According to the McKinsey Global Institute, Big Data has the potential to increase net retailer margins by 60 percent, and is expected to drive up 1 percent of annual productivity growth in US retail alone.
Today's customers are constantly creating data in multiple platforms. This data contains valuable insights about their preferences and tastes. Coupled with transactional data and data from various touchpoints in a retailer, this repository can be an invaluable source of information. But there are a couple of challenges in making this dream a reality. In today's multi-channel, high-touch, always on hypercompetitive consumer markets, data from multiple sources need to be collected and analyzed, and it needs to be done fast. This is only possible if the team has the right skill set to both aggregate and analyze the data.
Understanding the customer is vital for a data-driven retailer, but it's not the only problem data can solve. Retailers can optimize their stock levels, decrease transportation costs, and analyze potential store locations using various types of predictive analytics methods.
Some of the most common analytical methods used in the retail industry include:
Experfy's courses on Retail Analytics are designed to help retail industry professionals and managers understand and implement various data-driven solutions geared towards reducing costs and increasing revenue. The courses are designed to address industry-specific problems, and they include a lot of demos and case studies to make sure that the students not only understand the solutions presented, but also gain the skills to apply them in real life.
According to Glassdoor, the average salary for a Retail Solutions Data Scientist is $113,436.
Using Non-Linear Models to Understand Data
Text classification: sentiment analysis and dialog act classification
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