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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glossary

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analytical tips and tutorials
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Predictive Analytics Glossary

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