Instructor: Jing Gong

Weekly Question #7 (Due Friday, November 6)

Leave your response as a comment on this post by the beginning of class on November 6, 2015. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!If you sign in using your AccessNet ID and password you won’t have to fill in the name, email and captcha fields when you leave your comment.
Data mining has become a useful tool for businesses. The goal of many data mining applications is (1) to address a complex business problems, (2) to explore an potential business opportunity, or (3) to gain competitive advantage. Think through an application of data mining in the real world or based on your experience. Describe the scenario and how data mining helps the business to achieve one or more of the above goals.

40 Responses to Weekly Question #7 (Due Friday, November 6)

  • I was assigned a data mining project during my internship this summer. The goal of the project was to see how PwC, the company I interned for, could become more corporately responsible in the community. I had to use excel files and lists of local organizations given to me by my supervisor to find the partners of my office and see if they were on the boards of any local organizations. If they were, I also had to see if they also gave money to the organization. If they weren’t on the board, I had to research the organization to see if it would be a good match for PwC to get involved with. This data mining project helped PwC achieve all three of the above projects. With the project, the company was able to figure out where else they could get involved, set up potential business opportunities and to gain competitive advantage over other similar firms in the area who also put a lot into corporate responsibility.

  • I read a book a couple of years ago that explained how Target uses data mining to target pregnant women shopping in its stores. The company tracks which women/families buy baby items, such as diapers, baby clothes, swaddling blankets, etc., and immediately mails coupons to those women’s homes. Target uses pregnancy predicting data mining to try and “hook” young families from the very beginning and create longtime customers. The company expects those families will eventually think of Target as their one-stop-shop for all essential baby items, and then those families will complete more of their essential shopping in Target stores. In addition to baby items, most Targets sell other essentials such as groceries, men’s and women’s clothing (including maternity wear), cookware, electronics, etc.

  • One example of how data mining helps businesses explore potential business opportunities is’s use of product recommendations. Amazon aggregates data about what products you have viewed and then uses that data to continually bombard you with targeted advertising in hopes of you returning to the site and purchasing the product. Another scenario could be that Amazon notices that people are searching for products that they do not sell. They could use this kind of information to branch out and sell the desired product.

    Another example of data mining is the recent creation of car insurance companies that charge your monthly premium based on the number of miles you drive. These new types of car insurance companies use data mined from their customers cars about their miles driven to offer just the right amount of coverage.

  • My real world example is how my fathers business, a pharmacy, focused on improving their profit margins. To do this, we actually used a data mining technique. We sorted through our prescription database to distinguish which drugs current profit margins were likely fads and which ones could be consistent. We then purchased the relevant inventory depending on our assessment. This Is an example of data mining in a pharmaceutical setting.

  • The company that I work for now Franklin Mint Federal Credit Union (FMFCU) and I think they use data mining as a tool to determine to how many account could potentially be opened next year. Looking at the demographic areas and where people are located/living FMFCU can also determine where the bulk of their members are coming from in order to create these type of analysis.

    When they are trying to solve problems FMFCU can congregate all of the data and literally determine most of the robbery issues for the year and what time of year most of their issues will occur.

  • The first thing I think of when I hear data mining is Walmart. Walmart has millions of transactions every day and store that data in their databases. That data would just sit there useless if there was no data mining to analyze it and make use of it. By data mining Walmart can gain a competitive advantage by figuring out sales trends and figure out which campaign strategies to use.

  • When I envision data mining in the corporate world, Amazon is the first company that comes to mind. They have effectively used data mining to improve the service they offer. By analyzing searching and buying patterns of individual customers, they have come up with a recommended feature that is perfect. I, personally, buy many things on Amazon and the recommended feature is very useful as it quickly shows some items I may want to consider purchasing. Many may think of this feature as luring customers to spending more money unnecessarily, but I find it interesting as it increases revenues of Amazon and it was all possible due to the capabilities of data mining.

  • In the insurance industry, data mining is used in order to price insurance products and decide whether or not an exposure is too risky. By using past loss data, insurance companies are able to more accurately determine the risks associated with a particular exposure. This helps those companies to gain a competitive advantage by having more accurate pricing and risk appetite strategies than their competitors.

  • I work as an underwriting intern at an employee benefits consulting firm. In this job I use data mining to sort through data and find the most competitive rates for our clients. In this way I am able to use data mining to gain competitive advantage because finding the best rates for our clients’ employee benefits plans helps to prevent them from going to one of our competitors for consulting.

  • Chase Nicolosi presented on Wednesday 11/4.
    Corinne McCoy and Faiza Samreen will present on Friday 11/6.

    • When I worked for Cophilly I was given the job of collecting, organizing the data; which consisted of student names and emails that pertain to engineering and entrepreneurial students that attend school at UPenn, UoftheArts, Temple and Drexel. Our goal with the data is to spread more awareness of our accelerator start-up programs and get enough people on board to join the Cophilly team. With the goal in mind of in a few years creating an innovation lab that connects students’ engineers and entrepreneurs all over the city into a productive working environment with 3-D printers and various other high tech gadgets and computer software.

    • A real life business application that uses data mining could be a supermarket. Supermarkets have loyalty cards for that helps them gain data on customers preferences on certain items. A large supermarket that comes to mind would be Target. Target recently was able to predict people pregnancies based on items they purchased. The data was very accurate and caused some commotion amongst families who didn’t know that someone in their family was pregnant.

  • The major uses of data mining that I have experienced come from the telecom industry. Large cable companies actively gather data on what customers watch and when they watch it to provide an experience that is tailored to their preferences. These companies accomplish this by having ads and content suggestions that are in line with their tastes, and that occur when they are most likely to be consuming content.

  • Football teams use data mining to evaluate players. They look through the box scores of many games. The reason teams use data mining is to figure out what players are the best and to help gain a competitive advantage over the other teams.

  • Grocery stores use data mining to determine what products are sold the most frequently and what type of person buy certain products. One way they do this is with the use of reward cards. Reward cards collect and store data of all the items shoppers purchase. This explores a potential business opportunity because grocery stores can remove products that don’t sell frequently and rearrange isles based on customer needs. This will generate a higher net income.

  • An example of data mining and its use to address a complex business problem, explore a business opportunity, and gain a competitive advantage would be Pennsylvania Ballet Associations utilization of Temple students to visualize data that’s been mined to solve a business problem. Offering a 19th century product to a 21st century consumer makes engaging this audience difficult. Through the use of data mining regarding purchasing habits and locations enables the PBA to find patterns in the data that can be beneficial to increasing revenues. By assessing the angles students take on the data that’s been mined, PBA maximizes it’s opportunity to find the best possible business practices to address the problem of underutilized markets, find a way to engage these markets, and gain a competitive advantage through the application of the most effective method.

  • Data Mining is becoming a way of life for businesses as it is being used more frequently to help increase overall performance for many businesses including retail store and online sites. Many company can see the patterns for purchases that you have made each visit. While shopping online at your favorite store, as you add specific products to your shopping cart, you will see other items that previous customers purchased in addition to the specific items you are purchasing. The company is able to match products according to purchasing history that is in their system. These suggestion that the store makes available can help increase the the total sale.

  • A common usage of data mining is through evaluating credit card activity. Most businesses evaluate credit cards for several reasons including: tracking consumer behavior patterns to determine who are the most loyal consumers, what are the most popular products (determined by repeat purchases), or being able to catch fraudulent purchases based on suspicious credit card activity. Being able to track activity helps a firm make logical decisions when it comes to spending money on products, security measures, and maintaining a budget for future marketing campaigns to boost sales, because the mining aspect makes any business process more efficient with relevant data.

  • I have used data mining as a technique during my research as a research assistant. I was assigned a project to find a company that could benefit from the analysis of big data. I choose the company Pandora. I utilized open source databases and the web to explore company data from various perspectives in search of constant patterns and systematic relationships between variables. By using data mining I found a potential business opportunity for Pandora, incorporating weather analysis.

  • Through co-utilization of web indexing via, say, web spiders, data mining can be used to enrich/update metadata pertinent to research and/or business systems of interest. Such enrichment or updates can be manifested in various ways: establishment of relevant search criteria, identification of new resources, distinguishing between dissimilar resources, etc. With the concept of addressing complex business problems in mind, data mining can be used to serve various purposes, such as cost-benefit analyses. If a business manager wants to run an efficient and profitable business, an obvious way to nourish this effort would be to cut unnecessary costs and seek out equitable or better alternatives. For instance, by aligning known resources, potentially new suppliers, developing awareness of cheaper materials of equal quality, etc., it is possible that a manager could make beneficial changes to his or her current business approach, in order to facilitate growth or simply solve in-house problems that have taxed management efforts and/or operational cash flows in the past.

    Another issue of note is the preservation of sensitive customer data. Certain forms of data may enable a single organization, or even an entire industry, to streamline business practices and amend potential or active problems. Most notably, hospitals, law enforcement agencies and the like may be interested in certain statistical data that may promulgate heightened efficiency in operations or identify a variety of trends, positive and/or negative. Privacy-protection algorithms consistent with data-protection models, like the k-anonymity model, are interesting concepts that seem appropriate to use in combination with traditional data mining techniques, especially where data is shared. This is especially true where the utilization of sensitive customer/client/patient data requires higher levels of discretion (e.g. medical records, credit information, criminal histories, or any data linking individuals to privacy-protected information, etc.)

  • One scenario in my life where I have experienced data mining is in super markets. I have a membership card to the fresh grocer/shop rite line. The company uses the system to collect data on me and my purchase history. Through this the company can gain competitive advantage over other grocery store lines by having a good customer relationship management plan. They also utilized this data mining to explore potential business activities. After sifting through the data collected through data mining the company can make assumptions about what i will buy in the future.

  • One example of a company using data mining to gain a competitive advantage would be Amazon. Amazon uses information about a user’s previous purchase or search history to predict what products they may want in the future. They use data mining applications to collect this information, then use it to figure out what customers may want to buy. Then after they know what products customers may want they use this information to gain a competitive advantage by targeting advertisements for those specific products to the customers who are likely to purchase them.

  • One application of data mining I have encountered in my life is Amazon’s use of “Suggested Products” or products “frequently bought together”. Amazon uses data about what items customers frequently buy at the same time, or what products customers buy after buying an initial product. By doing this, Amazon can spot buying patterns that allow them to recommend items to other customers who are making similar purchases. By doing this, Amazon gains a competitive advantage by being able to better cater to customers by being helpful. Furthermore, Amazon creates a business opportunity because suggesting useful products can lead to sales that otherwise would not have been made.

  • A real life example is Amazon. They use advanced mining techniques to drive their functions like ‘People who viewed that product, also liked this’. In an insurance company, considering whether or not they should a customer or not, they can build an application to predict the customer’s expected risks and loss.

  • I haven’t had any experience yet with data mining outside of this class. Based on what we’ve done with MySQL I can imagine what the a real life scenario can be like. Example, If Itunes wanted to know which songs or artist were selling better they could use data mining to find this out. By doing this they can explore a potential business opportunity. This opportunity may be that if they found out that Adele was their most popular artist they could reach out to her and see if they could be the first people with her knew album or leak a song from the album to get the public excited for the full album.

  • I purchases a lot of products on Amazon and Amazon utilized data mining to gain a competitive advantage. One example of data mining Amazon offering me a list of recommended products based on my past purchases. Amazon uses algorithms to track my buying behavior. This create a better online shopping experience for me.

  • A data mining example I can think of is from Google. Google tracks and collect data of users browsing using their web browser, Google Chrome. They then place web advertisement based on the user’s browsing history. Google’s data mining strategy help address a complex a business problem in advertising where advertising to an audience that have completely no interest is a complete waste of money. This strategy allows Google to give advertisers a platform for a more focused advertising where the users who see them have at least some interest thus giving Google a competitive advantage.

  • Data Mining helps Walgreen with sales, supplier, customer service, revenue and etc. Data mining help spot the sales trend. Walgreens have many suppliers like Coca Cola, giving us permission to access supplier’s data and products analysis. Customers services help us track the customer’s delight score. Revenue to keep track of our profit’s up and downs.

  • Amazon is a great example of how companies use data mining to predict what other products and service you will purchase based on prior purchasing history. For example, I go on Amazon a lot to buy electronics , so every time I go on there , the company uses my prior purchasing history to offer me new electronics I might be interested in.Indeed, this has driven me to make purchases I wouldn’t have normally made. From personal experience, I believe that Amazon uses data mining in this particular way to gain a competitive advantage over their competitors.

  • Retail business does alot of data mining to answer all three questions listed above. However, number three in regards to data mining helps them the most because stores like Target and Walmart target certain sales to tailor with their comparative advantage. For example, retail stores tend to put diapers and baby powder in the same section of there stores so people can buy both since both items are complements to each other. Retail stores only find this out though however when they data mine all their sales and correlate which goods sell the most and which goods they have a comparative advantage in. Once retail stores find this out; they can put up sales for those goods they have a comparative advantage in and gain more income then their competitors.

  • One goal of data mining that I have used personally is using atlas which is a qualitative data in showing graphing material for the Philadelphia health department. We were trying to solve a problem of why certain recreational pool pH levels would be higher and unsafe for pools compared to different factors that we had taken into account. We were able to factor in the certain times of day that a pH would be higher compared to different factors of the weather in order to see why the pH levels would be higher. It allowed us to make pools safer and made the health inspections for the department preform more efficiently.

  • I heard of a case, some shops sale beer and dippers and put the beer shelves near diaper shelves. Because research find that the young father always has to help their wives buy diaper for children, especially when they after work. However, for those young father, they want to drink some beer,too. When they buy diapers in the shop, they usually buy some beer for themselves. To some degree, put those two products together will help to increase potential sales.

  • One experience I have with Data mining is with my Job at dave and buster. Sometimes I analyze the frequency of the games that are being played. The information tells us which games are the most popular and which games are not so great. If a game falls under a certain threshold the staff decides future plans with the game, whether it is to lower the cost to play, move it to a more selective location, or plainly just get rid of the game/replace it. Doing this may stimulate more sales and may gain more attraction for the game and company.

  • An example of companies using data mining is the seasonal hiring of employees. For two summers I worked in the paint department at Lowe’s, and while it may seem obvious that Lowe’s attracts more business in the summer, Lowe’s can actually use data mining to know when to begin hiring seasonal employees and when to begin letting them go in order to save payroll. In my case, Lowe’s was actually able to use data mining to know when and where to schedule me during a shift. Primarily hired in the paint department, I would frequently be moved to help cover home decor departments past five o’clock, as Lowe’s knew that contractors were done buying paint for the day, and most “do-it-yourself” customers were coming in after work or dinner for information or to make orders for their home renovation projects such as new blinds, or flooring projects, and so on.

  • Amazon tracks your searches on the internet and uses data mining applications for ad personalization. This solves the business problem of not knowing what customers are looking to purchase. This achieves the business opportunity of selling more products because the advertisements are meant to meet personal interests. Sales go up and the competitive advantage is knowing before your competitors what customers are looking for and being able to market it to them faster.

  • One real world application of data mining is the use of it by police forces and other crime prevention organizations to spot trends / make predictions about where crimes will occur, so they can more effectively deploy their manpower. I would say this falls into the first category, of addressing complex business problems. While not technically ‘business’, it is very similar, and apparently works to great effect.

  • An example of data mining in the real world is retail stores. A retail store in particular that does this is the clothing store, Express. They give customers points based on their purchases in a given month, your points accumulate over time and they offer higher rewards for people that purchase more from the store as an incentive to keep high spending customers, high spending. The point system has different levels depending on how much you shop at Express. They offer discounts to lower spenders as well but show that customer what they would have gotten had their points been higher. They also send more emails to customers that spend less than those who spend more to try to get the lower spender to spend more at the store. Their efforts in data mining has been very beneficial in their company and keeps customers spending more than they did before.

  • Data mining can help a business achieve a competitive advantage in that it a business can drive consumers to their specific store. They can do this by offering a rewards program or coupons to their location via mailings or electronically that may convince a consumer to shop at their store over the competition. Essentially, customer behavior can be targted as to who they should reach out to.

  • I interned for a tech startup company called AtmosFi during the summer. It was at this time that data mining came in great use. Let me give you the backstory first. AtmosFi is a company that helps small businesses grow their customer base. The way they do this is by collecting customer information through wi-fi. AtmosFi would provide the business with their own router, and every time someone connected to the wi-fi, we would gain access to customer information, such as emails, etc.

    Based on the data we collected, we were able to analyze certain trends in customer behavior, such as times of high traffic, what products most customers bought, the busiest times of the day, and other similar information. We would then provide periodic reports to the business to show them where they could grow and what they should be focusing on.
    Although I wasn’t too familiar with how data mining worked, I saw that my colleagues were able to identify certain trends about certain businesses by analyzing the data. This gave the business a clear competitive advantage.

  • What comes to my mind when talking about data mining is the ads that most of us see on the side areas of our Facebook page. I noticed that when I was in the market to buy a used car, looking on sites such as Auto Trader and, similar cars to the ones that I had looked at or been interested in would appear on the sides. This actually led me to take a test drive in one of the cars that popped up in the ads. They are clearly collecting a vast amount of data, based on peoples’ browsing, and I’m sure that it helps their “pay per click” ad revenue immensely.

  • The basketball team can use the data mining to figure out the exact data of every teamplayer, and know who are in the raising and who are not. Then they know how to arrange the strategy and which players should on the board.

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