Data Mining Project : Using Customer Segmentation

The management team of Kudler fine Foods uses the intranet to keep demographic data on each of its customers. They can access their names, addresses and important data for special mailings. The inventory is managed and the availability of ingredients can be determined. The items from a store can be obtained from what has been purchased. An order table can be used to record pertinent information. The order line can be seen to show the description, quantity and price. The store table is used to retain data for each store. The supplies table contains data information from tax table is used to provide accurate taxes. The tender table is used to provide a record of payment for an order.

Kudler Fines Foods founded in 1998 and is growing rapidly. When they open their first store, Microsoft Access was selected as the database to track inventory, orders, and customers. The products, they sold are perishable and inventory must be properly tracked. Kudler excessive inventory is always a concern. This paper will evaluate database tables from accounting perspective, provide recommendation for improvement and create pivot table to help Kudler's management to improve decision making process.

This set of data belongs to the online retailer industry. The most significant categories that helped with our decision was the low inventory for a retail business and the relatively high inventory turnover. The reasoning behind the high inventory turnover was because the goods were allowed to sit in storage until sold because of the online aspect of the business. We were also able

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

As soon as the company’s goals of the analysis are decided, an analysis procedure will be selected to support the company’s analysis goals. Collecting data from multiple business systems and filtering through the data, then integrating them into a single place such as a data warehouse is usually the case with data analysis. As the data is analyzed patterns and relationships within the data will be uncovered, which in turn will bring forth more questions to be asks and the analytic process will continue until the company’s goals are met (Rouse, 2010).

As a potential manager it is important for me learn from these examples. For Amazon to have this level of detail and to be this thorough is clearly the future of retail business. Moreover, even if a person decides not to manage a retail business this article is still a good learning tool, in that it shows the importance of thinking outside the box and the level of commitment needed to be successful.

After this analysis, we are going to take a look at the characteristics of the Chinese retail

Exploiting the customer information hidden in large database can help identify valuable customers and predict future behavior, enable the company to become proactive with their campaign, implement knowledge-driven decisions and make it possible for the organization to limit the defect rate.

“I don’t know the key to success, but the key to failure is trying to please everybody.” (Bill Cosby)

Huge amounts of data are collected nowadays from different application domains and are not feasible to analyze all these data manually. Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In recent years, soft computing became more and more attractive for the researchers, who work in the related research field of data mining. This paper concerns primarily about how to use soft computing model to extract knowledge from data mining (database). The data mining preprocessing techniques have been applied on the available data to clean it in proper form to extract the knowledge from the data. Thereafter, statistical analysis and soft computing techniques have been applied on the clean data to select the preferable model. The decision of the preferable model has to be achieved based on the maximum number of minimum value of the parameters of residual analysis and average error. The preferable model has been used to extract the knowledge from the data mining. The goal of the

What are the products that are frequently bought by best customers? “. In this case ‘sales’ is the subject, thus a data warehouse can be defined by any subject like purchase, inventory, finance, marketing etc..,

The Retail industry consists of a systematic system including the products information, inventory system, employee’s information as well as for the billing purposes. To keep track of the sales and to avoid mix up in inventory and to have a aligned billing system to avoid mix of bills amongst the patrons. Other retail stores at the malls or separate factory outlets etc. is using this system to be able to give a good service to its patrons.

Data mining can also be used to determine the likelihood of a customer that will purchase a specific ordering. Given the current customer base for product x, who will most likely to become purchasers of product x? This question can be answered by using data mining in conjunction with customer database that include internal data about a customer's relationship and interactions, and derived data that identify the customer's value to the company and similarities or differences from other customers are applied.

An OLAP data analysis tool will be employed to analyses the information. Some of this customer information will be made available back at the POS station, used in conjunction with a designed customer loyalty card. An intranet and extranet will also be introduced to facilitate communications between stores in the chain and also between stores and suppliers.