Uplift modelling Uplift modelling is a technique for modelling the
change in probability caused by an action. Typically this is a marketing action such as an offer to buy a product, to use a product more or to re-sign a contract. For example, in a retention campaign you wish to predict the change in probability that a customer will remain a customer if they are contacted. A model of the change in probability allows the retention campaign to be targeted at those customers on whom the change in probability will be beneficial. This allows the retention programme to avoid triggering unnecessary
churn or
customer attrition without wasting money contacting people who would act anyway.
Archaeology Predictive modelling in
archaeology gets its foundations from
Gordon Willey's mid-fifties work in the
Virú Valley of Peru. Complete, intensive surveys were performed then
covariability between cultural remains and natural features such as slope and vegetation were determined. Development of quantitative methods and a greater availability of applicable data led to growth of the discipline in the 1960s and by the late 1980s, substantial progress had been made by major land managers worldwide. Generally, predictive modelling in archaeology is establishing statistically valid causal or covariable relationships between natural proxies such as soil types, elevation, slope, vegetation, proximity to water, geology, geomorphology, etc., and the presence of archaeological features. Through analysis of these quantifiable attributes from land that has undergone archaeological survey, sometimes the "archaeological sensitivity" of unsurveyed areas can be anticipated based on the natural proxies in those areas. Large land managers in the United States, such as the
Bureau of Land Management (BLM), the
Department of Defense (DOD), and numerous highway and parks agencies, have successfully employed this strategy. By using predictive modelling in their cultural resource management plans, they are capable of making more informed decisions when planning for activities that have the potential to require ground
disturbance and subsequently affect archaeological sites.
Customer relationship management Predictive modelling is used extensively in analytical
customer relationship management and
data mining to produce customer-level models that describe the likelihood that a customer will take a particular action. The actions are usually sales, marketing and
customer retention related. For example, a large
consumer organization such as a mobile telecommunications operator will have a set of predictive models for product
cross-sell, product deep-sell (or
upselling) and
churn. It is also now more common for such an organization to have a model of savability using an
uplift model. This predicts the likelihood that a customer can be saved at the end of a contract period (the change in churn probability) as opposed to the standard churn prediction model.
Auto insurance Predictive modelling is utilised in
vehicle insurance to assign
risk of incidents to policy holders from information obtained from policy holders. This is extensively employed in
usage-based insurance solutions where predictive models utilise telemetry-based data to build a model of predictive risk for claim likelihood. Black-box auto insurance predictive models utilise
GPS or
accelerometer sensor input only. Some models include a wide range of predictive input beyond basic telemetry including advanced driving behaviour, independent crash records, road history, and user profiles to provide improved risk models.
Health care In 2009
Parkland Health & Hospital System began analyzing
electronic medical records in order to use predictive modeling to help identify patients at high risk of readmission. Initially, the hospital focused on patients with congestive heart failure, but the program has expanded to include patients with diabetes, acute myocardial infarction, and pneumonia. In 2018, Banerjee et al. proposed a
deep learning model for estimating short-term
life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). It achieved an area under the ROC (
Receiver Operating Characteristic) curve of 0.89. To provide explain-ability, they developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable the model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to physicians. The first clinical prediction model reporting guidelines were published in 2015 (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)), and have since been updated. Predictive modelling has been
used to estimate surgery duration.
Algorithmic trading Predictive modeling in trading is a modeling process wherein the probability of an outcome is predicted using a set of
predictor variables. Predictive models can be built for different assets like stocks, futures, currencies, commodities etc. Predictive modeling is still extensively used by trading firms to devise strategies and trade. It utilizes mathematically advanced software to evaluate indicators on price, volume, open interest and other historical data, to discover repeatable patterns.
Lead tracking systems Predictive modelling gives
lead generators a head start by forecasting data-driven outcomes for each potential campaign. This method saves time and exposes potential blind spots to help client make smarter decisions.
Notable failures of predictive modeling Although not widely discussed by the mainstream predictive modeling community, predictive modeling is a methodology that has been widely used in the financial industry in the past and some of the major failures contributed to the
2008 financial crisis. These failures exemplify the danger of relying exclusively on models that are essentially backward looking in nature. The following examples are by no mean a complete list: • Bond rating.
S&P,
Moody's and
Fitch quantify the
probability of default of bonds with discrete variables called rating. The rating can take on discrete values from AAA down to D. The rating is a predictor of the risk of default based on a variety of variables associated with the borrower and historical
macroeconomic data. The rating agencies failed with their ratings on the US$600 billion mortgage backed Collateralized Debt Obligation (
CDO) market. Almost the entire AAA sector (and the super-AAA sector, a new rating the rating agencies provided to represent super safe investment) of the CDO market defaulted or severely downgraded during 2008, many of which obtained their ratings less than just a year previously. • So far, no statistical models that attempt to predict equity market prices based on historical data are considered to consistently make correct predictions over the long term. One particularly memorable failure is that of
Long Term Capital Management, a fund that hired highly qualified analysts, including a
Nobel Memorial Prize in Economic Sciences winner, to develop a sophisticated statistical model that predicted the
price spreads between different securities. The models produced impressive profits until a major debacle that caused the then
Federal Reserve chairman
Alan Greenspan to step in to broker a rescue plan by the
Wall Street broker dealers in order to prevent a meltdown of the bond market. ==Possible fundamental limitations of predictive models based on data fitting==