Data mining benefits for businesses

data mining benefits for businesses is various and much more than these examples and more diverse than from imagination!

• Optimize marketing campaigns: Data mining helps businesses understand which marketing campaigns will likely generate the most engagement, classify customers, display personalized advertisements, and optimize marketing spend.
• Detect possible fraud: Data mining helps businesses detect fraudulent activity and anticipate potential fraud. For example, analysis of point of sale (POS) data can help retailers detect fraudulent transactions. Banks and insurance agencies use data mining techniques to identify customers likely to default on premium payments or make fraudulent claims.
• Make better business decisions: Rather than solely relying on your intuition or experience, insights generated from your own business data can help you make better decisions. For example, intuition may tell you that your product is not selling because of its high price point while data analysis reveals that it’s actually because of fewer distribution channels. Such insights allow your business to identify and dress the underlying issue.
• Insight into employees and HR policies: Data mining not only helps improve external market performance but can also be used to understand employee behavior, predict attrition, and evaluate HR policies.

There are many benefits of data mining, including some specific ones that add value to your business:

Examples of data mining in real-world business scenarios

Giant corporations and small and midsize businesses (SMBs) in all industries can benefit from data mining. The right data helps companies increase revenue, cut costs, and add customers.

Let’s look at some real-world examples of how companies have converted data to dollars.

• The right follow-up strategy helped increase conversions by 40%: Envelopes.com was seeing potential customers routinely leave its website without completing their purchase, and was unsure when to send follow-up emails regarding abandoned carts. An analysis of data patterns revealed that emails sent 48 hours after a prospect left the website returned a higher conversion rate than follow-up emails sent 24 hours later.
• Improvements in product design and marketing drive market share: With most consumers preferring self-treatment for tooth sensitivity pain, a major CPG company wanted to improve the market share of its sensitivity products. The company hired a data analytics firm to mine data from multiple sources including social media and the company’s own AWS database. They analyzed over 250,000 customer responses and identified three main factors directly affecting sales using text analytics, regression analysis, and other techniques.
• Market basket analysis: Market basket analysis uses association rules to identify what items will likely be purchased by individual customers. Amazon’s recommendation engine mines data from user history, purchased and abandoned carts, wish lists, referral sites, etc. to target customers with product advertisements they’re most likely to click on and convert, thus driving sales.