What can your data tell you, that you don’t already know?
Data Mining is about explaining the past and predicting the future by means of data analysis.
We are collecting more data than ever before, yet many organizations are still looking for better ways to obtain value from their data and compete in the marketplace.
How old are my customers, did they go to college, do they have children, where do they live, where do they shop, what is their annual income, etc? I bet you can answer most of these questions with your existing data! Have millions of records, that my specialty!
The stocking layout in a grocery store has been designed to increase the dollar amount you purchase. Cereal is not next to milk, so you have to walk past other tempting items to buy both. This was designed using the analysis of data from shopping receipts.
How can my data save or make me money?
By only marketing to those customers that have a higher propensity to respond. Look at the face of your customer, do you really know them? Let me turn your data into increased revenue.
Consumers pursue good value over lowest prices when it comes to retailer choice – A Nielsen Report October 2011. A Nielsen Report October 2011:
Need to create a large database? I can assist you to ensure you set it up correctly the first time. (Table design, metadata, SQL and MySQL experience, SSAS and Cognos )
Learn how to build Cubes in SSAS (click here)
Bill Inmon vs. Ralph Kimball: These two data warehousing heavyweights have a different view of the role between data warehouse and data mart.
Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
For example, Red Box mines its video rental history database to recommend rentals to individual customers. Credit Card Companies can suggest products to its cardholders based on analysis of their monthly expenditures.
Data mining analyzes relationships and patterns in stored transaction data, making decisions more cost effective and efficient.
I talk a lot about data mining but data analysis is more than just mining, what about marketing analysis?
You would not sell snow shovels in Florida, would you? Marketing Analysis takes a BIG-picture approach to the measurement of your marketing, it is not just one thing, it is everything… The time of day you did things, the channel you used or the conversations offsite, or the engagement that resulted from those efforts? Marketing analytics is the act of looking past mere website results, and asking yourself, “How did that marketing campaign really go?”
Data Science: More Than Mining
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