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BUSINESS ANALYTICS SOFTWARE

Predictive analytics involves extracting data from existing data sets with the goal of identifying trends and patterns. These trends and patterns are then used to predict future outcomes and trends. While it’s not an absolute science, predictive analytics does provide companies with the ability to reliably forecast future trends and behaviors.

the concept as any approach to data mining that contains the following key elements:

• Emphasizing prediction, rather than description, classification, or clustering
• Rapid analysis, with measurements in hours or days, rather than the traditional approach to data mining
• Emphasizing business relevance of the resulting insights
• Ease of use, making data and tools easily accessible by business users

Predictive analytics emerged from a desire to turn raw data into informative insights that can be used not merely to understand past patterns and trends, but provide a model for accurately predicting future outcomes.

How Predictive Analytics Differs from Other Analytics Models

Gartner visualizes the various types of analytics as being on a spectrum, with each more advanced method of analysis being more difficult, but offering increased value. Descriptive analytics are at the low end of the spectrum, with a primary focus on information. Diagnostic analytics is the next level of analysis, providing insights on the motivations and causes driving trends and behaviors.

Diagnostic analytics is followed by predictive analytics, or the ability to forecast what is likely to happen. At the top of the spectrum is prescriptive analytics, providing foresight and the knowledge required to create desired outcomes.

Predictive Analytics Methods

Predictive analytics is primarily concerned with analyzing data and manipulating variables in order to glean forecasting capabilities from existing data. Predictive analytics techniques rely on measurable variables, manipulating metrics to predict future behavior or outcomes given various measurable approaches.

Predictive analytics models combine multiple predictors, or measurable variables, into a predictive model. This approach allows for the collection of data and subsequent formulation of a statistical model, to which additional data can be added as it becomes available.

The addition of higher volumes of data as it becomes available creates a smart predictive model, relying on larger and larger data sets which produces more reliable predictions based on the volume of data analyzed. Additionally, relying on real-time data to fuel predictive analytics models results in greater accuracy of forecasting.

Uses for Predictive Analytics in Marketing

Predictive analytics is a valuable tool in marketing, allowing marketers to make accurate predictions of the most likely behaviors of consumers. These forecasts are used to formulate the most effective marketing approaches offering the greatest likelihood of achieving desired outcomes.

Other predictive analytics examples include:

• Determining how interested a consumer is likely to be in a promotional offer
• Predicting the likelihood that a customer will become a loyal customer, based on specific promotions or pricing models
• Identifying which customers are most likely to churn
• Pinpointing the up-selling and cross-selling opportunities consumers are most likely to purchase
• Identifying the right combinations of products, services, and promotions to attract target consumers

Of course, in addition to forecasting opportunities, predictive analytics is often used in analyzing risk. Whether a consumer is likely to default on a payment plan, for example, is one of many ways predictive analytics is used in business to analyze and mitigate risk accompanying high-volume and high-cost consumer relationships. Likewise, predictive analytics is a valuable tool for forecasting substantial market changes. At one time, unexpected shifts in demand could be devastating for businesses financially. But with predictive analytics, companies can stay ahead of the curve and adapt in real-time with products and services that are perfectly in-tune with customer expectations.

Types

Predictive models

Predictive models are models of the relation between the specific performance of a unit in a sample and one or more known attributes or features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance. This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios.

The available sample units with known attributes and known performances is referred to as the “training sample”. The units in other samples, with known attributes but unknown performances, are referred to as “out of [training] sample” units. The out of sample units do not necessarily bear a chronological relation to the training sample units. For example, the training sample may consist of literary attributes of writings by Victorian authors, with known attribution, and the out-of sample unit may be newly found writing with unknown authorship; a predictive model may aid in attributing a work to a known author. Another example is given by analysis of blood splatter in simulated crime scenes in which the out of sample unit is the actual blood splatter pattern from a crime scene. The out of sample unit may be from the same time as the training units, from a previous time, or from a future time.

Descriptive models

Descriptive models quantify relationships in data

in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. Descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Instead, descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions.

Decision models

Decision models describe the relationship between all the elements of a decision—the known data (including results of predictive models), the decision, and the forecast results of the decision—in order to predict the results of decisions involving many variables. These models can be used in optimization, maximizing certain outcomes while minimizing others. Decision models are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.