The data your business gathers about current performance can be used for making predictions about the future. This is where predictive analytics solutions come into play. Combining AI, BI, machine learning, big data, data mining and modeling, these solutions enable you to use data you have collected about your business and customers to forecast future performance. Read the full software guide...
Predictive analytics software solutions utilize the practice of gathering vital information from existing big data sets in an effort to effectively identify and predict patterns, trends, and future outcomes. They are a type of advanced analytics solution that utilizes both current and historical data to forecast trends, behavior, and activity.
Predictive analytics covers a wide variety of statistical methods such as predictive modeling, machine learning, and conventional data mining, which evaluate the most recent data along with historical facts to come up with the most reasonable predictions regarding future or otherwise unknown trends and activities.
The goal of predictive analytics is to move beyond previous and/or current trends, events, and activities. It aims to focus on what will most likely happen in the future by extracting crucial data and providing the best assessment based on the information you have collected from your predictive analytics process.
Predictive analytics has been around for decades, but it has significantly grown in popularity mainly due to the advancement in the technologies associated with the process. In recent years, more and more businesses and organizations of all sizes across multiple industries started turning to predictive analytics software solutions in an effort to improve marketing strategies, increase revenue, and maintain a competitive edge.
Predictive analytics software solutions cover a category of statistics which deals with taking out crucial information from big data sets and utilizing it to predict potential outcomes, be it future trends, events, activities, or behavioral patterns.
The process of predictive analytics covers a variety of steps, starting with defining the project. The next step is to extract the valuable data, then analyzing and coming up with reasonable statistics. After this, the modeling process starts. Thereafter, deployment and finally the model monitoring commences.
The phrase predictive analytics is often used in reference to predictive modeling, scoring valuable data through predictive models, and forecasting. But, as of late, more and more people are using the term to mean descriptive modeling, decision modeling, or other related analytical disciplines.
Although such disciplines do involve quite a bit of data analysis, as well as being commonly utilized in business for the purposes of segmentation and decision making, they are done for different purposes and the statistical methods and techniques normally associated with them do differ.
The predictive modeling process involves utilizing models of the relationship between the key performance of a particular unit in a sample and one or more identified or known attributes/features of the unit. The goal is to determine or analyze the potential that a similar unit in another sample will exhibit the same specific performance.
This particular process covers models in many different aspects of the business, including marketing and fraud detection models. Predictive modeling typically involves performing key calculations during live interactions in order to guide the decision makers, enabling them to evaluate the risk or opportunity with regards to customers and/or transactions. The advancement in computing technology has made it possible for individual agent modeling systems to simulate human behavior or reactions based on specific scenarios.
Descriptive modeling, on the other hand, is a process that quantifies relationships in data in such a way that is commonly utilized to classify leads, customers, or prospects into specific groups or demographics. Unlike predictive modeling, which emphasizes assessing single customer behavior, descriptive modeling takes into account a variety of relationships between consumers or products/services.
Descriptive models do not necessarily rank the order of prospects or consumers by their likelihood of action or reaction the way predictive modeling does. This type of predictive analytics can essentially be used to classify leads and consumers by the type of products and/or services they generally prefer.
A variety of descriptive modeling tools can be deployed and used in order to develop more advanced models that may be able to simulate a larger sample of individualized agents and come up with predictions based on that data.
Decision modeling refers to the relationship between all the elements of a specific decision, all known data (including the insights gathered from predictive models), and the forecast results of the given decision in an effort to predict or come up with potential results of several decisions involving many different variables.
Decision models may be utilized in optimization strategies, thus maximizing key outcomes while minimizing others. Decision modeling is a process that is typically used to develop decision logic, or a specific set of business rules and/or policies, that will essentially produce the desired outcome or action for every consumer and/or scenario.
Predictive analytics is a process used in a number of industries, including aerospace, financial services, medical services, automotive, energy production, and industrial automation and machinery. The process helps teams and decision makers in these industries make better decisions to further their business and their industry as a whole.
But, choosing the right predictive analytics solution is not as simple as picking out the most popular off-the-shelf product on the market. There are a few important considerations to be made in order to ensure proper optimization, implementation, and utilization of the platform.
In order to get started on utilizing predictive analytics, the first thing you have to consider is identifying the problem to solve. Predictive analytics is all about figuring out or identifying potential issues before they occur. So, it goes without saying that before delving into the predictive analytics process, you should already have identified an issue or problem in your methodologies that could use a few tweaks and adjustments through predictive analytics.
The next step is to gather data. Any predictive analytics process requires a vast amount of data and a solid data extraction solution. In the age of the internet, big data can come from different places. Collecting and sifting through big data can be a tedious and time-consuming process. Transactional platforms, third-party data collection, information gathered from sensors, blogs, call center notes, and a lot of other data sources. You need an easy and efficient way to wrangle all the crucial information relevant to your purpose.
Once you have all the relevant data, the predictive model building commences. In this stage of the process, having a software solution that is straightforward and easy to use is crucial as it allows more users, regardless of their technical skills or experience, to have the prospect of building predictive models quickly and effortlessly. However, there’s really no replacing a good data analyst who can help refine the models.
The collaboration aspect is the other step in the process. Building a predictive model takes more than just one person, it requires a team approach. A good predictive analytics solution should have the features and capabilities that promote and integrate team collaboration.
Any company project requires a group of individuals who actually understand the business they’re in and the problems normally associated with it that needs immediate and direct resolution.
A lot of businesses and organizations are turning to predictive analytics software solutions to help increase revenue and competitive edge. Some of the main benefits of the predictive analytics process include detecting and identifying fraud. By combining several analytics methods, the process of detecting patterns and preventing criminal behavior can be significantly improved.
Predictive analytics may also help in the optimization process of marketing campaigns and strategies, ensuring positive results. It also helps to improve overall business processes and operations, as well as reduce potential risks.
|A/B Testing||Run split tests for websites, emails, ads and more by serving different versions of the content to different users.|
|Analytics||Analyze and gain insights from data including web traffic, campaign conversions, sensoric output and more.|
|API||Application Programming Interfaces (APIs) are programmatic intersections with external products or platforms that allow for custom integrations with your own solutions or other solutions you are using.|
|Calendar Management||Manage and update calendars for scheduling or consolidation of events across teams, departments or business functions.|
|Conversion Tracking||Track signups, purchases or user actions to measure the effectiveness of marketing or advertising campaigns. This is often used to optimize inbound or outbound marketing efforts and improve sales conversions through a range of online channels.|
|Dashboard||Dashboards are digital interfaces commonly used to visualise data or give quick access to important features and functions of online platforms. They often serve as an overview gateway in software applications.|
|Data Export||Exporting functionality can be used to streamline the migration of data sets and information across systems, platforms or applications.|
|Data Import||Importing functionality allows you to use data sets from other systems or platforms to cut down on data entry requirements or to more easily migrate records from similar applications you have used in the past.|
|Data Visualization||Data visualization features render a visual interpretation of data sets through the use of charts, infographics and other visual cues generally in form of a reporting dashboard.|
|External Integrations||Integrations with other software products or platforms to improve efficiency and compatibility across systems.|
|Forecasting||Forecast upcoming expenses, sales, revenue, user levels, etc. through the use of predictive methods and past data.|
|Keyword Tracking||Track keywords for Search Engine Optimization (SEO), Search Engine Marketing (SEM) or item tagging purposes.|
|Link Tracking||Track link performance to your own or to competitor websites.|
|Multi-Site||Support for several different websites in the same application.|
|Multi-User||Supports more than just one user account and generally allows for collaboration with colleagues.|
|Notifications||Includes notification support and sends you alerts with information on important events and other time sensitive instances. For example through push notifications on mobile phones or email notifications.|
|Referral Tracking||Track contact-, user- or visitor- referrals from advertising networks, other websites, business partners and other channels for intelligence, revenue sharing or similar purposes.|
|Scheduling||Schedule tasks, resources, appointments, payments, communications, etc.|
|SEO||Search Engine Optimization (SEO) refers to the process of producing content or optimizing and promoting webpages to improve their ranking on search engine results pages (SERPs).|
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