Explain market basket analysis with example

Discussion Question:

Describe one unique and specific example of market basket analysis or DNA sequence analysis where data mining can help. Explain how it would help the retailer or sponsor of the data mining effort.

Discussion Post:

Various businesses are using data mining techniques to better understand their customers behaviors. Data mining helps businesses to engage in successful marketing and sales initiatives by presenting the required information and data in a timely manner. Be it categorizing data and information or making sense of existing data, data mining techniques have proven to help businesses to target even the most elusive market segments (Data Mining Helps You, 2019).

Data mining provides a core set of technologies that help organizations anticipate future outcomes, discover new opportunities and improve business performance. It can be applied to a variety of customer issues in any industry from customer segmentation and targeting, to fraud detection and credit risk scoring, to identifying adverse drug effects during clinical trials (Data Mining From A to Z, 2016).

Market Basket Analysis is one of the most popular and useful types of data analysis for marketing and retailing. The purpose of market basket analysis is to determine what products customers purchase together (Market Basket Analysis, 2002). For example, stores or e-commerce can use data mining to better detect which products people are likely to buy based on their past purchasing habits, or which goods are likely to sell at specific times of the year.

Reference

Data Mining From A to Z. (2016). Retrieved from https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/data-mining-from-a-z-104937.pdf

Data Mining Helps You Understand Consumers Better. (2019). Retrieved from https://transformsolution.com/8-ways-data-mining-helps-understand-consumers-better/

Market Basket Analysis. (2002). Retrieved from https://web.fhnw.ch/personenseiten/taoufik.nouri/Data%20Mining/Course/Case%20Study/PA-Tutorial/mba.html

Discussion reply:

Task 2: 180-200 words with reference discussion reply

Discussion Question:

Describe one unique and specific example of market basket analysis or DNA sequence analysis where data mining can help. Explain how it would help the retailer or sponsor of the data mining effort.

Discussion post:

Market Basket Analysis is one of the main methods utilized by big retailers to discover relations between things. It works by looking for groupings of items that happen together regularly in transactions. To put it another way, it permits retailers to identify associations among the things that people purchase. Association Rules are usually utilized to examine retail basket or transaction data and are anticipated to recognize strong rules discovered in transaction data utilizing measures of interest, based on the concept of strong rules (Li, 2017).

There are many ways to see the similarities between items. These are techniques that fall under the general umbrella of association. The outcome of this type of technique, in simple terms, is a set of rules that can be understood as “if this, then that” (Salem, 2014). Imagine 1000 receipts sitting on your table. Each receipt represents a transaction with items that were purchased. The receipt is a representation of stuff that went into a customer’s basket (and therefore Market Basket Analysis). Using data mining, we can determine that if a customer buys coffee and sugar, then they are also likely to buy milk. Data mining can also permit to understand three important ratios; the support (the fraction of which our itemset occurs in our dataset), confidence (probability that a rule is correct for a new transaction with items on the left) and lift (the ratio by which by the confidence of a rule exceeds the expected confidence). Using the receipts data set, data mining can also be utilized to target items by providing answers to questions such as: what are customers likely to buy before buying whole milk? And what are customers likely to buy if they purchase whole milk?

V/R

JB

References:

Li, S. (2017, October 2). A Gentle Introduction on Market Basket Analysis — Association Rules. Retrieved from https://www.r-bloggers.com/a-gentle-introduction-on-market-basket-analysis%E2%80%8A-%E2%80%8Aassociation-rules/

Salem, M. (2014. March 19). Market Basket Analysis with R. Retrieved from http://www.salemmarafi.com/code/market-basket-analysis-with-r/

Discussion Reply:

Task 3: 180-200 words with reference discussion reply

Discussion Question:

Review the key roles involved in the design of a dimensional model such as data modeler, business analyst, business intelligence application developer, data steward, ETL developer, database administrator, security manager, data warehouse administrator. Select a role and define the tasks that this person performs.

Discussion post:

A data model refers to one responsible for creating, managing, logical and physical data models. These models should show the information requirements required to manage and maintain business intelligence in an organization. They are the senior analysts of the business system In order to become a professional data model, one must be experienced in the generation of data definition languages that are important for the creation of databases, the creation of models that correspond to each environment, ensuring that data structures match models by synchronizing them, being able to test the data definition languages before deploying them, and finally being able to maintain the models repository.

Main responsibilities of data modeler include

a) Creation of a conceptual business model that is designed as part of the information management strategy and should support the management and intelligence of businesses. b) Creation of project logical models that are part of the information management systems during the analysis phase. c) Creation of a logical business model that is created as part of the information management framework and should include all entities and their relationships in the information set. d) Design physical model during the architecture and design phase. These models can include data warehouse and dimensional models that translate the logical model into the physical model. Finally, Data modeler plays a key role during the requirements analysis, architecture and design and development phases (David Bowman’s n.d.).

David Bowman’s (n.d.). Data Modeler roles and responsibilities.http://www.information-management-architect.com/data-modeler-roles-and-responsibilities.html

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Task 4: 180-200 words with reference discussion reply

Discussion Question:

Loshin (2003) identified that high quality data is the most important factor for the success of a data warehouse. Review 2-3 issues regarding data quality regarding data warehouses. Please provide reference and examples.

Loshin, David. (2003). Business Intelligence. San Francisco: Morgan Kaufmann Publishers.

Discussion post:

In his section entitled “Staff the Project” Kimball described the role of the Data Modeller and the important balance that the role needs to maintain in a Data Warehouse project. While you might be lead to believe that this is a very technical and possibly a very isolated role the data modeler does not design independently. The data modeler is deeply involved in the business requirements phase of the process and must be well versed in the needs of stakeholders and business rules. The model they are designing must take these things into account and business rules will define a lot of the model’s features. However the model must also conform to the technical requirements of the data warehouse. Traditional entity-relationship modeling needs to be put aside in favor of the multidimensional model. The data modeler needs to be able to create a model that demonstrates data relationships in a way that both IT professionals and potential system users can understand (Kimball, Reeves, Ross, & Thornthewaite, 1998).

The role of the data modeler is often rolled up into a data architect role. The data architect has emerged in more recent years as a natural extension of the data analyst and the database designer. It’s become a more necessary role as databases and data warehouses have needed to be structured in a way that allows for the collection of data from multiple unrelated sources. The data architect goes beyond just modeling the data warehouse and instead needs to create and communicate an end to end vision of how the data warehouse will be constructed. This goes from logical design to physical design and includes an understanding of how the data flows. This includes data migration and data integration (Database Answers, 2003).

References:

Database Answers (2003). What is the role of a data architect? Retrieved from http://www.databaseanswers.org/role_of_data_architect.htm

Kimball, R., Reeves, L., Ross, M., & Thornthewaite, W. (1998). The data warehouse lifecycle toolkit. New York: Wiley & Sons, Incorporated, John. Retrieved from http://homes.dcc.ufba.br/~mauricio052/Material%20Artigo/The_Data_Warehouse_Life_Cycle_Toolkit_(Ralph_Kimball).pdf

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