Barbie Coke
Mensur Boydas Lecture on data mining techniques for marketing
1 – What is Data Mining?
Data mining is the exploration and analysis large amounts of data to discover meaningful prototypes and rules. The goal of data mining is to help organizations in marketing, sales improvement, and customer tracking procedures in analyzing their customers. In reality, data mining algorithms were first invented for marketable applications. Data mining in marketing consists of two ways; directed and undirected. Oriented data mining attempts to categorize some target fields, such as income or Consumer Response. Undirected data mining is pattern recognition or agreements between groups the data without the use of a field or object classes. The main goal is to build data mining models. A model is an algorithm that a set of inputs connected on a particular outcome. A model may result in imminent by providing an explanation of how the results of particular interest, such as placing an order or omission of a bill. Models are also used to display scores. There are six tasks can be performed with data mining, are;
- Classification
- Estimate
- Prediction
- Association Rules
- Clustering
- Description and profiling.
Format consists of investigating the characteristics of a newly released item and assigning it to a predefined set of classes. Just discrete results: yes or no. Estimation deals Continuous results indicate a number, comes with an estimated value for a number of unidentified continuous variable such as income or credit balance. Prediction is not different from the estimation and classification, except that the records are classified according to some predicted future behavior. The purpose of the association rules to determine which items together go. As an example, determine the items together in a shopping cart in the supermarket. Clustering is the segmentation of a more diverse population in a number of identical clusters. Clustering is not depending on predefined classes. Profiling is a description of what happens in a complex database in a way that our understanding of people, products or processes that produced information increases. Decision trees, association rules, clustering can be used for profiling customers for a particular outcome in marketing.
2 – Data Mining Applications in Marketing
Data mining application used in business context to be of value. The topics covered in this section are prospecting, data mining in the right place to advertise, and data mining for customer relationship management to choose.
- Prospecting
A prospect is a person who is an expected one customer in the future. Data mining is used to achieve the organizational goal of finding people who are important customers the future. Relatively, few of the earth more than six billion people are in fact the outlook for a company. The exclusion based on geography, age, financial standing, the need for product / service, etc. A newspaper wants to target people who read the correct language and will be important for its advertisers. Data mining can be used in many ways in prospecting and some of the most important are the following;
- Identifying good prospects
- Choosing a communication channel for reaching prospects
- Picking appropriate messages for different groups of prospects.
The most common role here is to identify good prospects.
- Data mining choosing the right place to advertise
One way of finding prospects is looking for people that are similar to current customers. Majority of companies selling products and services to advertise and promote their products and services to their share maximize the average market. There are essentially two approaches to advertising and promotion, direct marketing and mass. Mass Marketing uses TV, radio and newspapers, broadcast news. Direct marketing analysis of customers' characteristics and needs and then some customers as the target for the promotion and advertising. As an example, if something is advertised In one of the mass media such as television, the publishing watchers look for channels whose match their prospect profile. Mass marketing is an effective way of promotion when there is a high demand for the promoted goods and services. Perfect in today's competitive business environment, mass marketing is becoming less and less effective, which expect a product to sell after the break, with mass marketing techniques. Direct marketing is more promising in collecting responses from the perspective of certain goods or services. Today the day there is an enormous amount of information stored in databases on customers, so there may be data mining are promising in direct marketing. from the databases patterns can be detected through data mining for the deserving customers for predicting promotion. The aim of marketing campaign is to change behavior. So, reaching a prospect who is still buying is less effective than those that achieve the unlikely purchase. Whether a client fits the profile similarity to the known distance between the customer and the profile.
- Data Mining for Customer Relationship Management
Customer Relationship Management (CRM) mainly deals with known customers. For data mining established customers is the main source. Is the customer to pay bills? What types of products the customer buy? How many times have we contacted the customer? This type data can be used to evaluate the value of customers and predicting the risks that the customer relationship will cut or stop paying bills. Customers who you have today may disappear tomorrow. Interaction with your customers is not as simple as it is in the past. Customers and potential customers want to work on their terms, implication you need to look at different criteria in assessing the go. At the end of value for the customer to determine the image data mining is to estimate potential customer value approximation of the income of a customer and give the client life remaining.
3 – Use of Association Rules in Marketing
Association rules in marketing is concentrated on the acquisition of different types of customers and what they bought. Each customer buys different products in every trip to the supermarket. This analysis uses this information to predict which products tend to be bought together and suitable for promotion. The information gathered from this could be used to alert you when coupons, special issue on products and so on. Association rules are patterns in the data without a special target that an undirected data mining technique.
Here are the real rules generated from the actual data; "
- Wal-Mart customers that Barbie dolls have a 60 percent chance of also buying of one of the three types of candy bars.
- Customers who purchase maintenance agreements are most likely to buy major appliances.
- When a new hardware store opens, one of the most sold items is toilet bowl cleaners "(Berry, Linoff, p.296).
Given a set of records each containing a number number of items from a given set of purchase; produce dependency rules that predict the incidence of an item based on occurrences of other items. Example
Table 1: Supermarket Transactions
The table shows the five transactions with customers. From these data using association rules that we can connect with the purchase of such rules that;
{} {} A Milk Coke
{Diaper, Milk} {} — A Beer
So who is buying milk can also buy coke and the probability This recognition can also be calculated and who is buying diapers and milk could also buy beer.
After the discovery rule, there are applications to provide, such as taking; Marketing and Sales Promotion, store management and inventory management.
Marketing and Sales Promotion:
Suppose that the discovered rule} {Milk-a {Coke}
Coke as a result could be used to determine what must be done to boost sales.
Milk can be used when the antecedent to see which products would be affected if the store discontinues selling milk.
Antecedent milk and Coke in it could be used to see which products milk must be sold with the sale of coke promotion.
Supermarket shelf management:
Objective: Categorize items together satisfactory purchased by many consumers.
Approach: Do the point-of-sale barcode scanners to information compiled by the trust between the products available.
Inventory Management:
Objective: A customer home renovation company wants the repairs to the nature of goods and services to predict and hold the prepared service vehicles with the right parts to reduce the number of visits to customers.
Approach: The approach is to provide the information to perform on instruments and components required in previous customer service at various locations and determine the co-occurrence models.
Dissociation article:
Dissociation association rule is a rule except that it allows the connector "and not" the State "and". Dissociation rules as follows:
If A and not B, C could Dissociation rules are made by simple variation of the market basket analysis algorithm. Variation is to bring a new set of items that the inverse of each of the original items.
Association rules provide ways to evaluate item-level detail, where the associations between items is determined by the customer transactions in supermarkets.
4 – Practice of Data Mining in Marketing
The methodology of data mining in marketing has eleven steps. They are "
- Translate the business problem in a data mining problem.
- Select the correct data.
- Get to know the details.
- Make a model.
- Fix problems with the data.
- Transforming data into information to the surface.
- Build models.
- Donkeys models.
- Implement models.
10. Donkeys results.
11. Begin Again "(Berry, Linoff, p. 54, 55).
The business problems are translated into data mining problems through the usability of the data mining tasks. The data are then visualized on a number of informative patterns. The model will create the following, which is an algorithm for numerical solution to reach. Data mining grabs the method of translating business needs into a problem that must be evaluated recovering the database with large amount of data for analysis, and application of statistical or machine leaning system with the ultimate goal of the completion of key results for taking a strategic decision for the business problem.
5 – Conclusion
Data Mining is very practical in business, where to obtain competitive advantage is crucial for a good business presentation and survival. Evaluate customer transaction information and modeling activities can provide information leading to better decision-making, such as product promotion to the right segment of customers. Data mining is usually used in the advertising plans, search fraud, credit scoring, consumer segmentation and merchandise prologue.
About the Author
Survey! Survey!?
1. Coke or Pepsi 2. Play Station or Xbox or Nintendo (the company) 3. iPhone or RAZR 4. wooden or mechanical pencils pencils 5. Water park or amusement park? 6. What more beliveable: aliens, ghosts or no 7. Republican, Democrat, or neither? 8. cats or dogs? 9. wikipedia or encyclopedia? 10. books or magazines? 11. Hip-hop, rap, alternative, country, or rock? 12. antiques, or new stuff? 13. pug or poodle? 14. Barbie or Bratz? 15. half full or half empty? Many fun !!!!! Thanks everyone!
1. Pepsi 2. Xbox 3. iPhone 4. wooden pencils 5. water parks 6. ghosts 7. 8 Democrat. 9 dogs. wikipidia 10. 11 books. Rock 12. antique 13. poodle 14. Bratz 15. half empty
Barbie’s Coke commercial
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