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Saturday, August 30, 2014 |
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Here's another great video explaining what is predictive analytics? Predictive Analytics (aka data mining) looks at the data you have about customers or other entities, and using techniques such as linear regression, forecasting, and clustering, seeks to predict future outcome - whether it's whether the customer will churn, whether he will accept your marketing offer, and so forth.
The video incidentally was by KXEN, a US company that was founded in 1998 and subsequently acquired by SAP in 2013.
So here's the video on what is predictive analytics:
Note: the posts on this blog may contain affiliate links. Please also see Disclosures and DisclaimerLabels: data mining, predictive analytics, vid, video, videos |
posted by Rick @ 6:54 AM
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Friday, August 29, 2014 |
What is data mining? |
Have you ever noticed that when you search for some item, etc such as a laptop on Best Buy or other sites, somehow the ad for that laptop appears on other sites you may visit? How does that happen?
Similarly, when you purchase a book on Amazon.com, Amazon's recommendation engine suggests other related books? How?
Well, it's all thanks to data mining - or as many people call it these days - predictive analytics.
Data mining is actually a broad term referring to the process of using statistical analysis, database technology, modeling techniques, and machine learning to analyze vast amounts of data in an
automated fashion. The purpose of data mining or predictive analytics is to identify any patterns that you may not know of. Basically, you want to see if the historical data you have about
customers, vendors, etc has any predictive information in it. For example, companies like Walmart, Amazon.com or Best Buy have huge amounts of customer data in their data warehouses. Data such as:
- Where their customers live
- What products they tend to buy
- How much they spend on different categories and so forth.
Based on this data, using various data mining techniques, these companies try to identify what different customers are likely to buy and so forth. They can accordingly information about the
products these customers are likely to buy, send coupons and other offers to their customers. All this is made possible, thanks to data mining. While not 100% perfect, data mining can increase
the likelihood of what a company is trying to accomplish - whether it's to increase customer retention or identify products most likely to sell during upcoming holiday season and so forth.
Here's a good video on what is data mining:
Note: the posts on this blog may contain affiliate links. Please also see Disclosures and DisclaimerLabels: data mining, predictive analytics, videos |
posted by Rick @ 8:33 PM
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Disclosures |
This site, including blog posts, may contain reviews, links, and advertisements for products, services, courses, and so forth of companies of which we may be an affiliate member or for which we may get paid for referrals. This site may also contain blog posts for which we are paid, as well as blog posts regarding products or services of companies from which we may have received complimentary products or services. Additionally, I am an Amazon.com affiliate as well as affiliate of many other firms. The book reviews posted on this site may be based on books that I may have received free. Labels: disclosure |
posted by Rick @ 7:50 PM
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Disclaimer |
This content on this site expresses my own personal views and opinions, and is not meant as a professional advice. I am not rendering any financial, accounting, tax, legal, or any other advice. You are responsible for your own financial decisions. You should consult with appropriate attorney, accountant or other appropriate advisors on all matter related to financial, tax, accounting, legal or other obligations and requirements. I make absolutely no warranties, promises or guarantees of any kind and disclaim any liability, loss or damage that may occur to you directly or indirectly based on the information provided on this site. Labels: disclaimers |
posted by Rick @ 7:49 PM
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posted by Rick @ 7:47 PM
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