Predictive Analytics Glossary

Predictive Analytics

ABC… glossary

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Analytical CRM (aCRM)

supports decision-making processes that improve customer

interactions or increase the value of customer interactions;

aCRM aims at storing, analyzing and applying the

knowledge about customers and about ways to approach

them effectively.

PREDICTIVE ANALYTICS GLOSSARY


Big Data

both an often abused buzzword and a real trend in today’s world, reflecting the growing amount of data being captured, processed, aggregated, stored and analyzed every day. Wikipedia describes Big Data as „collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools (…)”.

Business Intelligence

the applications, infrastructure, tools or processes for analyzing data and presenting information to help company executives, managers and others make more informed business decisions.



Churn Analysis (Attrition Analysis) profiles the customers who are likely to stop using the company’s services or products and identifies those whose churn is likely to bring the biggest loss. Results of churn analysis are used to prepare new offers for valuable customers under the risk of defection.

Credit Scoring assessing the creditworthiness of the entity (usually a person or company), used by banks (lenders) to determine if a person will repay his/her debts.

Cross / Up selling a marketing notion of selling complementary (cross-selling) or additional (up-selling) products to specific customers considering their characteristics and past behavior.

Customer Segmentation & Profiling grouping of customers with similar profiles and behavior based on the available customer data, describing and comparing such groups.



Data Mart

part of data stored in an organization that is focused on a single subject or department, such as Sales, Finance, or Marketing.

Data Warehouse

central repository of data that is collected and/or stored by an enterprise’s various business systems.

Data Quality

the processes and techniques involved in ensuring the reliability and application efficiency of data. Data is of high quality if it reliably reflects underlying processes and fits the intended uses in operations, decision making and planning.



ETL (Extract-Transform-Load)

a process in data warehousing responsible for pulling data

out of one source, transforming them so that they meet the

needs of the processes that will be using them on next

stages, and placing them into target database.



Fraud Detection

identifying characteristics of suspicious fraudulent transfers,

orders and other illegal activities against an organization or

company and designing relevant triggers in IT systems that

raise warnings whenever such transactions are attempted or

made.



Hadoop

another hot topic in Big Data nowadays; Apache Hadoop is

an open-source software framework for distributed storage

and processing of very large data sets on computer clusters

built from commodity (already available) hardware. It

enables massive data storage and faster processing.



IoT (Internet of Things)

the notion of wide-ranging network of electronic devices of

various kinds (personal, household-level, industrial) and

purposes (healthcare, leisure, media consumption, shopping,

manufacturing, environment control etc.) exchanging data

over Internet, coordinating its activities with one another.



LTV (Lifetime Value) of a customer

the anticipated discounted profit that a customer will

generate for a company during his/her lifetime.



Machine Learning

a discipline that studies methods and algorithms of automated

learning from data through which computer systems can adjust their

operations according to feedback they receive. A term strongly

related to artificial intelligence, data mining, statistical methods.

Market Basket Analysis

identifying combinations of products or services that frequently co-

occur in transactions, for example products that are often purchased

together. Results of such analysis are used to recommend additional

purchases, inform decisions on placing products in relation to one

another etc.



No SQL

database type, that is „not structured” as traditional

relational SQL databases. NoSQL is used for high volume

non-structured data, hence it’s very common in big data

area.

Neural Networks

machine learning algorithm with structure imitating how

brain works. It has a structure of artificial neurons network,

the learning is done by adjusting weights of neuron

connections, the same way as human brain synapses work.



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OLAP (On-Line Analytical Processing)

tools enabling a user to easily make and browse reports

summarizing relevant data, analyzing them from various

perspectives.




the practice of extracting information from existing data sets

in order to determine patterns and predict future outcomes

and trends. Applied to business, predictive models and

analysis are used to analyze current data and historical facts

in order to better understand customers, products and

partners and to identify potential risks and opportunities for

a company.



Real Time Decisioning (RTD)

helps companies make the best sales/marketing decisions in

real time (near to zero latency). For example RTD systems

(scoring systems) can score and rate customers at the very

moment of their interaction with the company using various

business rules or models.



Social Network Analysis (SNA)

the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships.

Survival Analysis

estimates the time a customer will subscribe to a service or the probability of customer’s defection in subsequent periods of time. This information allows the company to determine the predicted period of retaining the customer and introduce an appropriate loyalty policy.



Text Mining

the analysis of data contained in natural language

text. It works by computing statistics for words and

phrases in source data, thus expressing text structure

in numerical terms, and then analyzing it with

traditional data mining techniques.



Unstructured Data

information that either does not have a pre-defined

data model and/or is not organized in a predefined

manner. It usually refers to the information that

doesn’t reside in a traditional row-column database,

for example e-mail messages or comments.



Web Mining (Web Data Mining)

the use of data mining techniques to automatically

discover and extract information from Web sites,

documents and services.


Incomplete, still looking for some letters ;-)

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Predictive Analytics Glossary

Data & Analytics

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