Reality of Data Mining in the Real Business World
Published by : Industrial Automation
For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.
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.
Data mining trends and latest developments
While simple pattern detection and regression techniques have been widely used by businesses for a long time now, the large volume of unstructured data, scattered data sources, and poor data quality have made data mining challenges.
Here are some of the latest data mining trends and developments:
• Big data and multimedia data mining: Data comes in many forms—text data, audio files, images, and videos. Gathering this data, cleaning it, and running models require the latest tools such as text mining or speech analytics software.
• Security and privacy concerns: Data mining by gathering sensitive client details—often without necessary obtaining the necessary approval or sharing rights—has led to increased concerns about data security and privacy. Regulations such as GDPR have reduced the ways in which businesses can use and store consumer data.
• Distributed data mining: As data is stored in multiple locations and devices, sophisticated algorithms are being developed and used to mine data from these locations and generate reports.
• Geographic and spatial data mining: This type of data mining extracts geographic, environment, and astronomical data to reveal insights on topology and distance. This is especially useful for the travel, navigation, and government sectors.
Key data mining software applications include Alteryx, Grepsr, Mozenda, Periscope by Sisense, and RapidMiner among others.
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