Data Mining & Trade Spend

According to Trade Promotion Management & Protiviti, trade spend (promotions and coupons) is the 2nd largest expense to companys today, averaging 15% to 32% of gross sales.  Because of this, it is imperative for todays business to get the most bang for their trade spend buck.  However, doing this requires much more than simply assuming you know the wants and needs of the consumer.

Todays most competitive companies employ highly indepth data mining tools and tons of historical data to allow them to model the spending habits of their consumer base.  By using information such as SKU sales analysis, trade promotions offers, sales volume data, and demand forecasts, these companies employ many of the commonly used algorithms that are synonymous with data mining to prepare a trade marketing plan to capitalize on their trade spending.

Some of the most commonly used data mining tools for a company doing this kind of analysis would include regression trending, classification, and segmentation.  Using regression trending, business can identify which products are no longer selling, and may opt to attempt to boost sales to previous levels by applying trade spend to these items.  By using classification, companies can group customers based on the types of products they purchase, and focus specific marketing and trade spend campaigns towards groups they are more likely to succeed with.  The use of segmentation algorithms will allow the company to clearly draw lines between different groups of customers who like or dislike types of products.  By the use of these tools, a company is able to develop a much more detailed and definitive trade spending plan, rather than over saturating the market with coupons, and obsorbing the cost of the discounted sales.




Source: http://www.tpmaww.com/base/document/Presentation/2007.02.Protiviti_Linda_TPMA%20Feb%202007_Risk%20of%20TPM.pdf

Data Mining And Lean Manufacturing

In todays economy, there is a overwhelming focus by many industries to make a shift to "Lean Manufacturing".  What few people seem to consider are what tools are needed to make this transition.  One of the key components to a solid transformation from traditional to lean manufacturing is a solid database system and good data mining skills.  Although most of the data mining is on a much smaller, and less automated scale in the lean manufacturing environment, all of the concepts correlate.

In Lean Manufacturing data mining plays a critical role in many of the successes or failures a company will have.  One of the key tools of lean, and the simplest form of data mining, is the use of production charts, showing the trends of the lines, the quality (and anamolies), and other useful information.  These tools are a series of data, being collected, organized, presented in a specific format/view, and analyzed, for the benefit of the company (the definition of data mining).

Another example of the use of data mining within Lean Manufacturing would be its use to help smooth the supply chain.  An example of this can be found when attempting to implement a "Just In Time" delivery system for products coming from your vendors.  Using historical data, you can mine the data to allow you to see on time deliveries, any anamolies to delivery time frames, thusly allowing a company to work with their vendors to correct any issues in the supply chain causing these delays.

Although these are only a few of the many uses of data mining in the manufacturing world, these examples do help to paint the picture of the role that data mining plays in the success of any major manufacturing firm on the globe. 

Source: http://www.northhavengroup.com/pdfs/PartitionInSixSigma.pdf

Data mining and Statistics

Data mining is an art of learning from statistics and being able to find patterns in the data. Data mining  sits at the interface between statistics, computer science, artificial intelligence, machine learning, database management and data visualization (to name some of the fields), the definition changes with the perspective of the user. This article give an you an understanding what it is and how it can used.

Check it out.

http://www.tdan.com/view-articles/5226

Data mining used for consumer and Suppliers

From reading this article from a college professor at UCLA, Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations.It helps them with internal and external factors that are related with the relationship with the consumer.  For example some of the internal factors are price and promotion for the consumer as external is delivery and public image.  Businesses also data mine to help suggest other relative items of theirs to customers. Two examples they used were:
     "Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures."

I also found out that WalMart is pioneering massive data mining to transform its supplier relationships. "WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse."  The information they obtain to manage local store inventory and identify new merchandising opportunities.

http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm

History of data mining

Turns out the word data mining is a new phrase that started in the early 1990's.  Data mining can be traced back to three family lines within the business.  The first family line is classical statistics. Classical statistics are pretty much the ordinary concepts we learn in school such as regression analysis and standard deviation.  The second family line is artificial intelligence.  This discipline was brought to apply human-thought processing to statistics.  Artificial intelligence was brought up til the 80's due to the lack of technology.  The third and final family line is machine learning which is described as the joining of statistics and artificial intelligence.  Machine learning was how businesses knew how efficient machines/labor were and how to cut production costs to achieve maximum profit.    

From those family lines came Data mining which is the technique of studying past and present statistics to better progress the business.

http://www.data-mining-software.com/data_mining_history.htm

Privacy in Mobility Data Mining

This article covers the research on two types of privacy for mobile data: privacy in location based services, and anonymity in the publication of personal mobility data.

Data mining report privacy issues emerging from certain applications and sensing systems.

Data mining possesses techniques that allow us to make supporting decisions in urban planning, intelligent transportation and environmental pollution.  In conclusion, privacy methods are crucial for protecting our data from privacy threats.

If you would like to know more about privacy in mobility data mining, please visit the following site:

Privacy in Mobility Data Mining

www.sigkdd.org/explorations/issues/13-1-2011-07/v13-01-3-gkoulalas- divanis.pdf

Data Mining

Data mining has a direct influence on the best practices for knowledge discovery in the real-world data applications. Data mining is capable of managing real-world data such as life science industries.   

However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches.

According to these discoveries, data mining can be classified in several ways (i) data mining on engineered systems or systems designed by nature  (ii) explanatory or predictive data mining, (iii) data mining from static data or dynamic data, (iv) user operated or automated data mining

http://ieeexplore.ieee.org

Data Mining Resources

If your career involves data mining, which I'm sure it does somehow- directly or indirectly, then you need to pay KDnuggets.com a visit.

www.kdnuggets.com

KDnuggets is an extensive collaborative resource center for all things data mining... Analytics software, jobs, consulting, courses, and more. KDnuggets also fits into the world of social media in that you can connect via facebook and twitter, an important feature of any successful website that wants to maintain a relationship with its users.

In my opinion, the most valuable resource on kdnuggets.com is Consulting page, where you can find expert advice on data mining, web mining, and analytics. Each advice column is complete with a bio and photograph on the professional who wrote the article. This adds a personal and credible vide to whatever information it is that you are exploring.

In conclusion, if you've taken the time to read this blog you are probably interested in data mining. This being the case, you need to check out kdnugget.com when you get the chance, it's a highly valuable resource.

Data Mining

http://www.thearling.com/text/dmwhite/dmwhite.htm

The above article gives an in depth summary of what data mining is, how it is useful, and what kinds of tools we can use to utilize it.  From a broad view, data mining is simply what it sounds like: digging for information that is not obvious to the naked eye.  This involves using MIS tools to analyze vast amounts of information for specific variables.  Corporations can use data mining to gain specific consumer research information that may be necessary for marketing a new product or solving a unique tast at hand.