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The purpose of the sales force compensation metric is to determine the mix of salary, bonus, and commission that will maximize sales generated by the sales force. When designing a compensation plan for a sales force, managers face four key considerations: level of pay, mix between salary and incentive, measures of performance, and performance-payout relationships.
Two of Salesforce's earliest investors were Larry Ellison, the co-founder and first CEO of Oracle, and Halsey Minor, the founder of CNET. [7] Salesforce was severely affected by the dot-com bubble bursting at the beginning of the new millennium, resulting in the company laying off 20% of its workforce. Despite its losses, Salesforce continued ...
"A/B testing" is a shorthand for a simple randomized controlled experiment, in which a number of samples (e.g. A and B) of a single vector-variable are compared. [1] A/B tests are widely considered the simplest form of controlled experiment, especially when they only involve two variants.
System for Cross-domain Identity Management (SCIM) is a standard for automating the exchange of user identity information between identity domains, or IT systems. One example might be that as a company onboards new employees and separates from existing employees, they are added and removed from the company's electronic employee directory .
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China, which has striven toward self-sufficiency in IT, saw little impact to key services such as airlines and banks, although foreign businesses and luxury hotels in the country were affected. [45] Russia and Iran—both restricted by international sanctions from using the services of American high-tech companies—reported no disruptions.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.