Time Series

Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and recently wavelet analysis; the latter include auto-correlation and cross-correlation analysis.In time domain, correlation analyses can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in frequency domain. Additionally, time series analysis techniques may be divided into parametric and non-parametric methods.

The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process.

Market Segmentation

Market segmentation, also called customer profiling, is a marketing strategy which involves dividing a broad target market into subsets of consumers, businesses, or countries that have, or are perceived to have, common needs, interests, and priorities, and then designing and implementing strategies to target them.

Market segmentation strategies are generally used to identify and further define the target customers, and provide supporting data for marketing plan elements such as positioning to achieve certain marketing plan objectives.

Businesses may develop product differentiation strategies, or an undifferentiated approach, involving specific products or product lines depending on the specific demand and attributes of the target segment.

Clustering

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.

Unlike supervised classification, clustering does not use training sets. Though there are some hybrid implementations, called semi-supervised learning.

Clinical trials

Clinical trials are experiments done in clinical research, usually involving small data. Such prospective biomedical or behavioral research studies on human participants are designed to answer specific questions about biomedical or behavioral interventions, including new treatments and known interventions that warrant further study and comparison.

Clinical trials generate data on safety and efficacy. Major concerns include how test patients are sampled (especially if they are compensated), conflict of interests in these studies, and the lack of reproducibility.

Attribution Modeling

An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Google Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions.

Macro-economic models use long-term, aggregated historical data to assign, for each sale or conversion, an attribution weight to a number of channels. These models are also used for advertising mix optimization.

Scoring

Scoring model is a special kind of predictive models. Predictive models can predict defaulting on loan payments, risk of accident, client churn or attrition, or chance of buying a good. Scoring models typically use a logarithmic scale (each additional 50 points in your score reducing the risk of defaulting by 50%), and are based on logistic regression and decision trees, or a combination of multiple algorithms.

Scoring technology is typically applied to transactional data, sometimes in real time (credit card fraud detection,  other fraud).