What Is Predictive Analytics?

Predictive analytics is a statistical or data mining solution that includes algorithms and techniques that can be used in structured and unstructured data to determine future results. It can be deployed for many other uses such as forecasting, optimization, forecasting, and simulation. It can also provide a variety of information for the planning process and provide key insights into the future of the business.

Predictive and what-if analysis help users review and weigh the impact of potential decisions. It can be used to analyze historical patterns and probabilities to predict future performance and take preventive measures.

This level of analysis can provide a variety of information for the planning process and provide key insights into the future of the business. Cognos Business Intelligence provides more than predictive analytics, enabling users to perform advanced analytics, publish, and communicate with a wider user base. What-if analysis is also available, enabling users to create and evaluate instant scenarios.

The Hurwitz & Associates Success Index is a market research assessment tool developed by Hurwitz & Associates that analyzes suppliers from four dimensions: vision, viability, effectiveness, and value. Not only can you evaluate the technical capabilities of the technology, but also its ability to bring real value to your business. Hurwitz & Associates analysts have used predictive analytics to evaluate predictive analytics and believe they have great potential to continue to drive innovation and market development in this area. IBM SPSS, SAS, StatSoft, Pegasystems, and Pitney Bowes received points for being successful from a market perspective.

manufacturing

Predictive analytics helps manufacturing maintain operations efficiently and better control costs.

The challenge for manufacturing has always been to optimize resources at every step of the process while producing quality goods. Over the years, manufacturers have developed a series of proven methods to control quality, manage supply chains, and maintain equipment. Today, faced with ongoing cost control efforts, plant managers, maintenance engineers, and quality control supervisors all want to know how to avoid costly unplanned downtime or equipment failures while maintaining quality standards, and how to control maintenance, Labor and inventory costs for repair and overhaul (MRO) operations. In addition, managers in the finance and customer service departments, and ultimately executive-level managers, are closely related to whether the production process can deliver the finished product well.

IBM SPSS predictive analytics helps manufacturers minimize unplanned maintenance downtime, truly eliminate unnecessary maintenance, and predict warranty costs well to meet new quality standards and save money. It can be used for predictive analysis of production lines, timely maintenance to prevent production interruptions due to failures, and can resolve a range of customer service issues, including customer complaints about downtime due to unplanned repairs and product failures. It can also be used for manufacturing operations in different industries such as automotive, electronics, aerospace, chemicals and petroleum.

Crime prediction and prevention

Predictive analytics uses advanced analytics to create a safe public environment.

To ensure public safety, law enforcement officials have relied primarily on personal intuition and available information to complete tasks. In order to be able to work smarter, many police organizations are making full and reasonable use of the structured information (such as crime and criminal data) and unstructured information (audio and video materials obtained during communication and supervision) that they obtain and store. By summarizing and analyzing these huge data, the information obtained not only helps to understand what happened in the past, but also helps to predict possible events in the future.

Using historical crime incidents, archival data, maps and typologies, and data such as triggers (such as weather) and triggering events (such as holidays or paydays), police officers will be able to: identify areas where violent crimes occur frequently; Or national gangster activities to match local events; analyze criminal behavior to find similarities, link criminal behavior to criminals with criminal records; find out the conditions most likely to induce violent crime, and predict when these criminal activities may occur in the future And location; determine the possibility of recidivism.

IBM's crime prediction and prevention analytics technology helps organizations make the most of their people and information resources to monitor, measure, and predict crime and crime trends. Analyze police data to provide insights that enable police officers to track criminal activity, predict the likelihood of an incident, deploy resources efficiently, and process cases quickly.

telecommunications

Predictive analytics helps telecom operators better understand their customers.

Driven by technical and regulatory requirements, and the emergence of a new ecosystem of Internet-based communication service providers and models, telecommunications providers are facing unprecedented changes. To gain new sources of value, telecom service providers need to make fundamental changes to their business models and must be able to combine strategic assets and customer relationships with innovations designed to capture new market opportunities. The ability to anticipate and manage change will be a key capability for future telecommunications service providers. This involves predicting and managing continuous change, including allowing employees to participate in the development of innovation agendas, promoting collaboration with customers, suppliers, and partners, and deploying a dynamic business architecture with a flexible and adaptable IT infrastructure that supports Changing business models.

IBM can help telecom operators adopt real-time analytics and predictive analytics to gain a deeper understanding of customers in order to unlock the value of customer data and assets ^[1]

No assumed results after start

Everyone is excited about predictive analytics, and you see its potential value. But there is a problem: you don't have a specific goal in your mind.

This is the case at a large company in which Elder Research is involved. The company started using their data to predict something, or all things, that a square tube could go out and sell to his business unit. Although the research organization agreed to cooperate with him and tailored a usage model for him, but because no one in this business unit had any questions about what he was going to sell, the project finally lost its direction.

Lesson: Don't do a hammer first, then find a nail. Before you start, be sure to have a specific goal.

Define projects based on data not supported

A debt collection company wants to find the most effective way to get debtors to pay their debts. The challenge is that the company already has a strict set of rules, and it follows them in every case.

Data mining is an art of contrast. Because the company has a mature set of principles and has followed them, they don't know which outcome is more conducive to debt recovery. So the company needs some historic examples.

If you don't have these cases, then you need to create a series of experiments to collect data. For example, suppose there are 1,000 debtors, 500 receive a threatening letter, and the other 500 receive a telephone debt collection. This is the first step. Then, the prediction model can make predictions, predicting which type of debtors will respond better to threatening letters, and which type will respond better to calls.

In some cases, the types of debtors may include debt caused by historical models, past debt paid on a daily basis, income, zip code homes, and so on. Based on predictive models, this debt collection agency may better use more economical strategies than use the same strategy for everyone. But you have to start with experiments. Nothing happens, which is impossible for predictive analysis.

Don't move forward until you get the best data

People often misunderstand operations: they must make the data perfectly organized without any loopholes, obstacles, or missing value before predictive analytics.

A client of Elder Research, a multinational petrochemical company just started a predictive analysis project, expecting a greater return on investment, but at this time their data scientists found that the existing operational data was worse than they originally thought.

One of the most critical target values is missing in this case. The project may be delayed for at least one year while the business is waiting to collect new data. Most companies are stagnant here. This error is the biggest killer of the project compared to other errors.

Does not remove junk data when assessing data quality

A Fortune 1000 financial services company wants to predict which call center employees will work the longest. At first glance, the company's historical data seems to indicate that employees without a high school diploma who stay at the company for at least 9 months have 2.6 times more data than employees with other educational backgrounds. The consulting firm advises clients to start by hiring high school dropouts first.

But there are two problems. The data manually typed from the resume of the job seeker has been marked inconsistently. One piece of data examines people at all levels of education, and the other only examines people who have completed a high level of education.

Another more complicated problem is that, for some reason, the latter is more common than the former among all the marks in the simplicity of the longest person. You can avoid these problems by making sure that the tags you make are a set of resumes that you type at random and that everyone uses the same notation.

In this case, we got the most information: "Only garbage is present, there will be garbage cleaning. We must ensure the integrity of the data between ensuring the quality of the data ^[2]

Banco ItaúArgentina uses predictive analytics to optimize cross-selling. Banco ItaúArgentina is an Argentine bank. The retail financial market in Argentina is extremely competitive, and many banks are competing for the "intentions and wallets" of target customers. In such a competitive environment, Banco ItaúArgentina needs to improve its response rate to sales activities, and then increase its revenue channels to provide financial support for market share growth efforts.

In mid-2007, the bank launched an optimized cross-selling and customer win strategy, which was achieved in four phases. First, the IBM SPSS Modeler is used to build a predictive model to identify target customers with high purchase possibilities. Secondly, the cross-selling machine forecast is used to quickly test and promote promotions for the products most likely to be purchased by each customer in the revenue stream. Once again, IBM SPSS Event Builder was introduced to run monthly optimization of the bank's sales system, allocate limited marketing resources, and allocate multiple channels with different costs, customer contact restrictions, activity goals, and other business restrictions in order to use the correct Products, win the right customers at the right time, through the right channels, while maximizing the customer's financial return to the business. Finally, using the IBM SPSS Data Collection *, the bank has developed a direct "dialogue" bridge with customers to better understand customer needs and be able to provide personalized products.

Predictive analysis helps Fiat identify the most likely customers and potential customers. Fiat Automotive, through predictive analysis, identifies potential customers in sales, consolidating Fiat's success in today's highly competitive and constantly changing automotive market. Using IBM SPSS Statistics and IBM SPSS Modeler helps Fiat identify specific targets among existing and potential Fiat car owners, enabling dealers to allocate their marketing budgets in the most efficient way. Increased customer retention by 7%, and now 54% of Fiat customers still choose Fiat cars when they replace them with new ones. It also improved response rates in marketing campaigns by 15 to 20 percentage points, and more accurately targeted potential customer groups.

Predictive analysis improves Bayer Schering Pharma's competitive advantage

Which diseases do not have any form of treatment? Which medicines have serious side effects that can be avoided with new medicines? How satisfied are patients with their treatment? How many patients can get help with a medicine? How well does the patient know about a particular medicine? Are capsule or pill packages constructed so that patients can understand them? These are the challenges facing Bayer Schering Pharma.

Using IBM SPSS predictive analytics to accurately segment populations, analyze survey and trial data, and help Bayer Schering Pharma create a competitive advantage. Business benefits include the use of pre-segmentation to identify each precisely defined target group; the use of email, electronic medical promotion methods, or company representatives to target patient segments; significant resource savings and increased customer (doctor) satisfaction. Compared with the analysis provided by external market research institutions, the internal analysis of survey and experimental data brings deeper insights and competitive advantages.

Avis Uses Predictive Analytics Software to Save Email Marketing Costs Avis Europe is a car rental company with operations in Europe, Africa, the Middle East, and Asia, with a rental network of more than 2,800 locations. Avis Europe plc wanted to identify the factors that drove the growth of its direct business, especially customer inquiries and lease bookings obtained through its e-commerce channels. By gaining a deeper understanding of customers, the company wanted to be able to customize every email received by each customer and achieve greater information relevance.

IBM subsidiary SPSS helped Avis Europe create highly accurate and cost-effective email marketing campaigns. Not only segment customers, reveal key areas of marketing expense investment; target potential customers more accurately, reduce e-mail marketing costs and maximize revenue; e-mail marketing costs as a percentage of revenue have decreased by 42%; Through timely and valuable communication with customers, we have a deeper understanding of customer behavior and increased customer loyalty.

Predictive analysis helps the Memphis Police Department to identify "hot spots" to prevent crime. Traditional police work methods cannot cope with rising crime rates and tight budgets. The Memphis Police Department focuses on patrolling resources and actively improves intelligent management. By discovering the emergence of crimes, the Memphis Police Department s predictive control tools allow district commanders to change tactics, deploy patrol resources, effectively prevent and control crime, and at the same time be able to arrest more criminals in the process of criminal activities. By conducting predictive analysis and intelligent police work, the Memphis Police Department has reduced the overall crime volume in the Memphis area by 30%, including a target area with a 36.8% reduction in crime and a 15% reduction in violent crime. Division (FAU) case closing rate increased from 16% to nearly 70%, a four-fold increase. Under the financial situation with limited budget, the overall deployment of police forces has been improved, and a safer living environment has been established for citizens ^[4] .

Housing Construction Association uses predictive analytics to explore customer data

The Newcastle Building Society (NBS) is the eighth largest building society in the UK and the largest housing construction society in north-east England. It has a branch network of 35 branches, as well as one in Gibraltar. Since the economic downturn in 2007, the NewcastleBuilding Society has been looking for more cost-effective ways to attract new customers and retain existing customers.

By combining core data, geo-demographic segments, and Customer Panel responses, NBS uses the statistical capabilities of predictive analytics to understand correlations across various customer segment categories. Confidence scores enable NBS to link responses back to the overall customer base and build a propensity model using IBM SPSS Statistics. Asking specific people questions can identify smaller, more targeted groups. Through customer segmentation and customer response analysis, NBS can measure the benefits provided by this service, and then assess the likelihood of retaining or losing customers. ^[5]