Businesses using Big Data to try and grow their companies are quickly learning that collecting the information is only one-half of the equation. Once they have all of their data, the next key step is trying to make sense of it all.
One way businesses can turn the information into something useful is through data mining. Data mining is a process used to analyze raw information to try and find useful patterns and trends in it.
Jean-Francois Belisle, director of marketing and performance at the digital agency K3 Media, describes data mining as the process of discovering insights in large datasets by using statistical and computational methods.
"A data miner is like (the magician) Criss Angel that will make appear from your messy ocean of data, insights that will be valuable to your company and may give you a competitive advantage compared to your competitors," Belisle wrote on his website.
Because businesses are collecting data for all aspects of their operations, data mining can be used in a variety of ways. In Herbert Edelstein's book "Introduction to Data Mining and Knowledge Discovery," Third Edition (Two Crows Corporation 1999), he writes that innovative organizations worldwide are already using data mining to locate and appeal to higher-value customers, to reconfigure their product offerings to increase sales, and to minimize losses due to error or fraud
"Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions," Edelstein writes in the book.
Data mining tools
In order to take of advantage of everything data mining has to offer, businesses need to use specially designed software. Numerous vendors offer them; businesses can also build their own program to fit their specific needs.
"Data mining applications help users discover correlations and connections within large data sets," Software Advice writes on its website. "These might have gone unnoticed without these algorithms."
The software gives businesses the ability to speed up discovery with semi-automated analyses, break up customers into groups based in similar activities and demographics and predict future trends.
When searching for data-mining software, Software Advice advises businesses to consider several factors, including:
- Whether they want a stand-alone, best-of-breed data mining application, or would prefer to go with the data-mining module from their existing Enterprise Resource Planning (ERP) provider.
- If they want to invest in new hardware or take advantage of cloud capabilities
- If they have employees with the right skills to analyze the data.
In a survey by the business analytic and data mining website KDnuggets, some of the most popular data mining software options are R, Excel, Rapid-I RapidMiner, KNIME, Weka/Pentaho, StatSoft Statistics, SAS, Rapid-I RapidAnalytics, MATLAB, IBM SPSS Statistics, IBMS SPSS Modeler and SAS Enterprise Miner.
Some of the top options that Software Advice recommends include Prism, BOARD Management Intelligence Toolkit, Necto, Tableau and GoodData,
Data mining techniques and examples
What makes data mining such a popular tool among businesses are all of the different ways it can be used. Nearly every aspect of a business can benefit from the information data mining provides.
Data analytics firm KISSmetrics believes there are a wide variety of ways businesses can use data mining to increase customer loyalty, unlock hidden profitability and reduce client churn. In a recent blog post, the company outlines the various ways to use data mining to get a competitive edge. They include:
Basket analysis: Also known as affinity analysis, this uses the data on products a customer bought in order to help brick-and-mortar stores improve their layouts or help online stores recommend related products. It's based on the assumption that companies can predict future customer behavior by past performance, including purchases and preferences.
Sales forecasting: This looks at when customers made purchases, and tries to predict when they will buy again. Businesses can use this type of analysis to figure out complementary products to sell.
Database marketing: By examining customer purchasing patterns and looking at the demographics and psychographics of customers to build profiles, businesses can create products that will sell themselves.
Merchandise planning: Brick-and-mortar businesses looking to expand to new locations can evaluate the amount of merchandise they will need by looking at the exact layout of a current store. For an online business, merchandise planning can help determine stocking options and inventory warehousing.
Card marketing: Businesses that issue credit cards can collect the information from their usage, identify customer segments and then use that data to create programs that improve retention, boost acquisition, target products to develop and design prices.
Call detail record analysis: Businesses that use telecommunications can mine that data to see use patterns and build customer profiles. They can then use that information to construct a tiered pricing structure to maximize profit.
- Market segmentation: Data mining can be used by businesses to segment customers by age, income, occupation or gender, which is helpful for email marketing campaigns and SEO strategies.
There are many examples of grocery store chains using data mining to their advantage. In a lecture to his students at UCLA one professor gives a popular example of how one grocery store chain discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays.
The professor said the grocery store could use this information in various ways to increase revenue, such as by moving the beer display closer to the diaper display and making sure beer and diapers were sold at full price on those days.