining come from not understanding the issue of why something was purchased (Nisbet, Elder, & Miner, 2009). Other errors can also come from using the wrong kind of analysis to get information. Depending on what kind of information a company wants to collect, a different type of data mining operation may be a better fit for that company. Before data mining is undertaken, a company must spend time understanding its various options and determining what it most wants to know (Cabena, et al., 1997; Hastie, Tibshirani, & Friedman, 2001). Once that has been done, then the company can move toward actually collecting the data and using it in order to give customers of that company a better experience.

One of the biggest issues with data mining is privacy (Nisbet, Elder, & Miner, 2009). When personal data is being collected for mining purposes, there is always the chance that customers will not take kindly to that. There are three main concerns that are raised by consumers. These are whether their information is being sold to others, whether it is being stored safely so that it is protected properly, and what the original company is actually doing with it (Tibshirani, & Friedman, 2001). These concerns are all valid in their own way, and they are also being allayed by companies in several ways. Data protection and encryption is a serious business today, partially because so much data mining is taking place (Nisbet, Elder, & Miner, 2009). If a company wants to collect information but cannot keep it safe, that company will easily lose customers when the breach is discovered.

Additionally, customers are generally more comfortable with companies that state in writing that they are not going to sell any customer information to other companies for any reason (Hastie, Tibshirani, & Friedman, 2001). When companies promise this to customers, it brings a level of trust to the equation that would not otherwise be present. Companies are seeing the value of this more and more, and they know that they have to do what it takes to keep customers because the market is very difficult. Some companies are more exclusive and they have more options with how they handle information - mostly because customers do not have very many other places from which to choose if they want the same products or services. However, other companies are more mainstream and offer things that are also provided by many other companies. It is these businesses that have to be the most careful with customer data and provide information about data mining and handling to their customers, or they risk losing them to a competitor (Cabena, et al., 1997). Some customers will leave for other reasons, but leaving due to data mining problems should be avoided.

Several businesses have used predictive analyses to gain a competitive advantage. Three of these businesses are Walmart, Safeway, and Intuit. It is important to evaluate the strategies of these businesses in order to see where they have worked and where they have not. For large retailers like Walmart, a predictive analysis strategy is very effective. The company knows what sells in most markets, and those things are found in nearly all of the stores because it has been clearly predicted that customers will buy those things if they are available to them. Some other items are only sold at specific stores, because the market there allows for them where it might not in another area of the country. Safeway essentially does the same thing. The grocery giant has stores across a large part of the United States, and they are all relatively similar to one another. There are a few regional differences, but the staple goods are the same and they provide similar pricing structures.