While there are many placement options that we should consider, we decide to look for any correlations between the row a product is placed on and its sales. Since we have our data stored in a data warehouse, it is easily accessible and responds quickly to our data request. Consider each of the following: What Judgments can you make regarding the placement of each type of product being considered? Answer - I think that we are more likely to place those items that are in higher demand by customers and those items that the company wants to generate the greatest profit from on the shelves that have the best sales What is the consequence of making the wrong choice? Answer - Profit decreases, inventory doesn't turn over What types of products do you think each of the product groupings represent? Answer - Most likely to sell/greatest profitability to least likely to sell/lowest profitability What target markets can you associate with each product group? Answer - ? Example 1: Our data mining program has performed association analysis and has generated a listing of items that are typically purchased together. Two sets of items currently have your attention: o Peanut Butter, Crackers Peanut Butter, Vanilla Wafers How can you use this information?Answer - You can use this information to predict sales.
If products are complements (as in this case), when the price of a complement goes up, the sales of that item go down and vice versa. Example 2: Because we can track our customer's purchases over time, we are not limited to analyzing individual "market baskets". Instead, we are able to perform a richer analysis of their cumulative market baskets over time. Our data mining program discovers the following item sets with significant support: Item set 1 Batty Take Apart Toy Amazing Airplanes children's book by Tony Mitten Child's Pilot Hat Stomp Rocket Jar.Glow Kit Item Set 2 Biggest Green Tea Cozy Chamomile Nighttime Tea The Smarter Science of Slim book by Jonathan Bailer Item Set 3 Being George Washington by Glen Beck Killing Lincoln by Bill Reilly Original Intent by David Barton Answer: We can use this information to strategically locate items in the store.
Group these items together and you may increase sales. We can also use this information to predict sales as the prices change. Example 3: We use our data mining program to analyze local buying patterns. We discover that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer.Further analysis shows that these shoppers typically did their weekly grocery shopping on Saturdays.
On Thursdays, however, they only bought a few items. How can you use this information? ... For product placement? For promotions? Answer: If people do more shopping in Saturdays, then promote and position products strategically on that day. Problem 3: Market Basket Analysis: Concept Tree/Sequence Analysis A local home improvement store has performed data mining and has identified the following incept tree which corresponds to a deck.
The concept tree defines a "deck" in terms of the products that are sold at the store. What does this allow you to do when analyzing market basket data? Answer: This allows you to visualize and determine every piece that will be required to complete a deck After we were able to build this concept tree, we also perform sequence analysis on our past purchase data and have found strong support for the following sequences of purchases. Deck -> Outdoor Furniture -> BBC Deck - > Flowers/Landscaping upsetting and cross-selling Problem 4: Decision TreeAnswer: You can use this data for In the past, your company's loan department paid their staff to analyze the credit worthiness of individuals?I. E. , to accuse winter or not an Uninominal snouts De approved Tort a loan.
Over ten years, a large amount of data was generated related to this process. The data set contains information about the applicant, about their loan, and whether the individual met their responsibility to pay the loan back with regular monthly payments. This data has recently been used by data mining processes to look for patterns that determine an applicant's credit worthiness.The following decision tree was produced as a result. How can you use this decision tree? Answer: The decision tree would allow the staff to quickly make a decision about whether an individual is credit worthy Receivables: How could this concept be applied too different scenario? What is typical of a company's handling of receivables? How could a decision tree that predicts payment defaults be helpful? What about situations where you are already having problems collecting what is owed to you? O What if, based on past data, we could predict what you should expect to collect? What if you could determine what collection strategy has worked best in the past for this particular type of customer? Consider the 100% Stacked Column charts below which compare 3 different approaches to collecting past-due debt for a particular type of customer.
What are the differences in the approaches? Which approach would you use? Video Rental Chain: You are the manager of a company specializing in video rentals (similar to Red Box). By applying data analytics procedures to your customer data, you have found that your customers form a number of clusters?as pictured in the figure.Consider each of the following: What type of data would your company have available to perform this clustering analysis? Answer: We have segmented customer information, which helps to identify customers with similar behavioral traits. How might you utilize the results of this analysis to benefit your organization? Answer: It would help to understand the customers' preferences and demand in each region.
It can also be used to determine the relationship among customer gender, occupation, age and video categories.Favorite videos and personal preferences of customers can be determined and video rental stores can recommend arsenal favorites to each customer. Will you be able to offer these benefits with existing business processes or would they need to be modified? Answer: These extra benefits can be offered with existing business processes. As manager of a credit card company, you have worked with your data mining group to perform cluster analysis on each customers' transaction data. For one customer, you have identified the clusters shown in the figure below.
Answer each of the following questions. How could this possibly benefit you?Answer: You could understand what transactions are most important to various roofs of customers and then further refine business offerings strategically What benefit might you be able to offer to your card holders? Answer: You could tailor services and adjust costs to fit card holder's needs Think about what item u In ten lower let nana corner malign represent, Ana now It Decodes relevant. Answer: This would be a transaction type that no one utilizes - if significant costs are associated with maintaining this service which nobody wants, then the credit card company could save significant money by eliminating the service
