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Predictive Analytics Predictive analytics Predictive analytics encompasses a variety of statisti- 2 Types cal techniques from modeling, machine learning, and data mining that analyze current and historical facts to Generally, the term predictive analytics is used to mean make predictions about future, or otherwise unknown, predictive modeling, “scoring” data with predictive mod- [1][2] events. els, and forecasting. However, people are increasingly In business, predictive models exploit patterns found in using the term to refer to related analytical disciplines, historical and transactional data to identify risks and op- such as descriptive modeling and decision modeling or portunities. Models capture relationships among many optimization. These disciplines also involve rigorous data factors to allow assessment of risk or potential associated analysis, and are widely used in business for segmentation with a particular set of conditions, guiding decision mak- and decision making, but have different purposes and the ing for candidate transactions.[3] statistical techniques underlying them vary. Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] 2.1 Predictive models pharmaceuticals[11] and other fields. Predictive models are models of the relation between the One of the most well known applications is credit scor- specific performance of a unit in a sample and one or [1] ing, which is used throughout financial services. Scor- more known attributes or features of the unit. The objec- ing models process a customer’s credit history, loan appli- tive of the model is to assess the likelihood that a similar cation, customer data, etc., in order to rank-order individ- unit in a different sample will exhibit the specific per- uals by their likelihood of making future credit payments formance. This category encompasses models that are on time. in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predic- tive models often perform calculations during live trans- 1 Definition actions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a Predictive analytics is an area of data mining that deals decision. With advancements in computing speed, indi- with extracting information from data and using it to pre- vidual agent modeling systems have become capable of dict trends and behavior patterns. Often the unknown simulating human behaviour or reactions to given stimuli event of interest is in the future, but predictive analytics or scenarios. can be applied to any type of unknown whether it be in The available sample units with known attributes and the past, present or future. For example, identifying sus- known performances is referred to as the “training sam- pects after a crime has been committed, or credit card ple.” The units in other sample, with known attributes but fraud as it occurs.[12] The core of predictive analytics re- un-known performances, are referred to as “out of [train- lies on capturing relationships between explanatory vari- ing] sample” units. The out of sample bare no chrono- ables and the predicted variables from past occurrences, logical relation to the training sample units. For exam- and exploiting them to predict the unknown outcome. It ple, the training sample may consists of literary attributes is important to note, however, that the accuracy and us- of writings by Victorian authors, with known attribution, ability of results will depend greatly on the level of data and the out-of sample unit may be newly found writing analysis and the quality of assumptions. with unknown authorship; a predictive model may aid 1 2 3 APPLICATIONS the attribution of the unknown author. Another example 3.1 Analytical customer relationship man- is given by analysis of blood splatter in simulated crime agement (CRM) scenes in which the out-of sample unit is the actual blood splatter pattern from a crime scene. The out of sample Analytical Customer Relationship Management is a fre- unit may be from the same time as the training units, from quent commercial application of Predictive Analysis. a previous time, or from a future time. Methods of predictive analysis are applied to customer data to pursue CRM objectives, which involve construct- ing a holistic view of the customer no matter where their information resides in the company or the department 2.2 Descriptive models involved. CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a com- Descriptive models quantify relationships in data in a way pany to posture and focus their efforts effectively across that is often used to classify customers or prospects into the breadth of their customer base. They must analyze groups. Unlike predictive models that focus on predicting and understand the products in demand or have the po- a single customer behavior (such as credit risk), descrip- tential for high demand, predict customers’ buying habits tive models identify many different relationships between in order to promote relevant products at multiple touch customers or products. Descriptive models do not rank- points, and proactively identify and mitigate issues that order customers by their likelihood of taking a particular have the potential to lose customers or reduce their abil- action the way predictive models do. Instead, descriptive ity to gain new ones. Analytical Customer Relationship models can be used, for example, to categorize customers Management can be applied throughout the customers by their product preferences and life stage. Descriptive lifecycle (acquisition, relationship growth, retention, and modeling tools can be utilized to develop further models win-back). Several of the application areas described be- that can simulate large number of individualized agents low (direct marketing, cross-sell, customer retention) are and make predictions. part of Customer Relationship Managements. 3.2 Clinical decision support systems 2.3 Decision models Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain Decision models describe the relationship between all the conditions, like diabetes, asthma, heart disease, and other elements of a decision — the known data (including re- lifetime illnesses. Additionally, sophisticated clinical de- sults of predictive models), the decision, and the forecast cision support systems incorporate predictive analytics to results of the decision — in order to predict the results of support medical decision making at the point of care. A decisions involving many variables. These models can be working definition has been proposed by Robert Hay- used in optimization, maximizing certain outcomes while ward of the Centre for Health Evidence: “Clinical Deci- minimizing others. Decision models are generally used to sion Support Systems link health observations with health develop decision logic or a set of business rules that will knowledge to influence health choices by clinicians for produce the desired action for every customer or circum- improved health care.” stance. 3.3 Collection analytics 3 Applications Many portfolios have a set of delinquent customers who do not make their payments on time. The financial insti- tution has to undertake collection activities on these cus- Although predictive analytics can be put to use in many tomers to recover the amounts due. A lot of collection applications, we outline a few examples where predictive resources are wasted on customers who are difficult or analytics has shown positive impact in recent years. impossible to recover. Predictive analytics can help opti- 3.7 Fraud detection 3 mize the allocation of collection resources by identifying fective combination of product versions, marketing ma- the most effective collection agencies, contact strategies, terial, communication channels and timing that should be legal actions and other strategies to each customer, thus used to target a given consumer. The goal of predictive significantly increasing recovery at the same time reduc- analytics is typically to lower the cost per order or cost ing collection costs. per action. 3.4 Cross-sell 3.7 Fraud detection Often corporate organizations collect and maintain abun- Fraud is a big problem for many businesses and can be of dant data (e.g. customer records, sale transactions) as various types: inaccurate credit applications, fraudulent exploiting hidden relationships in the data can provide a transactions (both offline and online), identity thefts and competitive advantage. For an organization that offers false insurance claims. These problems plague firms of multiple products, predictive analytics can help analyze all sizes in many industries. Some examples of likely vic- [13] customers’ spending, usage and other behavior, leading to tims are credit card issuers, insurance companies, re- efficient cross sales, or selling additional products to cur- tail merchants, manufacturers, business-to-business sup- rent customers.[2] This directly leads to higher profitabil- pliers and even services providers. A predictive model ity per customer and stronger customer relationships. can help weed out the “bads” and reduce a business’s ex- posure to fraud. Predictive modeling can also be used to identify high-risk 3.5 Customer retention fraud candidates
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