For companies of all sizes, data mining involves the conscious effort to unearth “hidden gold” in their databases — zeroing in on the most interesting or valuable data. With the huge amount of data confronting both small and large companies alike, “digging through data” requires specialized approaches such as “rule induction” to make sense of both qualitative and quantitative data. With this article, Research Optimus shows how rule induction can help you gain a better understanding of consumer purchasing behavior — and also improve your inventory management and marketing promotions.
How to Discover New and Interesting Patterns in Databases?
One of the major forms of data mining is “rule induction” — extracting formal rules from a set of observations. For example, the observations could consist of looking at all items in the shopping cart of your customers for a given day. Rule induction helps you find “association rules” that bring relationships that would otherwise go unnoticed to your attention. As one illustration, Wal-Mart’s early data mining activities (in 1997) revealed that buyers of Barbie dolls are most likely to buy one of three specific candy bars.
Without data mining and rule induction, it is unlikely that you will discover many buying patterns with the “naked eye.” Just as gold is hard to find without knowing where to look, your business database often has “hidden gold” in the form of hard-to-find marketing relationships. Rule induction and data mining are key tools you can use to dig deeper into your business data. Once you identify such buying patterns by using rule induction as Wal-Mart did, you can do a better job of cross selling products to your customers.
Benefits of Applying Rules for Predicting Buyer Behavior
Rule induction continues to be the best data mining technique for predicting buyer behavior after analyzing everything in your business database. A major benefit of rule induction systems is how automated they are — this makes them easy to use without spending a lot of time. The “old-fashioned” alternative would require “number crunching” by several marketing analysts. Because consumers will periodically change how they make purchase decisions, it is equally important to use strategies that reflect these changes quickly. As buying patterns change, rule induction algorithms can be modified to coincide with current consumer behavior.
Patterns Can Be Uncertain, But Never Give Up!
Despite the marketing potential of data mining and rule induction, uncertainty will continue to be a factor in any business analysis process. Even if the “rule” says “If X and Y happen, then Z is likely to happen,” Z will not always happen. The “accuracy” or “confidence level” of the rule will depend on factors such as the size of the database. But do not expect any rule induction process to result in a “guaranteed” outcome all of the time. Think of comparisons to flipping a coin in which overall probabilities of either heads or tails might be equal — but this does not mean that each individual outcome can be accurately predicted with certainty.
Rules Are Safe and Do Not Imply Causality
Identifying buying patterns and relationships is not the same as determining what “causes” someone to buy a product or service. For example, the likelihood of someone buying cheese and then wine does not mean that the act of purchasing the cheese product actually “caused” someone to buy wine. Such a rule simply describes a probability that these two products are often purchased together rather than establishing causality.
Setting Rules for Continued Discoveries
With so much data in today’s business databases, it is not uncommon for business owners and managers to be somewhat intimidated by what to do with all of it. Developing rules via data mining and data induction is a practical and cost-effective solution to reduce “data anxiety” in many common situations.
With proper data management techniques, your database can provide a combination of macro and micro views — looking at the “big picture” with some rules while not overlooking important (but much smaller) subsets with different rules covering a smaller number of situations. For example, looking at the “small picture” (micro-level data analysis) allows you to monitor a small but growing subpopulation that might reflect a new competitor or a market shift.
How Do You Identify Buying Patterns?
What do you currently do to forecast buying behavior? Have you used rule induction in databases? What are your experiences? Please share your thoughts by using the social media buttons and leaving a comment.
If you are uncertain about the potential of data mining for your company, please get in touch with Research Optimus — data mining and research experts who can save you time and money.
– Research Optimus