An insurance company finds some intriguing patterns in the loyalty card data it bought from a grocery chain—the correlation between condom sales and HIV-related claims, for instance. How can both companies leverage the data responsibly? Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned?
How Data Mining Can Help Advertisers Hit Their Targets
Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. How can the two companies use the customer data responsibly?
Commenting on this fictional case study are George L. Laura Brickman was glad she was almost done grocery shopping. The lines at the local ShopSense supermarket were especially long for a Tuesday evening.
Her cart was nearly overflowing in preparation for several days away from her family, and she still had packing to do at home. Taking care not to crack any of the eggs, she squeezed the remaining items into the cart. She wheeled past the ShopSense Summer Fun displays. She got to the checkout area and waited. Laura for years had been interested in the idea of looking beyond the traditional sources of customer data that insurers typically used to set their premiums and develop their products.
Casinos, credit card companies, even staid old insurance firms were joining airlines, hotels, and other service-oriented businesses in gathering and analyzing specific details about their customers. And, according to recent studies, more and more of those organizations were sharing their data with business partners.
Laura had read a profile of ShopSense in a business publication and learned that it was one of only a handful of retailers to conduct its analytics in-house. As a result, the grocery chain possessed sophisticated data-analysis methods and a particularly deep trove of information about its customers. In the article, analytics chief Steve Worthington described how the organization employed a pattern-based approach to issuing coupons.
Shortly after reading that article, Laura had invited Steve to her office in San Francisco. The two met several times, and, after some fevered discussions with her bosses in Ohio, Laura made the ShopSense executive an offer. Several months after receiving the tapes, analysts at IFA ended up finding some fairly strong correlations between purchases of unhealthy products high-sodium, high-cholesterol foods and medical claims.
Laura understood it might be a tough sell. The make-or-break issue, she thought, would be the reliability and richness of the data. No one else has the historical data we have or as many customers nationwide. No one else has the historical data we have. Laura had finally made it to front of the line. The cashier scanned in the groceries and waited while Laura swiped her card and signed the touch screen. Once the register printer had stopped chattering, the cashier curled the long strip of paper into a thick wad and handed it to Laura.
Before wheeling her cart out of the store into the slightly cool evening, Laura briefly checked the total on the receipt and the information on the back: coupons for sunblock and a reminder about the importance of UVA and UVB protection. Archie had been invaluable in guiding the pilot project. Laura had flown in two days ahead of the meeting and had sat down with the chatty statistics expert and some members of his team, going over results and gauging their support for continuing the relationship with ShopSense.
And IFA was among the best in the industry at evaluating external sources of data credit histories, demographic studies, analyses of socioeconomic status, and so on to predict depression, back pain, and other expensive chronic conditions. Prospective IFA customers were required to disclose existing medical conditions and information about their personal habits—drinking, smoking, and other high-risk activities—the actuary reminded the group.
The CEO, meanwhile, felt that Rusty was overlooking an important point. Laura was keeping an eye on the clock; there were several themes she still wanted to hammer on. As a benefit to society? Several managers at the table began talking over one another in an attempt to respond.Data Mining is an important analytic process designed to explore data. Much like the real-life process of mining diamonds or gold from the earth, the most important task in data mining is to extract non-trivial nuggets from large amounts of data.
Extracting important knowledge from a mass of data can be crucial, sometimes essential, for the next phase in the analysis: the modeling. Although the definition of data mining seems to be clear and straightforward, you may be surprised to discover that many people mistakenly relate to data mining tasks such as generating histograms, issuing SQL queries to a database, and visualizing and generating multidimensional shapes of a relational table. For example: data mining is not about extracting a group of people from a specific city in our database; the task of data mining in this case will be to find groups of people with similar preferences or taste in our data.
The tasks of data mining are twofold: create predictive power —using features to predict unknown or future values of the same or other feature—and create a descriptive power —find interesting, human-interpretable patterns that describe the data.
This is especially the case due to the usefulness and strength of neural networks that use a regression-based technique to create complex functions that imitate the functionality of our brain. Association rule discovery is an important descriptive method in data mining.
The applications for associate roles are vast and can add lots of value to different industries and verticals within a business.
Here are some examples: Cross-selling and up-selling of products, network analysis, physical organization of items, management, and marketing. This was an industry staple for decades in market basket analysis, but in recent years, recommendation engines have largely come to dominate these traditional methods. Classification is another important task you should handle before digging into the hardcore modeling phase of your analysis.
Assume you have a set of records: each record contains a set of attributes, where one of the attributes is our class think about letter grades. Our goal is to find a model for the class that will be able to predict unseen or unknown records from external similar data sources accurately as if the label of the class was seen or knowngiven all values of other attributes. In order to train such a model, we usually divide the data set into two subsets: training set and test set. The training set will be used to build the model, while the test set used to validate it.
The accuracy and performance of the model is determined on the test set. Classification has many applications in the industry, such as direct marketing campaigns and churn analysis:.Storage tank safety
Direct marketing campaigns are intended to reduce the cost of spreading marketing content advertising, news, etc. This target feature will become the class attribute. Churn is the measure of individuals losing interest in your offering service, information, product, etc. In other words, churn analysis tries to predict whether a customer is likely to be lost to a competitor.
To analyze churn, we need to collect a detailed record of transactions with each of the past and current customers, to find attributes that can explain or add value to the question in hand.
Some of these attributes can be related to how engaged the subscriber was with the services and features that the company offers. Clustering is an important technique that aims to determine object groupings think about different groups of consumers such that objects within the same cluster are similar to each other, while objects in different groups are not. The Clustering problem in this sense is reduced to the following:. Given a set of data points, each having a set of attributes, and a similarity measure, find clusters such that:.
In order to find how close or far each cluster is from one another, you can use the Euclidean distance if attributes are continuous or any other similarity measure that is relevant to the specific problem. A useful application of clustering is marketing segmentation, which aims to subdivide a market into distinct subsets of customers where each subset can be targeted with a distinct marketing strategy.Want a systematic guidance?
Download the Octoparse handbook for step-by-step learning. Job sites are really popular today for anyone to find the right job or for any firm to hire the perfect candidates. A regimented recruitment plan should consider factors such as:. Find the best job title to post a position. In most cases, job seeker would apply for jobs with a matching title that they could understand or be hunting for.
Thus recruiters could use data from job sites, like Indeed, to figure out a more accurate and better fitting job title to any of the roles recruiting for. Then, talents with matching requirements will find a way to you instantly.
So now, how do you find the best fitting job title?
You could input a general keyword to search on any job platforms, then focus on the filtered search results of Job Titles. The job titles listed on the top are the ones associated the most with the keyword you are searching for.
This information will help recruiters to identify the underlying factors that are most attractive to any candidates, for example incentives, salaries, promotions and etc. Offer relative competitive wages. No surprisingly, competition from companies has driven up the wages to attract talents.
By doing so, recruiters may refer to some relevant job positions posted by other companies via searching the job search function from the perspective of a candidate, like the Indeed for example.
It will allow recruiters to track the information from their competitors, and offer a relatively competitive wage. Furthermore, you could pinpoint the accurate location within an area or specify whether this position is only available for full-time, part-time or others. Keep an eye on the Industry Employment Trends.
The supply-and-demand for professionals is always changing. This statics is not looking very optimistic for any accounting candidates. Plus, recruiters can also find the most sought-after job titles, some top searching keywords, and popular work places by delving into these statistics. Scrape data for deep learning. Now, we've seen how the data from job sites could substantially help recruiters finding the most wanted candidates.
Octoparse, a non-coding, desk-top web scraper. It is rather easy to work with with its user-friendly UI and self-explained workflow designer see below screenshot. Users could easily extract the data within a few steps. In addition, Octoparse is capable of transforming the captured data into structured data for further evaluation and analysis. Octoparse has provided a Regex built-in generation tool, which can help users normalize and purge data if needed.
This specific tool provides great convenience for any users not so fond of Regex as it generates regular expressions automatically with a few easy selections.Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs.
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detectionspam Email filtering, or even to discern the sentiment or opinion of users. The data mining process breaks down into five steps. First, organizations collect data and load it into their data warehouses.
Next, they store and manage the data, either on in-house servers or the cloud. Business analysts, management teams and information technology professionals access the data and determine how they want to organize it. Then, application software sorts the data based on the user's results, and finally, the end-user presents the data in an easy-to-share format, such as a graph or table.
Data mining programs analyze relationships and patterns in data based on what users request.
4 Data Mining Techniques for Businesses (That Everyone Should Know)
To illustrate, imagine a restaurant wants to use data mining to determine when it should offer certain specials. It looks at the information it has collected and creates classes based on when customers visit and what they order. Warehousing is an important aspect of data mining.
Warehousing is when companies centralize their data into one database or program. With a data warehouse, an organization may spin off segments of the data for specific users to analyze and use. Regardless of how businesses and other entities organize their data, they use it to support management's decision-making processes.
Grocery stores are well-known users of data mining techniques. Many supermarkets offer free loyalty cards to customers that give them access to reduced prices not available to non-members. The cards make it easy for stores to track who is buying what, when they are buying it and at what price. Data mining can be a cause for concern when a company uses only selected information, which is not representative of the overall sample group, to prove a certain hypothesis.
Automated Investing. Company Profiles. Financial Fraud. Your Money.Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers.
It discovers information within the data that queries and reports can't effectively reveal. This paper explores many aspects of data mining in the following areas:. The amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes.Titan yoke vs rogue yoke
A terabyte is equivalent to about 2 million books! Raw data by itself, however, does not provide much information.
In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.
The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers and potential customers stored in " Data Warehouses.
Data warehouses are used to consolidate data located in disparate databases. A data warehouse stores large quantities of data by specific categories so it can be more easily retrieved, interpreted, and sorted by users.
Warehouses enable executives and managers to work with vast stores of transactional or other data to respond faster to markets and make more informed business decisions. It has been predicted that every business will have a data warehouse within ten years.
But merely storing data in a data warehouse does a company little good.
Companies will want to learn more about that data to improve knowledge of customers and markets. The company benefits when meaningful trends and patterns are extracted from the data. Data miningor knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.
Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore.
Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides. Although data mining is still in its infancy, companies in a wide range of industries - including retail, finance, heath care, manufacturing transportation, and aerospace - are already using data mining tools and techniques to take advantage of historical data.
By using pattern recognition technologies and statistical and mathematical techniques to sift through warehoused information, data mining helps analysts recognize significant facts, relationships, trends, patterns, exceptions and anomalies that might otherwise go unnoticed.
For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.It service desk
Specific uses of data mining include:. Automated prediction of trends and behaviors : Data mining automates the process of finding predictive information in a large database.La frazione di parigi nel comune di felino (pr) emilia-romagna
Questions that traditionally required extensive hands-on analysis can now be directly answered from the data. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings.Lecture 20 — Frequent Itemsets - Mining of Massive Datasets - Stanford University
Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns : Data mining tools sweep through databases and identify previously hidden patterns.
An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together.
Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors. Using massively parallel computerscompanies dig through volumes of data to discover patterns about their customers and products.
For example, grocery chains have found that when men go to a supermarket to buy diapers, they sometimes walk out with a six-pack of beer as well.Prerequisites : Data Mining. When we talk about data mining, we usually discuss about knowledge discovery from data. To get to know about the data it is necessary to discuss about data objects, data attributes and types of data attributes. Mining data includes knowing about data, finding relation between data. And for this we need to discuss about data objects and attributes.
Data objects are the essential part of a database. A data object represents the entity. Data Objects are like group of attributes of a entity.
For example a sales data object may represent customer, sales or purchases. When a data object is listed in a database they are called data tuples. It can be seen as a data field that represents characteristics or features of a data object. For a customer object attributes can be customer Id, address etc. We can say that a set of attributes used to describe a given object are known as attribute vector or feature vector.
Type of attributes : This is the First step of Data Data-preprocessing. We differentiate between different types of attributes and then preprocess the data. So here is description of attribute types. Quantitative Discrete, Continuous.
Quantitative Attributes. If a measurement is ratio-scaled, we can say of a value as being a multiple or ratio of another value. The values are ordered, and we can also compute the difference between values, and the mean, median, mode, Quantile-range and Five number summary can be given.
Discrete : Discrete data have finite values it can be numerical and can also be in categorical form. These attributes has finite or countably infinite set of values. Example Continuous : Continuous data have infinite no of states. Continuous data is of float type. There can be many values between 2 and 3.
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Donate now. Sixteen training modules for teaching core skills. Learn more. Essentially, collecting data means putting your design for collecting information into operation. There are two kinds of variables in research. An independent variable the intervention is a condition implemented by the researcher or community to see if it will create change and improvement. This could be a program, method, system, or other action. A dependent variable is what may change as a result of the independent variable or intervention.
A dependent variable could be a behavior, outcome, or other condition.
Analyzing information involves examining it in ways that reveal the relationships, patterns, trends, etc. It may mean comparing your information to that from other groups a control or comparison group, statewide figures, etc.
Quantitative data refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analyzed mathematically. Qualitative data are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc. As you might expect, quantitative and qualitative information needs to be analyzed differently. Quantitative data are typically collected directly as numbers. Some examples include:.
Researchers can count the number of times an event is documented in interviews or records, for instance, or assign numbers to the levels of intensity of an observed event or behavior. For instance, community initiatives often want to document the amount and intensity of environmental changes they bring about — the new programs and policies that result from their efforts.
Quantitative data is usually subjected to statistical procedures such as calculating the mean or average number of times an event or behavior occurs per day, month, year. Various kinds of quantitative analysis can indicate changes in a dependent variable related to — frequency, duration, timing when particular things happenintensity, level, etc.
They can allow you to compare those changes to one another, to changes in another variable, or to changes in another population. They might be able to tell you, at a particular degree of reliability, whether those changes are likely to have been caused by your intervention or program, or by another factor, known or unknown.
And they can identify relationships among different variables, which may or may not mean that one causes another. A number may tell you how well a student did on a test; the look on her face after seeing her grade, however, may tell you even more about the effect of that result on her.
And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the test. Qualitative data can sometimes be changed into numbers, usually by counting the number of times specific things occur in the course of observations or interviews, or by assigning numbers or ratings to dimensions e.Sns unsubscribe permission
The challenges of translating qualitative into quantitative data have to do with the human factor. Furthermore, the numbers say nothing about why people reported the way they did. One may dislike the program because of the content, the facilitator, the time of day, etc. Where one person might see a change in program he considers important another may omit it due to perceived unimportance. Quantitative analysis is considered to be objective — without any human bias attached to it — because it depends on the comparison of numbers according to mathematical computations.
Be aware, however, that quantitative analysis is influenced by a number of subjective factors as well. Part of the answer here is that not every organization — particularly small community-based or non-governmental ones — will necessarily have extensive resources to conduct a formal evaluation.
They may have to be content with less formal evaluations, which can still be extremely helpful in providing direction for a program or intervention. An informal evaluation will involve some data gathering and analysis. This data collection and sensemaking is critical to an initiative and its future success, and has a number of advantages. The level of significance of a statistical result is the level of confidence you can have in the answer you get.
Thus, if data analysis finds that the independent variable the intervention influenced the dependent variable at the.
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