1. What is the average salary of a Digital Merchandiser -Intermediate?
The average annual salary of Digital Merchandiser -Intermediate is $73,200.
In case you are finding an easy salary calculator,
the average hourly pay of Digital Merchandiser -Intermediate is $35;
the average weekly pay of Digital Merchandiser -Intermediate is $1,408;
the average monthly pay of Digital Merchandiser -Intermediate is $6,100.
2. Where can a Digital Merchandiser -Intermediate earn the most?
A Digital Merchandiser -Intermediate's earning potential can vary widely depending on several factors, including location, industry, experience, education, and the specific employer.
According to the latest salary data by Salary.com, a Digital Merchandiser -Intermediate earns the most in San Jose, CA, where the annual salary of a Digital Merchandiser -Intermediate is $91,800.
3. What is the highest pay for Digital Merchandiser -Intermediate?
The highest pay for Digital Merchandiser -Intermediate is $87,719.
4. What is the lowest pay for Digital Merchandiser -Intermediate?
The lowest pay for Digital Merchandiser -Intermediate is $51,230.
5. What are the responsibilities of Digital Merchandiser -Intermediate?
The Digital Merchandiser -Intermediate ensures that online product catalogs are designed and maintained accurately on site. Supports effective digital merchandising programs and strategies by selecting and sourcing products focused on achieving marketing and sales goals. Being a Digital Merchandiser -Intermediate performs daily analysis of website performance, including product and category performance and visitor data. Maintains product descriptions, specifications, digital assets, and categorizations. In addition, Digital Merchandiser -Intermediate coordinates with marketing to execute campaigns and seasonal merchandising with an appropriate mix of products. May participate in site testing and updates. Requires a bachelor's degree in marketing, merchandising, business or other related field. Typically reports to a supervisor or manager. Being a Digital Merchandiser -Intermediate occasionally directed in several aspects of the work. Gaining exposure to some of the complex tasks within the job function. Working as a Digital Merchandiser -Intermediate typically requires 2 -4 years of related experience.
6. What are the skills of Digital Merchandiser -Intermediate
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
1.)
Insight: Insight is the understanding cause and effect based on the identification of relationships and behaviors within a model, context, or scenario.
2.)
Pricing: Pricing is a process of fixing the value that a manufacturer will receive in the exchange of services and goods.
3.)
Data Analysis: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
