Electronic customer relationship management (eCRM) key terms are very similar to regular CRM terms.

The principles are the same but the format for storing the CRM data is different. eCRM is a business strategy that aims at identifying customers and their needs and then creating sales and service strategies that are unique to them. As you study eCRM key terms, it helps if you have an idea of what your program is capable of doing. In all likelihood, you should find most of the key terms in this guide used in the implementation of your eCRM program.

Here’s a look at some of the top eCRM terms:

Customer Relationship Analytics (CRA) is the actual process of collecting and analyzing data about customers in order to better understand their relationship to the organization, improve sales and customer service and reduce costs. CRA is a form of online analytical processing (OLAP) and can employ data mining.

Online Analytical Processing (OLAP) is a type of computer processing that allows a user to extract and view data from multiple points of view. A user wanting to analyze CRM data might perform OLAP in order to do a specific, interactive analysis of the data.

Web analytics is the process of analyzing the behavior of website visitors, and is often used as part of CRM analytics. Web analytics is usually used by companies looking to retain or attract more online visitors and/or increase online spending. Web analytics tools can monitor the geographical regions of visitors, track clickthrough and drill-down behavior, monitor purchases and predict the future purchases of customers.

Web mining is the integration of information extracted through traditional data mining techniques with information gathered on the Internet. Web mining is used to analyze patterns using three techniques: content mining (used to examine data collected by search engines), structure mining (used to examine data related to a particular website) and usage mining (used to examine data related to a specific user's browser). In CRM, Web mining is often used to help companies better understand customer behavior and evaluate the successfulness of a marketing campaign or a website.

Clickstream analytics is the process of collecting and analyzing data about which pages website visitors visit, and what order they visit them in. Clickstream analysis is split into two levels: traffic analysis and e-commerce analysis. Traffic analysis collects clickstream data based on the user's experience with the website (how many pages the user sees, how fast they load, etc.). E-commerce analysis uses this clickstream data to determine site effectiveness.

Predictive analytics focuses on predicting future probabilities and trends. The "predictor" is the variable used to predict future behaviors, and is the main element of a predictive analytics tool. In CRM, predictive analytics is often used to predict customer behavior.

Speech analytics mines customer calls for data, providing insight into customer behaviors. Often used in the call center, speech analytics can be used to reduce operating expenses, improve customer experience, increase revenue and reduce customer attrition. Speech analytics data can also be used to improve up-sell and cross-sell opportunities and establish targeted marketing campaigns.

Text analytics obtains and analyzes unstructured, text-based customer data from customer surveys and text-based customer interactions. As Web 2.0 technology and social networking gains in popularity, some experts suggest that text analytics may take on a greater role in helping companies analyze their customer relationships.

Real-time analytics allows users to analyze available data in real time or near real time. Real-time analytics is often used in CRM analytics, letting users track and manage customer behaviors as they occur. Real-time analytics is most often used between call centers and marketing departments.

Customer Lifecycle Management (CLM) is the measurement of multiple customer related metrics, which, when analyzed for a period of time, indicate performance of a business. The overall scope of the CLM implementation process encompasses all domains or departments of an organization, which generally brings all sources of static and dynamic data, marketing processes, and value added services to a unified decision supporting platform through iterative phases of customer acquisition, retention, cross and up-selling and lapsed customer win-back. Some detailed CLM models further breakdown these phases into acquisition, introduction to products, profiling of customers, growth of customer base, cultivation of loyalty among customers, and termination of customer relationship.

Mobile CRM (mCRM) is a subset of Electronic CRM is Mobile CRM (mCRM). It is defined as “services that aim at nurturing customer relationships, acquiring or maintaining customers, support marketing, sales or services processes, and use wireless networks as the medium of delivery to the customers. However, since communication is the central aspect of customer relations activities, many opt for the following definition of mCRM: “communication, either one-way or interactive, which is related to sales, marketing and customer service activities conducted through the mobile medium for the purpose of building and maintaining customer relationships between a company and its customer(s).

eCRM allows customers to access company services from more and more places, since the Internet access points are increasing by the day. mCRM however, takes this one step further and allows customers or managers to access the systems for instance from a mobile phone or PDA with internet access, resulting in high flexibility. An example of a company that implemented mCRM is Finnair, who made it possible for their customers to check in for their flights by SMS. Since mCRM is not able to provide a complete range of customer relationship activities it should be integrated in the complete CRM system.